Practice Management

From Banner Health Corporation, Phoenix, AZ.

Background: Health care providers are routinely incentivized with pay-for-performance (P4P) metrics to increase the quality of care. In an inpatient setting, P4P models typically measure quality by attributing each patient’s outcome to a single provider even though many providers routinely care for the patient. This study investigates a new attribution approach aiming to distribute each outcome across all providers who provided care.

Methods: The methodology relies on a multi-membership model and is demonstrated in the Banner Health system using 3 clinical outcome measures (length of stay, 30-day readmissions, and mortality) and responses to 3 survey questions that measure a patient’s perception of their care. The new approach is compared to the “standard” method, which attributes each patient to only 1 provider.

Results: When ranking by clinical outcomes, both methods were concordant 72.1% to 82.1% of the time for top-half/bottom-half rankings, with a median percentile difference between 7 and 15. When ranking by survey scores, there was more agreement, with concordance between 84.1% and 86.6% and a median percentile difference between 11 and 13. Last, Pearson correlation coefficients of the paired percentiles ranged from 0.56 to 0.78.

Conclusion: The new approach provides a fairer solution when measuring provider performance.

Keywords: patient attribution; PAMM; PAPR; random effect model; pay for performance.

Providers practicing in hospitals are routinely evaluated based on their performance and, in many cases, are financially incentivized for a better-than-average performance within a pay-for-performance (P4P) model. The use of P4P models is based on the belief that they will “improve, motivate, and enhance providers to pursue aggressively and ultimately achieve the quality performance targets thus decreasing the number of medical errors with less malpractice events.”¹ Although P4P models continue to be a movement in health care, they have been challenging to implement.

One concern involves the general quality of implementation, such as defining metrics and targets, setting payout amounts, managing technology and market conditions, and gauging the level of transparency to the provider.² Another challenge, and the focus of this project, are concerns around measuring performance to avoid perceptions of unfairness. This concern can be minimized if the attribution is handled in a fairer way, by spreading it across all providers who affected the outcome, both in a positive or negative direction.³

To implement these models, the performance of providers needs to be measured and tracked periodically. This requires linking, or attributing, a patient’s outcome to a provider, which is almost always the attending or discharging provider (ie, a single provider).³ In this single-provider attribution approach, one provider will receive all the credit (good or bad) for their respective patients’ outcomes, even though the provider may have seen the patient only a fraction of the time during the hospitalization. Attributing outcomes—for example, length of stay (LOS), readmission rate, mortality rate, net promoter score (NPS)—using this approach reduces the validity of metrics designed to measure provider performance, especially in a rotating provider environment where many providers interact with and care for a patient. For example, the quality of providers’ interpersonal skills and competence were among the strongest determinants of patient satisfaction,⁴ but it is not credible that this is solely based on the last provider during a hospitalization.

Proportionally distributing the attribution of an outcome has been used successfully in other contexts. Typically, a statistical modeling approach using a multi-membership framework is used because it can handle the sometimes-complicated relationships within the hierarchy. It also allows for auxiliary variables to be introduced, which can help explain and control for exogenous effects.^5-7 For example, in the education setting, standardized testing is administered to students at defined years of schooling: at grades 4, 8, and 10, for instance. The progress of students, measured as the academic gains between test years, are proportionally attributed to all the teachers who the student has had between the test years. These partial attributions are combined to evaluate an overall teacher performance.^8,9

Although the multi-membership framework has been used in other industries, it has yet to be applied in measuring provider performance. The purpose of this project is to investigate the impact of using a multi-provider approach compared to the standard single-provider approach. The findings may lead to modifications in the way a provider’s performance is measured and, thus, how providers are compensated. A similar study investigated the impact of proportionally distributing patients’ outcomes across all rotating providers using a weighting method based on billing practices to measure the partial impact of each provider.³

This study is different in 2 fundamental ways. First, attribution is weighted based on the number of clinically documented interactions (via clinical notes) between a patient and all rotating providers during the hospitalization. Second, performance is measured via multi-membership models, which can estimate the effect (both positive and negative) that a provider has on an outcome, even when caring for a patient a fraction of the time during the hospitalization.

Methods

Setting

Banner Health is a non-profit, multi-hospital health care system across 6 states in the western United States that is uniquely positioned to study provider quality attribution models. It not only has a large number of providers and serves a broad patient population, but Banner Health also uses an instance of Cerner (Kansas City, MO), an enterprise-level electronic health record (EHR) system that connects all its facilities and allows for advanced analytics across its system.

For this study, we included only general medicine and surgery patients admitted and discharged from the inpatient setting between January 1, 2018, and December 31, 2018, who were between 18 and 89 years old at admission, and who had a LOS between 1 and 14 days. Visit- and patient-level data were collected from Cerner, while outcome data, and corresponding expected outcome data, were obtained from Premier, Inc. (Charlotte, NC) using their CareScience methodologies.¹⁰ To measure patient experience, response data were extracted from post-discharge surveys administered by InMoment (Salt Lake City, UT).

Provider Attribution Models

Provider Attribution by Physician of Record (PAPR). In the standard approach, denoted here as the PAPR model, 1 provider—typically the attending or discharging provider, which may be the same person—is attributed to the entire hospitalization. This provider is responsible for the patient’s care, and all patient outcomes are aggregated and attributed to the provider to gauge his or her performance. The PAPR model is the most popular form of attribution across many health care systems and is routinely used for P4P incentives.

In this study, the discharging provider was used when attributing hospitalizations using the PAPR model. Providers responsible for fewer than 12 discharges in the calendar year were excluded. Because of the directness of this type of attribution, the performance of 1 provider does not account for the performance of the other rotating providers during hospitalizations.

Provider Attribution by Multiple Membership (PAMM). In contrast, we introduce another attribution approach here that is designed to assign partial attribution to each provider who cares for the patient during the hospitalization. To aggregate the partial attributions, and possibly control for any exogenous or risk-based factors, a multiple-membership, or multi-member (MM), model is used. The MM model can measure the effect of a provider on an outcome even when the patient-to-provider relationship is complex, such as in a rotating provider environment.⁸

The purpose of this study is to compare attribution models and to determine whether there are meaningful differences between them. Therefore, for comparison purposes, the same discharging providers using the PAPR approach are eligible for the PAMM approach, so that both attribution models are using the same set of providers. All other providers are excluded because their performance would not be comparable to the PAPR approach.

While there are many ways to document provider-to-patient interactions, 2 methods are available in almost all health care systems. The first method is to link a provider’s billing charges to each patient-day combination. This approach limits the attribution to 1 provider per patient per day because multiple rotating providers cannot charge for the same patient-day combination.³ However, many providers interact with a patient on the same day, so using this approach excludes non-billed provider-to-patient interactions.

The second method, which was used in this study, relies on documented clinical notes within the EHR to determine how attribution is shared. In this approach, attribution is weighted based on the authorship of 3 types of eligible clinical notes: admitting history/physical notes (during admission), progress notes (during subsequent days), and discharge summary notes (during final discharge). This will (likely) result in many providers being linked to a patient on each day, which better reflects the clinical setting (Figure). Recently, clinical notes were used to attribute care of patients in an inpatient setting, and it was found that this approach provides a reliable way of tracking interactions and assigning ownership.¹¹

The provider-level attribution weights are based on the share of authorships of eligible note types. Specifically, for each provider j, let a_ij be the total count of eligible note types for hospitalization i authored by provider j, and let a_i be the overall total count of eligible note types for hospitalization i. Then the attribution weight is

(Eq. 1)

for hospitalization i and provider j. Note that ∑_jw_ij = 1: in other words, the total attribution, summed across all providers, is constrained to be 1 for each hospitalization.

Patient Outcomes

Outcomes were chosen based on their routine use in health care systems as standards when evaluating provider performance. This study included 6 outcomes: inpatient LOS, inpatient mortality, 30-day inpatient readmission, and patient responses from 3 survey questions. These outcomes can be collected without any manual chart reviews, and therefore are viewed as objective outcomes of provider performance.

Each outcome was aggregated for each provider using both attribution methods independently. For the PAPR method, observed-to-expected (OE) indices for LOS, mortality, and readmissions were calculated along with average patient survey scores. For the PAMM method, provider-level random effects from the fitted models were used. In both cases, the calculated measures were used for ranking purposes when determining top (or bottom) providers for each outcome.

Individual Provider Metrics for the PAPR Method

Inpatient LOS Index. Hospital inpatient LOS was measured as the number of days between admission date and discharge date. For each hospital visit, an expected LOS was determined using Premier’s CareScience Analytics (CSA) risk-adjustment methodology.¹⁰ The CSA methodology for LOS incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let o_ij and e_ij be the observed and expected LOS, respectively, for hospitalization i = 1,…,n_j attributed to provider j. Then the inpatient LOS index for provider j is L_j = ∑_io_ij⁄∑_ie_ij.

Inpatient Mortality Index. Inpatient mortality was defined as the death of the patient during hospitalization. For each hospitalization, an expected mortality probability was determined using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for mortality incorporates a patient’s demographics and comorbidities.

Just as before, let n_j be the number of hospitalizations attributed to provider j. Let m_ij = 1 if the patient died during hospitalization i = 1, … , n_j attributed to provider j; m_ij = 0 otherwise. Let p_ij^(m) be the corresponding expected mortality probability. Then the inpatient mortality index for provider j is M_j = ∑_im_ij⁄∑_ip_ij^(m).

30-Day Inpatient Readmission Index. A 30-day inpatient readmission was defined as the event when a patient is discharged and readmits back into the inpatient setting within 30 days. The inclusion criteria defined by the Centers for Medicare and Medicaid Services (CMS) all-cause hospital-wide readmission measure was used and, consequently, planned readmissions were excluded.¹² Readmissions could occur at any Banner hospital, including the same hospital. For each hospital visit, an expected readmission probability was derived using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for readmissions incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let r_ij = 1 if the patient had a readmission following hospitalization i = 1, … , n_j attributed to provider j; r_ij = 0 otherwise. Let p_ij^(r) be the corresponding expected readmission probability. Then the 30-day inpatient readmission index for provider j is R_j = ∑_ir_ij ⁄∑_ip_ij^(r).

Patient Survey Scores. The satisfaction of the patient’s experience during hospitalization was measured via post-discharge surveys administered by InMoment. Two survey questions were selected because they related directly to a provider’s interaction with the patient: “My interactions with doctors were excellent” (Doctor) and “I received the best possible care” (Care). A third question, “I would recommend this hospital to my family and friends,” was selected as a proxy measure of the overall experience and, in the aggregate, is referred to as the net promoter score (NPS).^13,14 The responses were measured on an 11-point Likert scale, ranging from “Strongly Disagree” (0) to “Strongly Agree” (10); “N/A” or missing responses were excluded.

The Likert responses were coded to 3 discrete values as follows: if the value was between 0 and 6, then -1 (ie, detractor); between 7 and 8 (ie, neutral), then 0; otherwise 1 (ie, promoter). Averaging these coded responses results in a patient survey score for each question. Specifically, let n_j be the number of hospitalizations attributed to provider j in which the patient responded to the survey question. Let s_ij ∈{−1, 0, 1} be the coded response linked to hospitalization i = 1, … , n_j attributed to provider j. Then the patient experience score for provider j is S_j = ∑_is_ij⁄n_j.

Handling Ties in Provider Performance Measures. Because ties can occur in the PAPR approach for all measures, a tie-breaking strategy is needed. For LOS indices, ties are less likely because their numerator is strictly greater than 0, and expected LOS values are typically distinct enough. Indeed, no ties were found in this study for LOS indices. However, mortality and readmission indices can routinely result in ties when the best possible index is achieved, such as 0 deaths or readmissions among attributed hospitalizations. To help differentiate between those indices in the PAPR approach, the total estimated risk (denominator) was utilized as a secondary scoring criterion.

Mortality and readmission metrics were addressed by sorting first by the outcome (mortality index), and second by the denominator (total estimated risk). For example, if provider A has the same mortality rate as provider B, then provider A would be ranked higher if the denominator was larger, indicating a higher risk for mortality.

Similarly, it was very common for providers to have the same overall average rating for a survey question. Therefore, the denominator (number of respondents) was used to break ties. However, the denominator sorting was bidirectional. For example, if the tied score was positive (more promoters than detractors) for providers A and B, then provider A would be ranked higher if the denominator was larger. Conversely, if the tied score between providers A and B was neutral or negative (more detractors than promoters), then provider A would be ranked lower if the denominator was larger.

Individual Provider Metrics for the PAMM Method

For the PAMM method, model-based metrics were derived using a MM model.⁸ Specifically, let J be the number of rotating providers in a health care system. Let Y_i be an outcome of interest from hospitalization i, X_1i, …, X_pi be fixed effects or covariates, and ß₁, …, ß_p be the coefficients for the respective covariates. Then the generalized MM statistical model is

(Eq. 2)

where g(μ_i) is a link function between the mean of the outcome, μ_i, and its linear predictor, ß₀, is the marginal intercept, w_ij represents the attribution weight of provider j on hospitalization i (described in Equation 1), and γ_j represents the random effect of provider j on the outcome with γ_j~N(0,σ_γ²).

For the mortality and readmission binary outcomes, logistic regression was performed using a logit link function, with the corresponding expected probability as the only fixed covariate. The expected probabilities were first converted into odds and then log-transformed before entering the model. For LOS, Poisson regression was performed using a log link function with the log-transformed expected LOS as the only fixed covariate. For coded patient experience responses, an ordered logistic regression was performed using a cumulative logit link function (no fixed effects were added).

MM Model-based Metrics. Each fitted MM model produces a predicted random effect for each provider. The provider-specific random effects can be interpreted as the unobserved influence of each provider on the outcome after controlling for any fixed effect included in the model. Therefore, the provider-specific random effects were used to evaluate the relative provider performance, which is analogous to the individual provider-level metrics used in the PAPR method.

Measuring provider performance using a MM model is more flexible and robust to outliers compared to the standard approach using OE indices or simple averages. First, although not investigated here, the effect of patient-, visit-, provider-, and/or temporal-level covariates can be controlled when evaluating provider performance. For example, a patient’s socioeconomic status, a provider’s workload, and seasonal factors can be added to the MM model. These external factors are not accounted for in OE indices.

Another advantage of using predicted random effects is the concept of “shrinkage.” The process of estimating random effects inherently accounts for small sample sizes (when providers do not treat a large enough sample of patients) and/or when there is a large ratio of patient variance to provider variance (for instance, when patient outcome variability is much higher compared to provider performance variability). In both cases, the estimation of the random effect is pulled ever closer to 0, signaling that the provider performance is closer to the population average. See Henderson¹⁵ and Mood¹⁶ for further details.

In contrast, OE indices can result in unreliable estimates when a provider has not cared for many patients. This is especially prevalent when the outcome is binary with a low probability of occurring, such as mortality. Indeed, provider-level mortality OE indices are routinely 0 when the patient counts are low, which skews performance rankings unfairly. Finally, OE indices also ignore the magnitude of the variance of an outcome between providers and patients, which can be large.

Comparison Methodology

In this study, we seek to compare the 2 methods of attribution, PAPR and PAMM, to determine whether there are meaningful differences between them when measuring provider performance. Using retrospective data described in the next section, each attribution method was used independently to derive provider-level metrics. To assess relative performance, percentiles were assigned to each provider based on their metric values so that, in the end, there were 2 percentile ranks for each provider for each metric.

Using these paired percentiles, we derived the following measures of concordance, similar to Herzke, Michtalik³: (1) the percent concordance measure—defined as the number of providers who landed in the top half (greater than the median) or bottom half under both attribution models—divided by the total number of providers; (2) the median of the absolute difference in percentiles under both attribution models; and (3) the Pearson correlation coefficient of the paired provider ranks. The first measure is a global measure of concordance between the 2 approaches and would be expected to be 50% by chance. The second measure gauges how an individual provider’s rank is affected by the change in attribution methodologies. The third measure is a statistical measure of linear correlation of the paired percentiles and was not included in the Herzke, Michtalik³ study.

All statistical analyses were performed on SAS (version 9.4; Cary, NC) and the MM models were fitted using PROC GLIMMIX with the EFFECT statement. The Banner Health Institutional Review Board approved this study.

Results

Descriptive Statistics

A total of 58,730 hospitalizations were included, of which care was provided by 963 unique providers across 25 acute care and critical access hospitals. Table 1 contains patient characteristics, and Table 2 depicts overall unadjusted outcomes. Providers responsible for less than 12 discharges in the calendar year were excluded from both approaches. Also, some hospitalizations were excluded when expected values were not available.

Multi-Membership Model Results

Table 3 displays the results after independently fitting MM models to each of the 3 clinical outcomes. Along with a marginal intercept, the only covariate in each model was the corresponding expected value after a transformation. This was added to use the same information that is typically used in OE indices, therefore allowing for a proper comparison between the 2 attribution methods. The provider-level variance represents the between-provider variation and measures the amount of influence providers have on the corresponding outcome after controlling for any covariates in the model. A provider-level variance of 0 would indicate that providers do not have any influence on the outcome. While the mortality and readmission model results can be compared to each other, the LOS model cannot given its different scale and transformation altogether.

The results in Table 3 suggest that each expected value covariate is highly correlated with its corresponding outcome, which is the anticipated conclusion given that they are constructed in this fashion. The estimated provider-level variances indicate that, after including an expected value in the model, providers have less of an influence on a patient’s LOS and likelihood of being readmitted. On the other hand, the results suggest that providers have much more influence on the likelihood of a patient dying in the hospital, even after controlling for an expected mortality covariate.

Table 4 shows the results after independently fitting MM-ordered logistic models to each of the 3 survey questions. The similar provider-level variances suggest that providers have the same influence on the patient’s perception of the quality of their interactions with the doctor (Doctor), the quality of the care they received (Care), and their likelihood to recommend a friend or family member to the hospital (NPS).

Comparison Results Between Both Attribution Methods

Table 5 compares the 2 attribution methods when ranking providers based on their performance on each outcome measure. The comparison metrics gauge how well the 2 methods agree overall (percent concordance), agree at the provider level (absolute percentile difference and interquartile range [IQR]), and how the paired percentiles linearly correlate to each other (Pearson correlation coefficient).

LOS, by a small margin, had the lowest concordance of clinical outcomes (72.1%), followed by mortality (75.9%) and readmissions (82.1%). Generally, the survey scores had higher percent concordance than the clinical outcome measures, with Doctor at 84.1%, Care at 85.9%, and NPS having the highest percent concordance at 86.6%. Given that by chance the percent concordance is expected to be 50%, there was notable discordance, especially with the clinical outcome measures. Using LOS performance as an example, one attribution methodology would rank a provider in the top half or bottom half, while the other attribution methodology would rank the same provider exactly the opposite way about 28% of the time.

The median absolute percentile difference between the 2 methods was more modest (between 7 and 15). Still, there were some providers whose performance ranking was heavily impacted by the attribution methodology that was used. This was especially true when evaluating performance for certain clinical measures, where the attribution method that was used could change the provider performance percentile by up to 90 levels.

The paired percentiles were positively correlated when ranking performance using any of the 6 measures. This suggests that both methodologies assess performance generally in the same direction, irrespective of the methodology and measure. We did not investigate more complex correlation measures and left this for future research.

It should be noted that ties occurred much more frequently with the PAPR method than when using PAMM and therefore required tie-breaking rules to be designed. Given the nature of OE indices, PAPR methodology is especially sensitive to ties whenever the measure includes counting the number of events (for example, mortality and readmissions) and whenever there are many providers with very few attributed patients. On the other hand, using the PAMM method is much more robust against ties given that the summation of all the weighted attributed outcomes will rarely result in ties, even with a nominal set of providers.

 

 

Discussion

In this study, the PAMM methodology was introduced and was used to assess relative provider performance on 3 clinical outcome measures and 3 patient survey scores. The new approach aims to distribute each outcome among all providers who provided care for a patient in an inpatient setting. Clinical notes were used to account for patient-to-provider interactions, and fitted MM statistical models were used to compute the effects that each provider had on each outcome. The provider effect was introduced as a random effect, and the set of predicted random effects was used to rank the performance of each provider.

The PAMM approach was compared to the more traditional methodology, PAPR, where each patient is attributed to only 1 provider: the discharging physician in this study. Using this approach, OE indices of clinical outcomes and averages of survey scores were used to rank the performance of each provider. This approach resulted in many ties, which were broken based on the number of hospitalizations, although other tie-breaking methods may be used in practice.

Both methodologies showed modest concordance with each other for the clinical outcomes, but higher concordance for the patient survey scores. This was also true when using the Pearson correlation coefficient to assess agreement. The 1 outcome measure that showed the least concordance and least linear correlation between methods was LOS, which would suggest that LOS performance is more sensitive to the attribution methodology that is used. However, it was the least concordant by a small margin.

Furthermore, although the medians of the absolute percentile differences were small, there were some providers who had large deviations, suggesting that some providers would move from being shown as high-performers to low-performers and vice versa based on the chosen attribution method. We investigated examples of this and determined that the root cause was the difference in effective sample sizes for a provider. For the PAPR method, the effective sample size is simply the number of hospitalizations attributed to the provider. For the PAMM method, the effective sample size is the sum of all non-zero weights across all hospitalizations where the provider cared for a patient. By and large, the PAMM methodology provides more information of the provider effect on an outcome than the PAPR approach because every provider-patient interaction is considered. For example, providers who do not routinely discharge patients, but often care for patients, will have rankings that differ dramatically between the 2 methods.

The PAMM methodology has many statistical advantages that were not fully utilized in this comparative study. For example, we did not include any covariates in the MM models except for the expected value of the outcome, when it was available. Still, it is known that other covariates can impact an outcome as well, such as the patient’s age, socioeconomic indicators, existing chronic conditions, and severity of hospitalization, which can be added to the MM models as fixed effects. In this way, the PAMM approach can control for these other covariates, which are typically outside of the control of providers but typically ignored using OE indices. Therefore, using the PAMM approach would provide a fairer comparison of provider performance.

Using the PAMM method, most providers had a large sample size to assess their performance once all the weighted interactions were included. Still, there were a few who did not care for many patients for a variety of reasons. In these scenarios, MM models “borrow” strength from other providers to produce a more robust predicted provider effect by using a weighted average between the overall population trend and the specific provider outcomes (see Rao and Molina¹⁷). As a result, PAMM is a more suitable approach when the sample sizes of patients attributed to providers can be small.

One of the most interesting findings of this study was the relative size of the provider-level variance to the size of the fixed effect in each model (Table 3). Except for mortality, these variances suggest that there is a small difference in performance from one provider to another. However, these should be interpreted as the variance when only 1 provider is involved in the care of a patient. When multiple providers are involved, using basic statistical theory, the overall provider-level variance will be σ_γ² ∑w_ij² (see Equation 2). For example, the estimated variance among providers for LOS was 0.03 (on a log scale), but, using the scenario in the Figure, the overall provider-level variance for this hospitalization will be 0.03 (0.375²+ 0.125²+ 0.5²) = 0.012. Hence, the combined effect of providers on LOS is less than would be expected. Indeed, as more providers are involved with a patient’s care, the more their combined influence on an outcome is diluted.

In this study, the PAMM approach placed an equal weight on all provider-patient interactions via clinical note authorship, but that may not be optimal in some settings. For example, it may make more sense to set a higher weight on the provider who admitted or discharged the patient while placing less (or 0) weight on all other interactions. In the extreme, if the full weight were placed on 1 provider interaction (eg, during discharge, then the MM model would be reduced to a one-way random effects model. The flexibility of weighting interactions is a feature of the PAMM approach, but any weighting framework must be transparent to the providers before implementation.

Conclusion

This study demonstrates that the PAMM approach is a feasible option within a large health care organization. For P4P programs to be successful, providers must be able to trust that their performance will be fairly assessed and that all provider-patient interactions are captured to provide a full comparison amongst their peers. The PAMM methodology is one solution to spread the positive (and negative) outcomes across all providers who cared for a patient and therefore, if implemented, would add trust and fairness when measuring and assessing provider performance.

Acknowledgments: The authors thank Barrie Bradley for his support in the initial stages of this research and Dr. Syed Ismail Jafri for his help and support on the standard approaches of assessing and measuring provider performances.

Corresponding author: Rachel Ginn, MS, Banner Health Corporation, 2901 N. Central Ave., Phoenix, AZ 85012; [email protected].

Financial disclosures: None.

From Banner Health Corporation, Phoenix, AZ.

Background: Health care providers are routinely incentivized with pay-for-performance (P4P) metrics to increase the quality of care. In an inpatient setting, P4P models typically measure quality by attributing each patient’s outcome to a single provider even though many providers routinely care for the patient. This study investigates a new attribution approach aiming to distribute each outcome across all providers who provided care.

Methods: The methodology relies on a multi-membership model and is demonstrated in the Banner Health system using 3 clinical outcome measures (length of stay, 30-day readmissions, and mortality) and responses to 3 survey questions that measure a patient’s perception of their care. The new approach is compared to the “standard” method, which attributes each patient to only 1 provider.

Results: When ranking by clinical outcomes, both methods were concordant 72.1% to 82.1% of the time for top-half/bottom-half rankings, with a median percentile difference between 7 and 15. When ranking by survey scores, there was more agreement, with concordance between 84.1% and 86.6% and a median percentile difference between 11 and 13. Last, Pearson correlation coefficients of the paired percentiles ranged from 0.56 to 0.78.

Conclusion: The new approach provides a fairer solution when measuring provider performance.

Keywords: patient attribution; PAMM; PAPR; random effect model; pay for performance.

Providers practicing in hospitals are routinely evaluated based on their performance and, in many cases, are financially incentivized for a better-than-average performance within a pay-for-performance (P4P) model. The use of P4P models is based on the belief that they will “improve, motivate, and enhance providers to pursue aggressively and ultimately achieve the quality performance targets thus decreasing the number of medical errors with less malpractice events.”¹ Although P4P models continue to be a movement in health care, they have been challenging to implement.

One concern involves the general quality of implementation, such as defining metrics and targets, setting payout amounts, managing technology and market conditions, and gauging the level of transparency to the provider.² Another challenge, and the focus of this project, are concerns around measuring performance to avoid perceptions of unfairness. This concern can be minimized if the attribution is handled in a fairer way, by spreading it across all providers who affected the outcome, both in a positive or negative direction.³

To implement these models, the performance of providers needs to be measured and tracked periodically. This requires linking, or attributing, a patient’s outcome to a provider, which is almost always the attending or discharging provider (ie, a single provider).³ In this single-provider attribution approach, one provider will receive all the credit (good or bad) for their respective patients’ outcomes, even though the provider may have seen the patient only a fraction of the time during the hospitalization. Attributing outcomes—for example, length of stay (LOS), readmission rate, mortality rate, net promoter score (NPS)—using this approach reduces the validity of metrics designed to measure provider performance, especially in a rotating provider environment where many providers interact with and care for a patient. For example, the quality of providers’ interpersonal skills and competence were among the strongest determinants of patient satisfaction,⁴ but it is not credible that this is solely based on the last provider during a hospitalization.

Proportionally distributing the attribution of an outcome has been used successfully in other contexts. Typically, a statistical modeling approach using a multi-membership framework is used because it can handle the sometimes-complicated relationships within the hierarchy. It also allows for auxiliary variables to be introduced, which can help explain and control for exogenous effects.^5-7 For example, in the education setting, standardized testing is administered to students at defined years of schooling: at grades 4, 8, and 10, for instance. The progress of students, measured as the academic gains between test years, are proportionally attributed to all the teachers who the student has had between the test years. These partial attributions are combined to evaluate an overall teacher performance.^8,9

Although the multi-membership framework has been used in other industries, it has yet to be applied in measuring provider performance. The purpose of this project is to investigate the impact of using a multi-provider approach compared to the standard single-provider approach. The findings may lead to modifications in the way a provider’s performance is measured and, thus, how providers are compensated. A similar study investigated the impact of proportionally distributing patients’ outcomes across all rotating providers using a weighting method based on billing practices to measure the partial impact of each provider.³

This study is different in 2 fundamental ways. First, attribution is weighted based on the number of clinically documented interactions (via clinical notes) between a patient and all rotating providers during the hospitalization. Second, performance is measured via multi-membership models, which can estimate the effect (both positive and negative) that a provider has on an outcome, even when caring for a patient a fraction of the time during the hospitalization.

Methods

Setting

Banner Health is a non-profit, multi-hospital health care system across 6 states in the western United States that is uniquely positioned to study provider quality attribution models. It not only has a large number of providers and serves a broad patient population, but Banner Health also uses an instance of Cerner (Kansas City, MO), an enterprise-level electronic health record (EHR) system that connects all its facilities and allows for advanced analytics across its system.

For this study, we included only general medicine and surgery patients admitted and discharged from the inpatient setting between January 1, 2018, and December 31, 2018, who were between 18 and 89 years old at admission, and who had a LOS between 1 and 14 days. Visit- and patient-level data were collected from Cerner, while outcome data, and corresponding expected outcome data, were obtained from Premier, Inc. (Charlotte, NC) using their CareScience methodologies.¹⁰ To measure patient experience, response data were extracted from post-discharge surveys administered by InMoment (Salt Lake City, UT).

Provider Attribution Models

Provider Attribution by Physician of Record (PAPR). In the standard approach, denoted here as the PAPR model, 1 provider—typically the attending or discharging provider, which may be the same person—is attributed to the entire hospitalization. This provider is responsible for the patient’s care, and all patient outcomes are aggregated and attributed to the provider to gauge his or her performance. The PAPR model is the most popular form of attribution across many health care systems and is routinely used for P4P incentives.

In this study, the discharging provider was used when attributing hospitalizations using the PAPR model. Providers responsible for fewer than 12 discharges in the calendar year were excluded. Because of the directness of this type of attribution, the performance of 1 provider does not account for the performance of the other rotating providers during hospitalizations.

Provider Attribution by Multiple Membership (PAMM). In contrast, we introduce another attribution approach here that is designed to assign partial attribution to each provider who cares for the patient during the hospitalization. To aggregate the partial attributions, and possibly control for any exogenous or risk-based factors, a multiple-membership, or multi-member (MM), model is used. The MM model can measure the effect of a provider on an outcome even when the patient-to-provider relationship is complex, such as in a rotating provider environment.⁸

The purpose of this study is to compare attribution models and to determine whether there are meaningful differences between them. Therefore, for comparison purposes, the same discharging providers using the PAPR approach are eligible for the PAMM approach, so that both attribution models are using the same set of providers. All other providers are excluded because their performance would not be comparable to the PAPR approach.

While there are many ways to document provider-to-patient interactions, 2 methods are available in almost all health care systems. The first method is to link a provider’s billing charges to each patient-day combination. This approach limits the attribution to 1 provider per patient per day because multiple rotating providers cannot charge for the same patient-day combination.³ However, many providers interact with a patient on the same day, so using this approach excludes non-billed provider-to-patient interactions.

The second method, which was used in this study, relies on documented clinical notes within the EHR to determine how attribution is shared. In this approach, attribution is weighted based on the authorship of 3 types of eligible clinical notes: admitting history/physical notes (during admission), progress notes (during subsequent days), and discharge summary notes (during final discharge). This will (likely) result in many providers being linked to a patient on each day, which better reflects the clinical setting (Figure). Recently, clinical notes were used to attribute care of patients in an inpatient setting, and it was found that this approach provides a reliable way of tracking interactions and assigning ownership.¹¹

The provider-level attribution weights are based on the share of authorships of eligible note types. Specifically, for each provider j, let a_ij be the total count of eligible note types for hospitalization i authored by provider j, and let a_i be the overall total count of eligible note types for hospitalization i. Then the attribution weight is

(Eq. 1)

for hospitalization i and provider j. Note that ∑_jw_ij = 1: in other words, the total attribution, summed across all providers, is constrained to be 1 for each hospitalization.

Patient Outcomes

Outcomes were chosen based on their routine use in health care systems as standards when evaluating provider performance. This study included 6 outcomes: inpatient LOS, inpatient mortality, 30-day inpatient readmission, and patient responses from 3 survey questions. These outcomes can be collected without any manual chart reviews, and therefore are viewed as objective outcomes of provider performance.

Each outcome was aggregated for each provider using both attribution methods independently. For the PAPR method, observed-to-expected (OE) indices for LOS, mortality, and readmissions were calculated along with average patient survey scores. For the PAMM method, provider-level random effects from the fitted models were used. In both cases, the calculated measures were used for ranking purposes when determining top (or bottom) providers for each outcome.

Individual Provider Metrics for the PAPR Method

Inpatient LOS Index. Hospital inpatient LOS was measured as the number of days between admission date and discharge date. For each hospital visit, an expected LOS was determined using Premier’s CareScience Analytics (CSA) risk-adjustment methodology.¹⁰ The CSA methodology for LOS incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let o_ij and e_ij be the observed and expected LOS, respectively, for hospitalization i = 1,…,n_j attributed to provider j. Then the inpatient LOS index for provider j is L_j = ∑_io_ij⁄∑_ie_ij.

Inpatient Mortality Index. Inpatient mortality was defined as the death of the patient during hospitalization. For each hospitalization, an expected mortality probability was determined using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for mortality incorporates a patient’s demographics and comorbidities.

Just as before, let n_j be the number of hospitalizations attributed to provider j. Let m_ij = 1 if the patient died during hospitalization i = 1, … , n_j attributed to provider j; m_ij = 0 otherwise. Let p_ij^(m) be the corresponding expected mortality probability. Then the inpatient mortality index for provider j is M_j = ∑_im_ij⁄∑_ip_ij^(m).

30-Day Inpatient Readmission Index. A 30-day inpatient readmission was defined as the event when a patient is discharged and readmits back into the inpatient setting within 30 days. The inclusion criteria defined by the Centers for Medicare and Medicaid Services (CMS) all-cause hospital-wide readmission measure was used and, consequently, planned readmissions were excluded.¹² Readmissions could occur at any Banner hospital, including the same hospital. For each hospital visit, an expected readmission probability was derived using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for readmissions incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let r_ij = 1 if the patient had a readmission following hospitalization i = 1, … , n_j attributed to provider j; r_ij = 0 otherwise. Let p_ij^(r) be the corresponding expected readmission probability. Then the 30-day inpatient readmission index for provider j is R_j = ∑_ir_ij ⁄∑_ip_ij^(r).

Patient Survey Scores. The satisfaction of the patient’s experience during hospitalization was measured via post-discharge surveys administered by InMoment. Two survey questions were selected because they related directly to a provider’s interaction with the patient: “My interactions with doctors were excellent” (Doctor) and “I received the best possible care” (Care). A third question, “I would recommend this hospital to my family and friends,” was selected as a proxy measure of the overall experience and, in the aggregate, is referred to as the net promoter score (NPS).^13,14 The responses were measured on an 11-point Likert scale, ranging from “Strongly Disagree” (0) to “Strongly Agree” (10); “N/A” or missing responses were excluded.

The Likert responses were coded to 3 discrete values as follows: if the value was between 0 and 6, then -1 (ie, detractor); between 7 and 8 (ie, neutral), then 0; otherwise 1 (ie, promoter). Averaging these coded responses results in a patient survey score for each question. Specifically, let n_j be the number of hospitalizations attributed to provider j in which the patient responded to the survey question. Let s_ij ∈{−1, 0, 1} be the coded response linked to hospitalization i = 1, … , n_j attributed to provider j. Then the patient experience score for provider j is S_j = ∑_is_ij⁄n_j.

Handling Ties in Provider Performance Measures. Because ties can occur in the PAPR approach for all measures, a tie-breaking strategy is needed. For LOS indices, ties are less likely because their numerator is strictly greater than 0, and expected LOS values are typically distinct enough. Indeed, no ties were found in this study for LOS indices. However, mortality and readmission indices can routinely result in ties when the best possible index is achieved, such as 0 deaths or readmissions among attributed hospitalizations. To help differentiate between those indices in the PAPR approach, the total estimated risk (denominator) was utilized as a secondary scoring criterion.

Mortality and readmission metrics were addressed by sorting first by the outcome (mortality index), and second by the denominator (total estimated risk). For example, if provider A has the same mortality rate as provider B, then provider A would be ranked higher if the denominator was larger, indicating a higher risk for mortality.

Similarly, it was very common for providers to have the same overall average rating for a survey question. Therefore, the denominator (number of respondents) was used to break ties. However, the denominator sorting was bidirectional. For example, if the tied score was positive (more promoters than detractors) for providers A and B, then provider A would be ranked higher if the denominator was larger. Conversely, if the tied score between providers A and B was neutral or negative (more detractors than promoters), then provider A would be ranked lower if the denominator was larger.

Individual Provider Metrics for the PAMM Method

For the PAMM method, model-based metrics were derived using a MM model.⁸ Specifically, let J be the number of rotating providers in a health care system. Let Y_i be an outcome of interest from hospitalization i, X_1i, …, X_pi be fixed effects or covariates, and ß₁, …, ß_p be the coefficients for the respective covariates. Then the generalized MM statistical model is

(Eq. 2)

where g(μ_i) is a link function between the mean of the outcome, μ_i, and its linear predictor, ß₀, is the marginal intercept, w_ij represents the attribution weight of provider j on hospitalization i (described in Equation 1), and γ_j represents the random effect of provider j on the outcome with γ_j~N(0,σ_γ²).

For the mortality and readmission binary outcomes, logistic regression was performed using a logit link function, with the corresponding expected probability as the only fixed covariate. The expected probabilities were first converted into odds and then log-transformed before entering the model. For LOS, Poisson regression was performed using a log link function with the log-transformed expected LOS as the only fixed covariate. For coded patient experience responses, an ordered logistic regression was performed using a cumulative logit link function (no fixed effects were added).

MM Model-based Metrics. Each fitted MM model produces a predicted random effect for each provider. The provider-specific random effects can be interpreted as the unobserved influence of each provider on the outcome after controlling for any fixed effect included in the model. Therefore, the provider-specific random effects were used to evaluate the relative provider performance, which is analogous to the individual provider-level metrics used in the PAPR method.

Measuring provider performance using a MM model is more flexible and robust to outliers compared to the standard approach using OE indices or simple averages. First, although not investigated here, the effect of patient-, visit-, provider-, and/or temporal-level covariates can be controlled when evaluating provider performance. For example, a patient’s socioeconomic status, a provider’s workload, and seasonal factors can be added to the MM model. These external factors are not accounted for in OE indices.

Another advantage of using predicted random effects is the concept of “shrinkage.” The process of estimating random effects inherently accounts for small sample sizes (when providers do not treat a large enough sample of patients) and/or when there is a large ratio of patient variance to provider variance (for instance, when patient outcome variability is much higher compared to provider performance variability). In both cases, the estimation of the random effect is pulled ever closer to 0, signaling that the provider performance is closer to the population average. See Henderson¹⁵ and Mood¹⁶ for further details.

In contrast, OE indices can result in unreliable estimates when a provider has not cared for many patients. This is especially prevalent when the outcome is binary with a low probability of occurring, such as mortality. Indeed, provider-level mortality OE indices are routinely 0 when the patient counts are low, which skews performance rankings unfairly. Finally, OE indices also ignore the magnitude of the variance of an outcome between providers and patients, which can be large.

Comparison Methodology

In this study, we seek to compare the 2 methods of attribution, PAPR and PAMM, to determine whether there are meaningful differences between them when measuring provider performance. Using retrospective data described in the next section, each attribution method was used independently to derive provider-level metrics. To assess relative performance, percentiles were assigned to each provider based on their metric values so that, in the end, there were 2 percentile ranks for each provider for each metric.

Using these paired percentiles, we derived the following measures of concordance, similar to Herzke, Michtalik³: (1) the percent concordance measure—defined as the number of providers who landed in the top half (greater than the median) or bottom half under both attribution models—divided by the total number of providers; (2) the median of the absolute difference in percentiles under both attribution models; and (3) the Pearson correlation coefficient of the paired provider ranks. The first measure is a global measure of concordance between the 2 approaches and would be expected to be 50% by chance. The second measure gauges how an individual provider’s rank is affected by the change in attribution methodologies. The third measure is a statistical measure of linear correlation of the paired percentiles and was not included in the Herzke, Michtalik³ study.

All statistical analyses were performed on SAS (version 9.4; Cary, NC) and the MM models were fitted using PROC GLIMMIX with the EFFECT statement. The Banner Health Institutional Review Board approved this study.

Results

Descriptive Statistics

A total of 58,730 hospitalizations were included, of which care was provided by 963 unique providers across 25 acute care and critical access hospitals. Table 1 contains patient characteristics, and Table 2 depicts overall unadjusted outcomes. Providers responsible for less than 12 discharges in the calendar year were excluded from both approaches. Also, some hospitalizations were excluded when expected values were not available.

Multi-Membership Model Results

Table 3 displays the results after independently fitting MM models to each of the 3 clinical outcomes. Along with a marginal intercept, the only covariate in each model was the corresponding expected value after a transformation. This was added to use the same information that is typically used in OE indices, therefore allowing for a proper comparison between the 2 attribution methods. The provider-level variance represents the between-provider variation and measures the amount of influence providers have on the corresponding outcome after controlling for any covariates in the model. A provider-level variance of 0 would indicate that providers do not have any influence on the outcome. While the mortality and readmission model results can be compared to each other, the LOS model cannot given its different scale and transformation altogether.

The results in Table 3 suggest that each expected value covariate is highly correlated with its corresponding outcome, which is the anticipated conclusion given that they are constructed in this fashion. The estimated provider-level variances indicate that, after including an expected value in the model, providers have less of an influence on a patient’s LOS and likelihood of being readmitted. On the other hand, the results suggest that providers have much more influence on the likelihood of a patient dying in the hospital, even after controlling for an expected mortality covariate.

Table 4 shows the results after independently fitting MM-ordered logistic models to each of the 3 survey questions. The similar provider-level variances suggest that providers have the same influence on the patient’s perception of the quality of their interactions with the doctor (Doctor), the quality of the care they received (Care), and their likelihood to recommend a friend or family member to the hospital (NPS).

Comparison Results Between Both Attribution Methods

Table 5 compares the 2 attribution methods when ranking providers based on their performance on each outcome measure. The comparison metrics gauge how well the 2 methods agree overall (percent concordance), agree at the provider level (absolute percentile difference and interquartile range [IQR]), and how the paired percentiles linearly correlate to each other (Pearson correlation coefficient).

LOS, by a small margin, had the lowest concordance of clinical outcomes (72.1%), followed by mortality (75.9%) and readmissions (82.1%). Generally, the survey scores had higher percent concordance than the clinical outcome measures, with Doctor at 84.1%, Care at 85.9%, and NPS having the highest percent concordance at 86.6%. Given that by chance the percent concordance is expected to be 50%, there was notable discordance, especially with the clinical outcome measures. Using LOS performance as an example, one attribution methodology would rank a provider in the top half or bottom half, while the other attribution methodology would rank the same provider exactly the opposite way about 28% of the time.

The median absolute percentile difference between the 2 methods was more modest (between 7 and 15). Still, there were some providers whose performance ranking was heavily impacted by the attribution methodology that was used. This was especially true when evaluating performance for certain clinical measures, where the attribution method that was used could change the provider performance percentile by up to 90 levels.

The paired percentiles were positively correlated when ranking performance using any of the 6 measures. This suggests that both methodologies assess performance generally in the same direction, irrespective of the methodology and measure. We did not investigate more complex correlation measures and left this for future research.

It should be noted that ties occurred much more frequently with the PAPR method than when using PAMM and therefore required tie-breaking rules to be designed. Given the nature of OE indices, PAPR methodology is especially sensitive to ties whenever the measure includes counting the number of events (for example, mortality and readmissions) and whenever there are many providers with very few attributed patients. On the other hand, using the PAMM method is much more robust against ties given that the summation of all the weighted attributed outcomes will rarely result in ties, even with a nominal set of providers.

 

 

Discussion

In this study, the PAMM methodology was introduced and was used to assess relative provider performance on 3 clinical outcome measures and 3 patient survey scores. The new approach aims to distribute each outcome among all providers who provided care for a patient in an inpatient setting. Clinical notes were used to account for patient-to-provider interactions, and fitted MM statistical models were used to compute the effects that each provider had on each outcome. The provider effect was introduced as a random effect, and the set of predicted random effects was used to rank the performance of each provider.

The PAMM approach was compared to the more traditional methodology, PAPR, where each patient is attributed to only 1 provider: the discharging physician in this study. Using this approach, OE indices of clinical outcomes and averages of survey scores were used to rank the performance of each provider. This approach resulted in many ties, which were broken based on the number of hospitalizations, although other tie-breaking methods may be used in practice.

Both methodologies showed modest concordance with each other for the clinical outcomes, but higher concordance for the patient survey scores. This was also true when using the Pearson correlation coefficient to assess agreement. The 1 outcome measure that showed the least concordance and least linear correlation between methods was LOS, which would suggest that LOS performance is more sensitive to the attribution methodology that is used. However, it was the least concordant by a small margin.

Furthermore, although the medians of the absolute percentile differences were small, there were some providers who had large deviations, suggesting that some providers would move from being shown as high-performers to low-performers and vice versa based on the chosen attribution method. We investigated examples of this and determined that the root cause was the difference in effective sample sizes for a provider. For the PAPR method, the effective sample size is simply the number of hospitalizations attributed to the provider. For the PAMM method, the effective sample size is the sum of all non-zero weights across all hospitalizations where the provider cared for a patient. By and large, the PAMM methodology provides more information of the provider effect on an outcome than the PAPR approach because every provider-patient interaction is considered. For example, providers who do not routinely discharge patients, but often care for patients, will have rankings that differ dramatically between the 2 methods.

The PAMM methodology has many statistical advantages that were not fully utilized in this comparative study. For example, we did not include any covariates in the MM models except for the expected value of the outcome, when it was available. Still, it is known that other covariates can impact an outcome as well, such as the patient’s age, socioeconomic indicators, existing chronic conditions, and severity of hospitalization, which can be added to the MM models as fixed effects. In this way, the PAMM approach can control for these other covariates, which are typically outside of the control of providers but typically ignored using OE indices. Therefore, using the PAMM approach would provide a fairer comparison of provider performance.

Using the PAMM method, most providers had a large sample size to assess their performance once all the weighted interactions were included. Still, there were a few who did not care for many patients for a variety of reasons. In these scenarios, MM models “borrow” strength from other providers to produce a more robust predicted provider effect by using a weighted average between the overall population trend and the specific provider outcomes (see Rao and Molina¹⁷). As a result, PAMM is a more suitable approach when the sample sizes of patients attributed to providers can be small.

One of the most interesting findings of this study was the relative size of the provider-level variance to the size of the fixed effect in each model (Table 3). Except for mortality, these variances suggest that there is a small difference in performance from one provider to another. However, these should be interpreted as the variance when only 1 provider is involved in the care of a patient. When multiple providers are involved, using basic statistical theory, the overall provider-level variance will be σ_γ² ∑w_ij² (see Equation 2). For example, the estimated variance among providers for LOS was 0.03 (on a log scale), but, using the scenario in the Figure, the overall provider-level variance for this hospitalization will be 0.03 (0.375²+ 0.125²+ 0.5²) = 0.012. Hence, the combined effect of providers on LOS is less than would be expected. Indeed, as more providers are involved with a patient’s care, the more their combined influence on an outcome is diluted.

In this study, the PAMM approach placed an equal weight on all provider-patient interactions via clinical note authorship, but that may not be optimal in some settings. For example, it may make more sense to set a higher weight on the provider who admitted or discharged the patient while placing less (or 0) weight on all other interactions. In the extreme, if the full weight were placed on 1 provider interaction (eg, during discharge, then the MM model would be reduced to a one-way random effects model. The flexibility of weighting interactions is a feature of the PAMM approach, but any weighting framework must be transparent to the providers before implementation.

Conclusion

This study demonstrates that the PAMM approach is a feasible option within a large health care organization. For P4P programs to be successful, providers must be able to trust that their performance will be fairly assessed and that all provider-patient interactions are captured to provide a full comparison amongst their peers. The PAMM methodology is one solution to spread the positive (and negative) outcomes across all providers who cared for a patient and therefore, if implemented, would add trust and fairness when measuring and assessing provider performance.

Acknowledgments: The authors thank Barrie Bradley for his support in the initial stages of this research and Dr. Syed Ismail Jafri for his help and support on the standard approaches of assessing and measuring provider performances.

Corresponding author: Rachel Ginn, MS, Banner Health Corporation, 2901 N. Central Ave., Phoenix, AZ 85012; [email protected].

Financial disclosures: None.

From Banner Health Corporation, Phoenix, AZ.

Background: Health care providers are routinely incentivized with pay-for-performance (P4P) metrics to increase the quality of care. In an inpatient setting, P4P models typically measure quality by attributing each patient’s outcome to a single provider even though many providers routinely care for the patient. This study investigates a new attribution approach aiming to distribute each outcome across all providers who provided care.

Methods: The methodology relies on a multi-membership model and is demonstrated in the Banner Health system using 3 clinical outcome measures (length of stay, 30-day readmissions, and mortality) and responses to 3 survey questions that measure a patient’s perception of their care. The new approach is compared to the “standard” method, which attributes each patient to only 1 provider.

Results: When ranking by clinical outcomes, both methods were concordant 72.1% to 82.1% of the time for top-half/bottom-half rankings, with a median percentile difference between 7 and 15. When ranking by survey scores, there was more agreement, with concordance between 84.1% and 86.6% and a median percentile difference between 11 and 13. Last, Pearson correlation coefficients of the paired percentiles ranged from 0.56 to 0.78.

Conclusion: The new approach provides a fairer solution when measuring provider performance.

Keywords: patient attribution; PAMM; PAPR; random effect model; pay for performance.

Providers practicing in hospitals are routinely evaluated based on their performance and, in many cases, are financially incentivized for a better-than-average performance within a pay-for-performance (P4P) model. The use of P4P models is based on the belief that they will “improve, motivate, and enhance providers to pursue aggressively and ultimately achieve the quality performance targets thus decreasing the number of medical errors with less malpractice events.”¹ Although P4P models continue to be a movement in health care, they have been challenging to implement.

One concern involves the general quality of implementation, such as defining metrics and targets, setting payout amounts, managing technology and market conditions, and gauging the level of transparency to the provider.² Another challenge, and the focus of this project, are concerns around measuring performance to avoid perceptions of unfairness. This concern can be minimized if the attribution is handled in a fairer way, by spreading it across all providers who affected the outcome, both in a positive or negative direction.³

To implement these models, the performance of providers needs to be measured and tracked periodically. This requires linking, or attributing, a patient’s outcome to a provider, which is almost always the attending or discharging provider (ie, a single provider).³ In this single-provider attribution approach, one provider will receive all the credit (good or bad) for their respective patients’ outcomes, even though the provider may have seen the patient only a fraction of the time during the hospitalization. Attributing outcomes—for example, length of stay (LOS), readmission rate, mortality rate, net promoter score (NPS)—using this approach reduces the validity of metrics designed to measure provider performance, especially in a rotating provider environment where many providers interact with and care for a patient. For example, the quality of providers’ interpersonal skills and competence were among the strongest determinants of patient satisfaction,⁴ but it is not credible that this is solely based on the last provider during a hospitalization.

Proportionally distributing the attribution of an outcome has been used successfully in other contexts. Typically, a statistical modeling approach using a multi-membership framework is used because it can handle the sometimes-complicated relationships within the hierarchy. It also allows for auxiliary variables to be introduced, which can help explain and control for exogenous effects.^5-7 For example, in the education setting, standardized testing is administered to students at defined years of schooling: at grades 4, 8, and 10, for instance. The progress of students, measured as the academic gains between test years, are proportionally attributed to all the teachers who the student has had between the test years. These partial attributions are combined to evaluate an overall teacher performance.^8,9

Although the multi-membership framework has been used in other industries, it has yet to be applied in measuring provider performance. The purpose of this project is to investigate the impact of using a multi-provider approach compared to the standard single-provider approach. The findings may lead to modifications in the way a provider’s performance is measured and, thus, how providers are compensated. A similar study investigated the impact of proportionally distributing patients’ outcomes across all rotating providers using a weighting method based on billing practices to measure the partial impact of each provider.³

This study is different in 2 fundamental ways. First, attribution is weighted based on the number of clinically documented interactions (via clinical notes) between a patient and all rotating providers during the hospitalization. Second, performance is measured via multi-membership models, which can estimate the effect (both positive and negative) that a provider has on an outcome, even when caring for a patient a fraction of the time during the hospitalization.

Methods

Setting

Banner Health is a non-profit, multi-hospital health care system across 6 states in the western United States that is uniquely positioned to study provider quality attribution models. It not only has a large number of providers and serves a broad patient population, but Banner Health also uses an instance of Cerner (Kansas City, MO), an enterprise-level electronic health record (EHR) system that connects all its facilities and allows for advanced analytics across its system.

For this study, we included only general medicine and surgery patients admitted and discharged from the inpatient setting between January 1, 2018, and December 31, 2018, who were between 18 and 89 years old at admission, and who had a LOS between 1 and 14 days. Visit- and patient-level data were collected from Cerner, while outcome data, and corresponding expected outcome data, were obtained from Premier, Inc. (Charlotte, NC) using their CareScience methodologies.¹⁰ To measure patient experience, response data were extracted from post-discharge surveys administered by InMoment (Salt Lake City, UT).

Provider Attribution Models

Provider Attribution by Physician of Record (PAPR). In the standard approach, denoted here as the PAPR model, 1 provider—typically the attending or discharging provider, which may be the same person—is attributed to the entire hospitalization. This provider is responsible for the patient’s care, and all patient outcomes are aggregated and attributed to the provider to gauge his or her performance. The PAPR model is the most popular form of attribution across many health care systems and is routinely used for P4P incentives.

In this study, the discharging provider was used when attributing hospitalizations using the PAPR model. Providers responsible for fewer than 12 discharges in the calendar year were excluded. Because of the directness of this type of attribution, the performance of 1 provider does not account for the performance of the other rotating providers during hospitalizations.

Provider Attribution by Multiple Membership (PAMM). In contrast, we introduce another attribution approach here that is designed to assign partial attribution to each provider who cares for the patient during the hospitalization. To aggregate the partial attributions, and possibly control for any exogenous or risk-based factors, a multiple-membership, or multi-member (MM), model is used. The MM model can measure the effect of a provider on an outcome even when the patient-to-provider relationship is complex, such as in a rotating provider environment.⁸

The purpose of this study is to compare attribution models and to determine whether there are meaningful differences between them. Therefore, for comparison purposes, the same discharging providers using the PAPR approach are eligible for the PAMM approach, so that both attribution models are using the same set of providers. All other providers are excluded because their performance would not be comparable to the PAPR approach.

While there are many ways to document provider-to-patient interactions, 2 methods are available in almost all health care systems. The first method is to link a provider’s billing charges to each patient-day combination. This approach limits the attribution to 1 provider per patient per day because multiple rotating providers cannot charge for the same patient-day combination.³ However, many providers interact with a patient on the same day, so using this approach excludes non-billed provider-to-patient interactions.

The second method, which was used in this study, relies on documented clinical notes within the EHR to determine how attribution is shared. In this approach, attribution is weighted based on the authorship of 3 types of eligible clinical notes: admitting history/physical notes (during admission), progress notes (during subsequent days), and discharge summary notes (during final discharge). This will (likely) result in many providers being linked to a patient on each day, which better reflects the clinical setting (Figure). Recently, clinical notes were used to attribute care of patients in an inpatient setting, and it was found that this approach provides a reliable way of tracking interactions and assigning ownership.¹¹

The provider-level attribution weights are based on the share of authorships of eligible note types. Specifically, for each provider j, let a_ij be the total count of eligible note types for hospitalization i authored by provider j, and let a_i be the overall total count of eligible note types for hospitalization i. Then the attribution weight is

(Eq. 1)

for hospitalization i and provider j. Note that ∑_jw_ij = 1: in other words, the total attribution, summed across all providers, is constrained to be 1 for each hospitalization.

Patient Outcomes

Outcomes were chosen based on their routine use in health care systems as standards when evaluating provider performance. This study included 6 outcomes: inpatient LOS, inpatient mortality, 30-day inpatient readmission, and patient responses from 3 survey questions. These outcomes can be collected without any manual chart reviews, and therefore are viewed as objective outcomes of provider performance.

Each outcome was aggregated for each provider using both attribution methods independently. For the PAPR method, observed-to-expected (OE) indices for LOS, mortality, and readmissions were calculated along with average patient survey scores. For the PAMM method, provider-level random effects from the fitted models were used. In both cases, the calculated measures were used for ranking purposes when determining top (or bottom) providers for each outcome.

Individual Provider Metrics for the PAPR Method

Inpatient LOS Index. Hospital inpatient LOS was measured as the number of days between admission date and discharge date. For each hospital visit, an expected LOS was determined using Premier’s CareScience Analytics (CSA) risk-adjustment methodology.¹⁰ The CSA methodology for LOS incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let o_ij and e_ij be the observed and expected LOS, respectively, for hospitalization i = 1,…,n_j attributed to provider j. Then the inpatient LOS index for provider j is L_j = ∑_io_ij⁄∑_ie_ij.

Inpatient Mortality Index. Inpatient mortality was defined as the death of the patient during hospitalization. For each hospitalization, an expected mortality probability was determined using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for mortality incorporates a patient’s demographics and comorbidities.

Just as before, let n_j be the number of hospitalizations attributed to provider j. Let m_ij = 1 if the patient died during hospitalization i = 1, … , n_j attributed to provider j; m_ij = 0 otherwise. Let p_ij^(m) be the corresponding expected mortality probability. Then the inpatient mortality index for provider j is M_j = ∑_im_ij⁄∑_ip_ij^(m).

30-Day Inpatient Readmission Index. A 30-day inpatient readmission was defined as the event when a patient is discharged and readmits back into the inpatient setting within 30 days. The inclusion criteria defined by the Centers for Medicare and Medicaid Services (CMS) all-cause hospital-wide readmission measure was used and, consequently, planned readmissions were excluded.¹² Readmissions could occur at any Banner hospital, including the same hospital. For each hospital visit, an expected readmission probability was derived using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for readmissions incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let r_ij = 1 if the patient had a readmission following hospitalization i = 1, … , n_j attributed to provider j; r_ij = 0 otherwise. Let p_ij^(r) be the corresponding expected readmission probability. Then the 30-day inpatient readmission index for provider j is R_j = ∑_ir_ij ⁄∑_ip_ij^(r).

Patient Survey Scores. The satisfaction of the patient’s experience during hospitalization was measured via post-discharge surveys administered by InMoment. Two survey questions were selected because they related directly to a provider’s interaction with the patient: “My interactions with doctors were excellent” (Doctor) and “I received the best possible care” (Care). A third question, “I would recommend this hospital to my family and friends,” was selected as a proxy measure of the overall experience and, in the aggregate, is referred to as the net promoter score (NPS).^13,14 The responses were measured on an 11-point Likert scale, ranging from “Strongly Disagree” (0) to “Strongly Agree” (10); “N/A” or missing responses were excluded.

The Likert responses were coded to 3 discrete values as follows: if the value was between 0 and 6, then -1 (ie, detractor); between 7 and 8 (ie, neutral), then 0; otherwise 1 (ie, promoter). Averaging these coded responses results in a patient survey score for each question. Specifically, let n_j be the number of hospitalizations attributed to provider j in which the patient responded to the survey question. Let s_ij ∈{−1, 0, 1} be the coded response linked to hospitalization i = 1, … , n_j attributed to provider j. Then the patient experience score for provider j is S_j = ∑_is_ij⁄n_j.

Handling Ties in Provider Performance Measures. Because ties can occur in the PAPR approach for all measures, a tie-breaking strategy is needed. For LOS indices, ties are less likely because their numerator is strictly greater than 0, and expected LOS values are typically distinct enough. Indeed, no ties were found in this study for LOS indices. However, mortality and readmission indices can routinely result in ties when the best possible index is achieved, such as 0 deaths or readmissions among attributed hospitalizations. To help differentiate between those indices in the PAPR approach, the total estimated risk (denominator) was utilized as a secondary scoring criterion.

Mortality and readmission metrics were addressed by sorting first by the outcome (mortality index), and second by the denominator (total estimated risk). For example, if provider A has the same mortality rate as provider B, then provider A would be ranked higher if the denominator was larger, indicating a higher risk for mortality.

Similarly, it was very common for providers to have the same overall average rating for a survey question. Therefore, the denominator (number of respondents) was used to break ties. However, the denominator sorting was bidirectional. For example, if the tied score was positive (more promoters than detractors) for providers A and B, then provider A would be ranked higher if the denominator was larger. Conversely, if the tied score between providers A and B was neutral or negative (more detractors than promoters), then provider A would be ranked lower if the denominator was larger.

Individual Provider Metrics for the PAMM Method

For the PAMM method, model-based metrics were derived using a MM model.⁸ Specifically, let J be the number of rotating providers in a health care system. Let Y_i be an outcome of interest from hospitalization i, X_1i, …, X_pi be fixed effects or covariates, and ß₁, …, ß_p be the coefficients for the respective covariates. Then the generalized MM statistical model is

(Eq. 2)

where g(μ_i) is a link function between the mean of the outcome, μ_i, and its linear predictor, ß₀, is the marginal intercept, w_ij represents the attribution weight of provider j on hospitalization i (described in Equation 1), and γ_j represents the random effect of provider j on the outcome with γ_j~N(0,σ_γ²).

For the mortality and readmission binary outcomes, logistic regression was performed using a logit link function, with the corresponding expected probability as the only fixed covariate. The expected probabilities were first converted into odds and then log-transformed before entering the model. For LOS, Poisson regression was performed using a log link function with the log-transformed expected LOS as the only fixed covariate. For coded patient experience responses, an ordered logistic regression was performed using a cumulative logit link function (no fixed effects were added).

MM Model-based Metrics. Each fitted MM model produces a predicted random effect for each provider. The provider-specific random effects can be interpreted as the unobserved influence of each provider on the outcome after controlling for any fixed effect included in the model. Therefore, the provider-specific random effects were used to evaluate the relative provider performance, which is analogous to the individual provider-level metrics used in the PAPR method.

Measuring provider performance using a MM model is more flexible and robust to outliers compared to the standard approach using OE indices or simple averages. First, although not investigated here, the effect of patient-, visit-, provider-, and/or temporal-level covariates can be controlled when evaluating provider performance. For example, a patient’s socioeconomic status, a provider’s workload, and seasonal factors can be added to the MM model. These external factors are not accounted for in OE indices.

Another advantage of using predicted random effects is the concept of “shrinkage.” The process of estimating random effects inherently accounts for small sample sizes (when providers do not treat a large enough sample of patients) and/or when there is a large ratio of patient variance to provider variance (for instance, when patient outcome variability is much higher compared to provider performance variability). In both cases, the estimation of the random effect is pulled ever closer to 0, signaling that the provider performance is closer to the population average. See Henderson¹⁵ and Mood¹⁶ for further details.

In contrast, OE indices can result in unreliable estimates when a provider has not cared for many patients. This is especially prevalent when the outcome is binary with a low probability of occurring, such as mortality. Indeed, provider-level mortality OE indices are routinely 0 when the patient counts are low, which skews performance rankings unfairly. Finally, OE indices also ignore the magnitude of the variance of an outcome between providers and patients, which can be large.

Comparison Methodology

In this study, we seek to compare the 2 methods of attribution, PAPR and PAMM, to determine whether there are meaningful differences between them when measuring provider performance. Using retrospective data described in the next section, each attribution method was used independently to derive provider-level metrics. To assess relative performance, percentiles were assigned to each provider based on their metric values so that, in the end, there were 2 percentile ranks for each provider for each metric.

Using these paired percentiles, we derived the following measures of concordance, similar to Herzke, Michtalik³: (1) the percent concordance measure—defined as the number of providers who landed in the top half (greater than the median) or bottom half under both attribution models—divided by the total number of providers; (2) the median of the absolute difference in percentiles under both attribution models; and (3) the Pearson correlation coefficient of the paired provider ranks. The first measure is a global measure of concordance between the 2 approaches and would be expected to be 50% by chance. The second measure gauges how an individual provider’s rank is affected by the change in attribution methodologies. The third measure is a statistical measure of linear correlation of the paired percentiles and was not included in the Herzke, Michtalik³ study.

All statistical analyses were performed on SAS (version 9.4; Cary, NC) and the MM models were fitted using PROC GLIMMIX with the EFFECT statement. The Banner Health Institutional Review Board approved this study.

Results

Descriptive Statistics

A total of 58,730 hospitalizations were included, of which care was provided by 963 unique providers across 25 acute care and critical access hospitals. Table 1 contains patient characteristics, and Table 2 depicts overall unadjusted outcomes. Providers responsible for less than 12 discharges in the calendar year were excluded from both approaches. Also, some hospitalizations were excluded when expected values were not available.

Multi-Membership Model Results

Table 3 displays the results after independently fitting MM models to each of the 3 clinical outcomes. Along with a marginal intercept, the only covariate in each model was the corresponding expected value after a transformation. This was added to use the same information that is typically used in OE indices, therefore allowing for a proper comparison between the 2 attribution methods. The provider-level variance represents the between-provider variation and measures the amount of influence providers have on the corresponding outcome after controlling for any covariates in the model. A provider-level variance of 0 would indicate that providers do not have any influence on the outcome. While the mortality and readmission model results can be compared to each other, the LOS model cannot given its different scale and transformation altogether.

The results in Table 3 suggest that each expected value covariate is highly correlated with its corresponding outcome, which is the anticipated conclusion given that they are constructed in this fashion. The estimated provider-level variances indicate that, after including an expected value in the model, providers have less of an influence on a patient’s LOS and likelihood of being readmitted. On the other hand, the results suggest that providers have much more influence on the likelihood of a patient dying in the hospital, even after controlling for an expected mortality covariate.

Table 4 shows the results after independently fitting MM-ordered logistic models to each of the 3 survey questions. The similar provider-level variances suggest that providers have the same influence on the patient’s perception of the quality of their interactions with the doctor (Doctor), the quality of the care they received (Care), and their likelihood to recommend a friend or family member to the hospital (NPS).

Comparison Results Between Both Attribution Methods

Table 5 compares the 2 attribution methods when ranking providers based on their performance on each outcome measure. The comparison metrics gauge how well the 2 methods agree overall (percent concordance), agree at the provider level (absolute percentile difference and interquartile range [IQR]), and how the paired percentiles linearly correlate to each other (Pearson correlation coefficient).

LOS, by a small margin, had the lowest concordance of clinical outcomes (72.1%), followed by mortality (75.9%) and readmissions (82.1%). Generally, the survey scores had higher percent concordance than the clinical outcome measures, with Doctor at 84.1%, Care at 85.9%, and NPS having the highest percent concordance at 86.6%. Given that by chance the percent concordance is expected to be 50%, there was notable discordance, especially with the clinical outcome measures. Using LOS performance as an example, one attribution methodology would rank a provider in the top half or bottom half, while the other attribution methodology would rank the same provider exactly the opposite way about 28% of the time.

The median absolute percentile difference between the 2 methods was more modest (between 7 and 15). Still, there were some providers whose performance ranking was heavily impacted by the attribution methodology that was used. This was especially true when evaluating performance for certain clinical measures, where the attribution method that was used could change the provider performance percentile by up to 90 levels.

The paired percentiles were positively correlated when ranking performance using any of the 6 measures. This suggests that both methodologies assess performance generally in the same direction, irrespective of the methodology and measure. We did not investigate more complex correlation measures and left this for future research.

It should be noted that ties occurred much more frequently with the PAPR method than when using PAMM and therefore required tie-breaking rules to be designed. Given the nature of OE indices, PAPR methodology is especially sensitive to ties whenever the measure includes counting the number of events (for example, mortality and readmissions) and whenever there are many providers with very few attributed patients. On the other hand, using the PAMM method is much more robust against ties given that the summation of all the weighted attributed outcomes will rarely result in ties, even with a nominal set of providers.

 

 

Discussion

In this study, the PAMM methodology was introduced and was used to assess relative provider performance on 3 clinical outcome measures and 3 patient survey scores. The new approach aims to distribute each outcome among all providers who provided care for a patient in an inpatient setting. Clinical notes were used to account for patient-to-provider interactions, and fitted MM statistical models were used to compute the effects that each provider had on each outcome. The provider effect was introduced as a random effect, and the set of predicted random effects was used to rank the performance of each provider.

The PAMM approach was compared to the more traditional methodology, PAPR, where each patient is attributed to only 1 provider: the discharging physician in this study. Using this approach, OE indices of clinical outcomes and averages of survey scores were used to rank the performance of each provider. This approach resulted in many ties, which were broken based on the number of hospitalizations, although other tie-breaking methods may be used in practice.

Both methodologies showed modest concordance with each other for the clinical outcomes, but higher concordance for the patient survey scores. This was also true when using the Pearson correlation coefficient to assess agreement. The 1 outcome measure that showed the least concordance and least linear correlation between methods was LOS, which would suggest that LOS performance is more sensitive to the attribution methodology that is used. However, it was the least concordant by a small margin.

Furthermore, although the medians of the absolute percentile differences were small, there were some providers who had large deviations, suggesting that some providers would move from being shown as high-performers to low-performers and vice versa based on the chosen attribution method. We investigated examples of this and determined that the root cause was the difference in effective sample sizes for a provider. For the PAPR method, the effective sample size is simply the number of hospitalizations attributed to the provider. For the PAMM method, the effective sample size is the sum of all non-zero weights across all hospitalizations where the provider cared for a patient. By and large, the PAMM methodology provides more information of the provider effect on an outcome than the PAPR approach because every provider-patient interaction is considered. For example, providers who do not routinely discharge patients, but often care for patients, will have rankings that differ dramatically between the 2 methods.

The PAMM methodology has many statistical advantages that were not fully utilized in this comparative study. For example, we did not include any covariates in the MM models except for the expected value of the outcome, when it was available. Still, it is known that other covariates can impact an outcome as well, such as the patient’s age, socioeconomic indicators, existing chronic conditions, and severity of hospitalization, which can be added to the MM models as fixed effects. In this way, the PAMM approach can control for these other covariates, which are typically outside of the control of providers but typically ignored using OE indices. Therefore, using the PAMM approach would provide a fairer comparison of provider performance.

Using the PAMM method, most providers had a large sample size to assess their performance once all the weighted interactions were included. Still, there were a few who did not care for many patients for a variety of reasons. In these scenarios, MM models “borrow” strength from other providers to produce a more robust predicted provider effect by using a weighted average between the overall population trend and the specific provider outcomes (see Rao and Molina¹⁷). As a result, PAMM is a more suitable approach when the sample sizes of patients attributed to providers can be small.

One of the most interesting findings of this study was the relative size of the provider-level variance to the size of the fixed effect in each model (Table 3). Except for mortality, these variances suggest that there is a small difference in performance from one provider to another. However, these should be interpreted as the variance when only 1 provider is involved in the care of a patient. When multiple providers are involved, using basic statistical theory, the overall provider-level variance will be σ_γ² ∑w_ij² (see Equation 2). For example, the estimated variance among providers for LOS was 0.03 (on a log scale), but, using the scenario in the Figure, the overall provider-level variance for this hospitalization will be 0.03 (0.375²+ 0.125²+ 0.5²) = 0.012. Hence, the combined effect of providers on LOS is less than would be expected. Indeed, as more providers are involved with a patient’s care, the more their combined influence on an outcome is diluted.

In this study, the PAMM approach placed an equal weight on all provider-patient interactions via clinical note authorship, but that may not be optimal in some settings. For example, it may make more sense to set a higher weight on the provider who admitted or discharged the patient while placing less (or 0) weight on all other interactions. In the extreme, if the full weight were placed on 1 provider interaction (eg, during discharge, then the MM model would be reduced to a one-way random effects model. The flexibility of weighting interactions is a feature of the PAMM approach, but any weighting framework must be transparent to the providers before implementation.

Conclusion

This study demonstrates that the PAMM approach is a feasible option within a large health care organization. For P4P programs to be successful, providers must be able to trust that their performance will be fairly assessed and that all provider-patient interactions are captured to provide a full comparison amongst their peers. The PAMM methodology is one solution to spread the positive (and negative) outcomes across all providers who cared for a patient and therefore, if implemented, would add trust and fairness when measuring and assessing provider performance.

Acknowledgments: The authors thank Barrie Bradley for his support in the initial stages of this research and Dr. Syed Ismail Jafri for his help and support on the standard approaches of assessing and measuring provider performances.

Corresponding author: Rachel Ginn, MS, Banner Health Corporation, 2901 N. Central Ave., Phoenix, AZ 85012; [email protected].

Financial disclosures: None.

From Nova Southeastern University College of Pharmacy, Fort Lauderdale, FL.

Abstract

• Objective: To determine pharmacists’ preferences in bleed risk tool (BRT) usage and gastroprotection when bleed risk was lower than or equal to stroke risk in patients with nonvalvular atrial fibrillation and who were candidates for oral anticoagulation therapy (warfarin or direct oral anticoagulants [DOACs]).
• Methods: A survey consisting of 4 domains (demographics, clinical experience, BRT usage, and treatment preferences based on cases where bleed risk was lower than or equal to stroke risk) was developed. The anonymous survey was disseminated via REDCap software to members of the American College of Clinical Pharmacy ambulatory care and cardiology Practice-based Research Networks. Descriptive statistics were calculated for all study variables and inferential statistics were employed as necessary.
• Results: Of 165 BRT users, 97% preferred HAS-BLED. When bleed risk was lower than stroke risk, 151 respondents chose either DOACs (65%) or warfarin (35%); 15% added gastroprotection. When bleed risk was equal to stroke risk, 141 respondents chose DOACs (50%), warfarin (45%), or aspirin (5%); 40% added gastroprotection.
• Conclusion: In addition to BRT usage, pharmacists were judicious in their recommendation to add gastroprotection and would consider doing so if there was a specific indication. As more than 80% of extracranial bleeds are gastrointestinal bleeds and most BRTs are nonspecific for predicting these bleeds, randomized, prospective studies stratified by HAS-BLED and stroke risk scores are needed to provide further guidance on the efficacy and safety of oral anticoagulation therapy with or without gastroprotection.

Keywords: NVAF; gastroprotection; proton pump inhibitors; warfarin; oral anticoagulants.

Management of patients with nonvalvular atrial fibrillation (NVAF) with oral anticoagulation therapy (OACT) requires constant attention to maintain a balance between preventing strokes and minimizing bleeds. Several validated bleed risk tools (BRTs) available for use in NVAF patients include HAS-BLED, HEMORR₂HAGES, ATRIA, and mOBRI.^1,2 A high bleed risk score is not a contraindication to OACT, but, prior to and throughout therapy, bleed risk should be assessed and modifiable risk factors addressed.³ While intraluminal gastrointestinal (GI) bleeds are not considered a critical bleed site, they are a common complication of chronic OACT and can result in hemodynamic compromise and permanent discontinuation of therapy.^4,5 In 3233 patients with nonvariceal upper GI bleeds (2005-2016), the adjusted odds ratio of hospital admission, transfusion, and re-bleeding while on OACT (warfarin, heparin, or apixaban) was 3.48, 2.53, and 2.26, respectively.⁶ Addition of acid-suppressive therapy with a proton pump inhibitor (PPI) or histamine-2 receptor antagonist (H₂RA) in NVAF patients at increased risk for upper GI bleeds and receiving OACT may result in fewer bleeds.^7,8

Pharmacists play an integral part in managing patients on warfarin,^9-11 and data on their role in managing patients receiving direct oral anticoagulants (DOACs) are increasing.^12-16 Inpatient pharmacists actively participate in multidisciplinary collaborative teams and use clinical decision-support systems or enhanced monitoring to ensure safe prescribing of high-risk medications.^12,15,16 Pharmacist-managed, outpatient-based anticoagulation services in patients on warfarin were associated with lower rates of bleeding and thromboembolic events and lower health care utilization versus routine care.¹⁷ However, it is unclear how pharmacists manage patients who are candidates for OACT but who may be at increased risk for upper GI bleeds. Using a US-based survey, the investigators sought to determine pharmacists’ preferences in BRT usage and gastroprotection when bleed risk was lower than or equal to stroke risk.

Methods

This cross-sectional study was conducted after receiving approval by Nova Southeastern University’s Institutional Review Board. The survey consisted of 16 items divided into 4 domains: demographics, clinical experience, use of BRTs, and treatment preferences based on cases where bleed risk was lower than or equal to stroke risk (Figure 1). Queries were multiple choice and allowed for free-text input when “Other” was selected. Licensed pharmacists ≥ 18 years of age who routinely provided care to patients with NVAF were eligible to participate in the study. Participants who reported using a BRT (users) completed all study domains, while participants who reported not using a BRT (nonusers) completed domains 1 through 3 only.

An invitation containing the survey link was sent to the American College of Clinical Pharmacy ambulatory care (n = 2237) and cardiology (n = 1318) pharmacists listed in the organization’s Practice-based Research Networks. The survey was administered in the United States between April and June 2016 via Research Electronic Data Capture (REDCap) software, a secure Web application for building and managing online surveys designed to support data collection for research studies.¹⁸

Survey responses were downloaded, and data were analyzed using NCSS 2019 Statistical Software, LLC (Kaysville, UT). Descriptive statistics were calculated for all study variables. Demographic and clinical experience data for the group that used a BRT versus the group that did not were compared using Pearson’s chi-square, ANOVA, or the Cochran-Armitage test for trends. Logistic regression with hierarchical forward selection with switching was used to identify predictors of drug selection and use of gastroprotection.

Results

Of 230 respondents who completed the survey (response rate 6.5%), 165 (72%) used a BRT and 65 (28%) did not. No significant differences were found for age, gender, duration in clinical practice, the percentage of time spent in patient care, or practice specialty between users and nonusers (Table). The median age of users was 32 years; 68% were females; the median duration in clinical practice was 6 years; 75% of their time was spent in clinical practice; and clinical settings included ambulatory care, cardiology, and internal medicine. A significant difference was found for practice region between users versus nonusers (P = 0.014). Respondents who managed more than 200 NVAF patients per year used a BRT more often than those who managed fewer than 100 NVAF patients per year (P = 0.001).

Of those who used a BRT, 97% utilized the HAS-BLED tool (n = 160). The remainder used HEMORR₂HAGES (n = 3), ATRIA (n = 1), and mOBRI (n = 1). Reasons for choosing HAS-BLED included “familiarity/ease-of-use,” “preference by institution/clinical team,” and the fact that it was a “validated tool for NVAF.”

When bleed risk was lower than stroke risk, 151 of 165 users (92%) chose a treatment option (Figure 2). Of those, 65% chose a DOAC and 35% chose warfarin. Fourteen respondents chose “other” and explained that they “would initiate OACT after weighing patient factors and preferences.” When a DOAC was selected, 9% (n = 9) chose PPI co-therapy and 4% (n = 4) chose a H₂RA. When warfarin was selected, 13% (n = 7) chose PPI co-therapy and 4% (n = 2) chose a H₂RA. Respondents who chose gastroprotection did not provide reasons for doing so, but those who did not add it explained that they “would add gastroprotection only if patient is also on an NSAID or has a history of GI bleed” or cited “patient preference.” Specific to warfarin, some respondents would not add gastroprotection, as anticoagulation with warfarin is “easily reversed.”

When bleed risk was equal to stroke risk, 141 of 165 users (85%) chose a treatment option (Figure 3). Fifty percent chose DOACs, 45% chose warfarin, and 5% chose aspirin. Logistic regression analysis (outcome DOAC versus warfarin area-under-ROC curve, 0.67) showed that as the number of NVAF patients seen in 12 months increased, respondents were more likely to select a DOAC over warfarin (odds ratio, 1.7; 95% CI, 1.1-2.5). Therefore, for every 50-patient increase per year, the probability of recommending a DOAC increased 1.7-fold.

Of respondents who selected either a DOAC or warfarin, 38% (n = 50) also added gastroprotection (Figure 3). When a DOAC was selected, 34% (n = 24) favored PPI co-therapy and 7% (n = 5) chose a H₂RA. When warfarin was selected, 19% (n = 12) favored PPI co-therapy, while 13% (n = 8) chose a H₂RA. Rationale for choosing gastroprotection, regardless of OACT selection, included “stroke is more devastating, so if patient wants to continue treatment, but knew risks of bleeding were similar, would recommend gastroprotection to help minimize bleeding risk” and “patient-specific consideration.” Rationales for not choosing gastroprotection included “would add gastroprotection only if patient is on dual antiplatelet therapy or has another indication”; “in most patients, stroke risk outweighs bleed risk so no need for gastroprotection unless there is a stated reason”; “would use apixaban as has lowest bleeding rate of all DOACs in clinical trials”; and “gastroprotection has not been shown to be beneficial in large scale trials.” 

Eight respondents chose aspirin because it was “easy and relatively low cost.” Twenty-four respondents chose “other” and explained that the choice of OACT depended on patient preference after they had discussed stroke and bleed risk with the patient and/or determined the etiology driving bleed risk.

Discussion

This is the first national survey exploring US pharmacists’ preferences in BRT usage and treatment based on bleed risk. Pharmacists preferred the HAS-BLED tool and considered patient-specific factors and evidence-based data when weighing the risk-benefit of OACT with or without gastroprotective therapy.

Similar to our findings, where three-quarters of pharmacists used a BRT, a recent Medscape/American College of Cardiology (ACC) survey reported that 74% of cardiologists used a BRT (eg, HAS-BLED) always/most of the time or sometimes to assess a patient’s overall risk of bleeding prior to initiating DOAC therapy; 27% never or rarely used a bleed risk score before prescribing DOACs.¹⁹ Although reasons for BRT preference were not provided, they may be similar to those reported by our respondents (ie, familiarity/ease-of-use). In both surveys, rationales for not using a BRT were not obtained, but possible reasons include lack of confidence with bleed risk calculators,²⁰ inconsistent implementation of comprehensive assessments (stroke risk, bleed risk, and medication-related issues prior to decision-making),²¹ and nonspecific guideline recommendations.²²

More recently, a network meta-analysis found that HAS-BLED and HEMORR₂HAGES had modest but balanced sensitivity (defined as the ratio between the number of major bleeding events in high-risk stratification and the total number of bleeding events) and specificity (defined as the ratio between the number of nonmajor bleeding events in the low-risk population and total nonbleeding events) for predicting major bleeding events.^2,3 Several respondents did comment that, although HAS-BLED was imprecise and only studied with warfarin, it was necessary to identify bleed risk in a patient starting a high-risk medication, and that the ACC anticoagulation application uses HAS-BLED with CHA₂DS₂VASc along with clinical trial data to estimate stroke risk and bleed risk, with projected risk reduction (strokes) and risk increases (bleeds) expected with each treatment (www.acc.org/tools-and-practice-support/mobile-resources/features/anticoag-evaluator). The 2019 AHA/ACC/HRS atrial fibrillation guideline recommends that HAS-BLED scores be used to assess bleed risk in patients for whom anticoagulation is being considered, and that the need for and choice of OACT should be periodically reevaluated to reassess stroke and bleed risks.²³

Although more than 80% of extracranial bleeds are GI bleeds,²⁴ most BRTs are nonspecific for predicting GI bleeds. Indeed, one respondent used a spreadsheet with several BRTs to maximize treatment guidance for patients with multiple risk factors for strokes and bleeds. A comprehensive approach to determining factors that increase bleed risk should be adopted. These factors include age (HAS-BLED, HEMORR₂HAGES, mOBRI, ATRIA); anemia (mOBRI, HEMORR₂HAGES, ATRIA); hepatic/renal disease (HAS-BLED, HEMORR₂HAGES, ATRIA, mOBRI); concomitant medications/alcohol use, including NSAIDs, corticosteroids, and antiplatelet therapy (HAS-BLED, HEMORR₂HAGES); bleed history/rebleeding risk (HEMORR₂HAGES, HAS-BLED, ATRIA); and GI bleeds (mOBRI).^1,2 Additional risk factors for GI bleeds include being a tobacco smoker and/or being infected with Helicobacter pylori. A prospective cohort study that analyzed data from questionnaires completed by 99,359 individuals from the Copenhagen General Population Study reported that the multivariable adjusted hazard ratio for current smokers versus never smokers was 2.20 (95% CI, 1.84-2.62) for GI bleeds.²⁵ Presence of H pylori should be investigated, with a subsequent eradication regimen implemented, as patients with warfarin-associated upper GI bleeds who were H pylori-positive had lower HAS-BLED scores versus those who were negative.²⁶

When bleed risk was lower than stroke risk (eg, HAS-BLED < 3, CHA₂DS₂VASc ≥ 1), respondents appropriately initiated therapy with an OAC (predominantly apixaban); a small proportion also added gastroprotection. If the patient did not have any other GI bleed risk factors (eg, a previous GI bleed or on chronic antiplatelet or NSAID therapy), the choice of OACT depended on the attributes of each OAC and patient preference.²⁷ Selection of warfarin was appropriate if cost, formulary restrictions, and availability of an inexpensive reversal agent were important concerns to patients and/or their health care providers. Rivaroxaban was selected because of its once-daily dosing and low risk for GI bleeding.

The recently published ARISTOPHANES study provides evidence that apixaban is an appropriate choice in patients with a HAS-BLED score < 3. In this retrospective observational study, more than 70% of patients received standard doses of DOACs (apixaban 5 mg, dabigatran 150 mg, or rivaroxaban 20 mg) and about 20% had a bleeding history, about 30% were on PPIs, less than 25% were on NSAIDs, and about 40% had a HAS-BLED score < 3. The study found that apixaban was more effective (reduced rates of ischemic or hemorrhagic strokes/systemic embolism) and safer (reduced rates of major GI bleed or intracranial bleed) than warfarin.²⁸ Dabigatran and rivaroxaban were also more effective than warfarin for stroke prevention and had a lower risk for major intracranial bleed risk; while the risk of major GI bleed was similar between dabigatran and warfarin, major GI bleed risk was higher for rivaroxaban. When compared with each other, the 3 DOACs were effective at stroke prevention, with apixaban more effective than dabigatran and rivaroxaban; similar efficacy was noted for dabigatran versus rivaroxaban. Apixaban was associated with fewer GI bleeds versus dabigatran and rivaroxaban, but with similar intracranial bleed risks; dabigatran was associated with fewer GI bleeds but similar intracranial bleed risks versus rivaroxaban.²⁸ Efficacy and safety findings from a subgroup analysis based on HAS-BLED scores < 3 and ≥ 3 were generally consistent with the main results.

When bleed risk was equal to stroke risk, the difficulty was determining how OACT in a patient at high stroke risk (CHA₂DS₂VASc score ≥ 2) and high bleed risk (HAS-BLED score ≥ 3) should be managed. Eight respondents chose aspirin and added gastroprotection with either a PPI or H₂RA; however, currently, aspirin is not recommended as the sole antithrombotic for patients with NVAF.²³ With the OACT, an interesting finding was that as the number of patients seen in 12 months increased, pharmacists were almost twice as likely to select a DOAC over warfarin. Moreover, pharmacists were judicious in their recommendation to add gastroprotection, and would consider doing so if there was a specific indication. At the time of our survey, several studies described DOAC-associated GI bleeds,^29-31 but data on the effectiveness of acid-suppressive therapy, specifically with PPIs, in the prevention of upper GI bleeds were sparse.^4,7,32 Respondents most likely were familiar with GI bleed risk factors and prevention strategies from various guidelines published between 2009 and 2010, which did not include DOACs.^33-35

Another important finding was pharmacists’ uncertainty as to the effectiveness of PPIs in preventing GI bleeds in combination with DOACs. The data are conflicting. A meta-analysis of older studies (2007-2015) showed that PPIs (but not H₂RAs) reduced the risk of upper GI bleeds in patients on warfarin but not for dabigatran.³⁶ A retrospective cohort study of Medicare beneficiaries on OACTs (2011-2015) showed the adjusted incidence of hospitalization for upper GI bleeds in patients on PPI co-therapy was significantly lower compared with patients not on PPI co-therapy (76 versus 115 per 10,000 person-years, respectively).⁸ Apixaban without PPI co-therapy was associated with the lowest risk of upper GI bleed hospitalizations (73/10,000 person-years), and PPI co-therapy further reduced this risk (49/10,000 person-years). Warfarin without PPI co-therapy was associated with the next lowest risk (113/10,000 person-years), followed by dabigatran (120/10,000 person-years) and rivaroxaban (144/10,000 person-years). PPI co-therapy significantly reduced the risk of upper GI bleed hospitalizations with all OACTs, but the incidence of upper GI bleed hospitalizations with rivaroxaban was significantly greater than with the other OACTs.⁸ Therefore, if there are concerns about the safety of PPIs,^37-39 or the patient is unable to tolerate a PPI, then apixaban may be the most appropriate DOAC for a patient with high bleed risk. Notably, a 2020 review of data from the PINNACLE registry (average age, 75-77 years; 31% on PPIs) found that the relative GI bleed safety advantage of apixaban and dabigatran versus warfarin was attenuated in patients ≥ 75 years.⁴⁰ Last, since the risk for lower GI bleeds is not reduced by PPIs,⁴¹ consideration of their use should be accompanied by an assessment to detect bleeds (eg, low hemoglobin/hematocrit, presence of bright red blood, hematochezia/melena, fecal occult testing), with prompt management as necessary.⁵

Limitations

Limitations of our survey included an overall low response rate, which can generate a biased sample if respondents are systematically different from nonrespondents. In addition, to maintain simplicity and reduce respondents’ time commitment, our survey did not include actual CHA₂DS₂VASc stroke risk scores, HAS-BLED bleed risk scores, or specific GI bleed risk factors when querying pharmacists about treatment options based on bleed risk. The addition of these variables would have improved the robustness of the data.

Conclusion

In addition to applying BRTs in the management of NVAF patients, pharmacists considered patient-specific variables, prescriber preferences, and evidence-based guidance when recommending OACT with or without gastroprotection. To avoid suboptimal patient management, busy pharmacists should be granted time to attend continuing education programs describing optimal OACT selection and formulation of individualized, evidence-based plans to address modifiable risk factors for bleeding, including the appropriate use of gastroprotection. Randomized, prospective, long-term studies stratified by HAS-BLED and CHA₂DS₂VASc scores are needed to further clarify efficacy, safety, and cost-effectiveness of OACT, with and without PPIs, in patients who may be at risk for upper GI bleeds.

Acknowledgments: The authors thank Robin J. Jacobs, PhD, MSW, MS, MPH, Patrick C. Hardigan, PhD, Steven Brettler, PharmD, MPH, Maria-Isabel A. Cabral, PharmD, and Reginald Gyapong, PharmD, for their participation in this project. The authors also sincerely thank Fabio Franco, BS Computer Science, who organized the database to enable efficient data management.

Corresponding author: Devada Singh-Franco, PharmD, CDE, Nova Southeastern University College of Pharmacy, 3200 S University Drive, Fort Lauderdale, FL 33328; [email protected]

Disclosures: None.

Funding: The study was supported by Nova Southeastern University’s Health Professions Division Internal Research Grant.

From Nova Southeastern University College of Pharmacy, Fort Lauderdale, FL.

Abstract

• Objective: To determine pharmacists’ preferences in bleed risk tool (BRT) usage and gastroprotection when bleed risk was lower than or equal to stroke risk in patients with nonvalvular atrial fibrillation and who were candidates for oral anticoagulation therapy (warfarin or direct oral anticoagulants [DOACs]).
• Methods: A survey consisting of 4 domains (demographics, clinical experience, BRT usage, and treatment preferences based on cases where bleed risk was lower than or equal to stroke risk) was developed. The anonymous survey was disseminated via REDCap software to members of the American College of Clinical Pharmacy ambulatory care and cardiology Practice-based Research Networks. Descriptive statistics were calculated for all study variables and inferential statistics were employed as necessary.
• Results: Of 165 BRT users, 97% preferred HAS-BLED. When bleed risk was lower than stroke risk, 151 respondents chose either DOACs (65%) or warfarin (35%); 15% added gastroprotection. When bleed risk was equal to stroke risk, 141 respondents chose DOACs (50%), warfarin (45%), or aspirin (5%); 40% added gastroprotection.
• Conclusion: In addition to BRT usage, pharmacists were judicious in their recommendation to add gastroprotection and would consider doing so if there was a specific indication. As more than 80% of extracranial bleeds are gastrointestinal bleeds and most BRTs are nonspecific for predicting these bleeds, randomized, prospective studies stratified by HAS-BLED and stroke risk scores are needed to provide further guidance on the efficacy and safety of oral anticoagulation therapy with or without gastroprotection.

Keywords: NVAF; gastroprotection; proton pump inhibitors; warfarin; oral anticoagulants.

Management of patients with nonvalvular atrial fibrillation (NVAF) with oral anticoagulation therapy (OACT) requires constant attention to maintain a balance between preventing strokes and minimizing bleeds. Several validated bleed risk tools (BRTs) available for use in NVAF patients include HAS-BLED, HEMORR₂HAGES, ATRIA, and mOBRI.^1,2 A high bleed risk score is not a contraindication to OACT, but, prior to and throughout therapy, bleed risk should be assessed and modifiable risk factors addressed.³ While intraluminal gastrointestinal (GI) bleeds are not considered a critical bleed site, they are a common complication of chronic OACT and can result in hemodynamic compromise and permanent discontinuation of therapy.^4,5 In 3233 patients with nonvariceal upper GI bleeds (2005-2016), the adjusted odds ratio of hospital admission, transfusion, and re-bleeding while on OACT (warfarin, heparin, or apixaban) was 3.48, 2.53, and 2.26, respectively.⁶ Addition of acid-suppressive therapy with a proton pump inhibitor (PPI) or histamine-2 receptor antagonist (H₂RA) in NVAF patients at increased risk for upper GI bleeds and receiving OACT may result in fewer bleeds.^7,8

Pharmacists play an integral part in managing patients on warfarin,^9-11 and data on their role in managing patients receiving direct oral anticoagulants (DOACs) are increasing.^12-16 Inpatient pharmacists actively participate in multidisciplinary collaborative teams and use clinical decision-support systems or enhanced monitoring to ensure safe prescribing of high-risk medications.^12,15,16 Pharmacist-managed, outpatient-based anticoagulation services in patients on warfarin were associated with lower rates of bleeding and thromboembolic events and lower health care utilization versus routine care.¹⁷ However, it is unclear how pharmacists manage patients who are candidates for OACT but who may be at increased risk for upper GI bleeds. Using a US-based survey, the investigators sought to determine pharmacists’ preferences in BRT usage and gastroprotection when bleed risk was lower than or equal to stroke risk.

Methods

This cross-sectional study was conducted after receiving approval by Nova Southeastern University’s Institutional Review Board. The survey consisted of 16 items divided into 4 domains: demographics, clinical experience, use of BRTs, and treatment preferences based on cases where bleed risk was lower than or equal to stroke risk (Figure 1). Queries were multiple choice and allowed for free-text input when “Other” was selected. Licensed pharmacists ≥ 18 years of age who routinely provided care to patients with NVAF were eligible to participate in the study. Participants who reported using a BRT (users) completed all study domains, while participants who reported not using a BRT (nonusers) completed domains 1 through 3 only.

An invitation containing the survey link was sent to the American College of Clinical Pharmacy ambulatory care (n = 2237) and cardiology (n = 1318) pharmacists listed in the organization’s Practice-based Research Networks. The survey was administered in the United States between April and June 2016 via Research Electronic Data Capture (REDCap) software, a secure Web application for building and managing online surveys designed to support data collection for research studies.¹⁸

Survey responses were downloaded, and data were analyzed using NCSS 2019 Statistical Software, LLC (Kaysville, UT). Descriptive statistics were calculated for all study variables. Demographic and clinical experience data for the group that used a BRT versus the group that did not were compared using Pearson’s chi-square, ANOVA, or the Cochran-Armitage test for trends. Logistic regression with hierarchical forward selection with switching was used to identify predictors of drug selection and use of gastroprotection.

Results

Of 230 respondents who completed the survey (response rate 6.5%), 165 (72%) used a BRT and 65 (28%) did not. No significant differences were found for age, gender, duration in clinical practice, the percentage of time spent in patient care, or practice specialty between users and nonusers (Table). The median age of users was 32 years; 68% were females; the median duration in clinical practice was 6 years; 75% of their time was spent in clinical practice; and clinical settings included ambulatory care, cardiology, and internal medicine. A significant difference was found for practice region between users versus nonusers (P = 0.014). Respondents who managed more than 200 NVAF patients per year used a BRT more often than those who managed fewer than 100 NVAF patients per year (P = 0.001).

Of those who used a BRT, 97% utilized the HAS-BLED tool (n = 160). The remainder used HEMORR₂HAGES (n = 3), ATRIA (n = 1), and mOBRI (n = 1). Reasons for choosing HAS-BLED included “familiarity/ease-of-use,” “preference by institution/clinical team,” and the fact that it was a “validated tool for NVAF.”

When bleed risk was lower than stroke risk, 151 of 165 users (92%) chose a treatment option (Figure 2). Of those, 65% chose a DOAC and 35% chose warfarin. Fourteen respondents chose “other” and explained that they “would initiate OACT after weighing patient factors and preferences.” When a DOAC was selected, 9% (n = 9) chose PPI co-therapy and 4% (n = 4) chose a H₂RA. When warfarin was selected, 13% (n = 7) chose PPI co-therapy and 4% (n = 2) chose a H₂RA. Respondents who chose gastroprotection did not provide reasons for doing so, but those who did not add it explained that they “would add gastroprotection only if patient is also on an NSAID or has a history of GI bleed” or cited “patient preference.” Specific to warfarin, some respondents would not add gastroprotection, as anticoagulation with warfarin is “easily reversed.”

When bleed risk was equal to stroke risk, 141 of 165 users (85%) chose a treatment option (Figure 3). Fifty percent chose DOACs, 45% chose warfarin, and 5% chose aspirin. Logistic regression analysis (outcome DOAC versus warfarin area-under-ROC curve, 0.67) showed that as the number of NVAF patients seen in 12 months increased, respondents were more likely to select a DOAC over warfarin (odds ratio, 1.7; 95% CI, 1.1-2.5). Therefore, for every 50-patient increase per year, the probability of recommending a DOAC increased 1.7-fold.

Of respondents who selected either a DOAC or warfarin, 38% (n = 50) also added gastroprotection (Figure 3). When a DOAC was selected, 34% (n = 24) favored PPI co-therapy and 7% (n = 5) chose a H₂RA. When warfarin was selected, 19% (n = 12) favored PPI co-therapy, while 13% (n = 8) chose a H₂RA. Rationale for choosing gastroprotection, regardless of OACT selection, included “stroke is more devastating, so if patient wants to continue treatment, but knew risks of bleeding were similar, would recommend gastroprotection to help minimize bleeding risk” and “patient-specific consideration.” Rationales for not choosing gastroprotection included “would add gastroprotection only if patient is on dual antiplatelet therapy or has another indication”; “in most patients, stroke risk outweighs bleed risk so no need for gastroprotection unless there is a stated reason”; “would use apixaban as has lowest bleeding rate of all DOACs in clinical trials”; and “gastroprotection has not been shown to be beneficial in large scale trials.” 

Eight respondents chose aspirin because it was “easy and relatively low cost.” Twenty-four respondents chose “other” and explained that the choice of OACT depended on patient preference after they had discussed stroke and bleed risk with the patient and/or determined the etiology driving bleed risk.

Discussion

This is the first national survey exploring US pharmacists’ preferences in BRT usage and treatment based on bleed risk. Pharmacists preferred the HAS-BLED tool and considered patient-specific factors and evidence-based data when weighing the risk-benefit of OACT with or without gastroprotective therapy.

Similar to our findings, where three-quarters of pharmacists used a BRT, a recent Medscape/American College of Cardiology (ACC) survey reported that 74% of cardiologists used a BRT (eg, HAS-BLED) always/most of the time or sometimes to assess a patient’s overall risk of bleeding prior to initiating DOAC therapy; 27% never or rarely used a bleed risk score before prescribing DOACs.¹⁹ Although reasons for BRT preference were not provided, they may be similar to those reported by our respondents (ie, familiarity/ease-of-use). In both surveys, rationales for not using a BRT were not obtained, but possible reasons include lack of confidence with bleed risk calculators,²⁰ inconsistent implementation of comprehensive assessments (stroke risk, bleed risk, and medication-related issues prior to decision-making),²¹ and nonspecific guideline recommendations.²²

More recently, a network meta-analysis found that HAS-BLED and HEMORR₂HAGES had modest but balanced sensitivity (defined as the ratio between the number of major bleeding events in high-risk stratification and the total number of bleeding events) and specificity (defined as the ratio between the number of nonmajor bleeding events in the low-risk population and total nonbleeding events) for predicting major bleeding events.^2,3 Several respondents did comment that, although HAS-BLED was imprecise and only studied with warfarin, it was necessary to identify bleed risk in a patient starting a high-risk medication, and that the ACC anticoagulation application uses HAS-BLED with CHA₂DS₂VASc along with clinical trial data to estimate stroke risk and bleed risk, with projected risk reduction (strokes) and risk increases (bleeds) expected with each treatment (www.acc.org/tools-and-practice-support/mobile-resources/features/anticoag-evaluator). The 2019 AHA/ACC/HRS atrial fibrillation guideline recommends that HAS-BLED scores be used to assess bleed risk in patients for whom anticoagulation is being considered, and that the need for and choice of OACT should be periodically reevaluated to reassess stroke and bleed risks.²³

Although more than 80% of extracranial bleeds are GI bleeds,²⁴ most BRTs are nonspecific for predicting GI bleeds. Indeed, one respondent used a spreadsheet with several BRTs to maximize treatment guidance for patients with multiple risk factors for strokes and bleeds. A comprehensive approach to determining factors that increase bleed risk should be adopted. These factors include age (HAS-BLED, HEMORR₂HAGES, mOBRI, ATRIA); anemia (mOBRI, HEMORR₂HAGES, ATRIA); hepatic/renal disease (HAS-BLED, HEMORR₂HAGES, ATRIA, mOBRI); concomitant medications/alcohol use, including NSAIDs, corticosteroids, and antiplatelet therapy (HAS-BLED, HEMORR₂HAGES); bleed history/rebleeding risk (HEMORR₂HAGES, HAS-BLED, ATRIA); and GI bleeds (mOBRI).^1,2 Additional risk factors for GI bleeds include being a tobacco smoker and/or being infected with Helicobacter pylori. A prospective cohort study that analyzed data from questionnaires completed by 99,359 individuals from the Copenhagen General Population Study reported that the multivariable adjusted hazard ratio for current smokers versus never smokers was 2.20 (95% CI, 1.84-2.62) for GI bleeds.²⁵ Presence of H pylori should be investigated, with a subsequent eradication regimen implemented, as patients with warfarin-associated upper GI bleeds who were H pylori-positive had lower HAS-BLED scores versus those who were negative.²⁶

When bleed risk was lower than stroke risk (eg, HAS-BLED < 3, CHA₂DS₂VASc ≥ 1), respondents appropriately initiated therapy with an OAC (predominantly apixaban); a small proportion also added gastroprotection. If the patient did not have any other GI bleed risk factors (eg, a previous GI bleed or on chronic antiplatelet or NSAID therapy), the choice of OACT depended on the attributes of each OAC and patient preference.²⁷ Selection of warfarin was appropriate if cost, formulary restrictions, and availability of an inexpensive reversal agent were important concerns to patients and/or their health care providers. Rivaroxaban was selected because of its once-daily dosing and low risk for GI bleeding.

The recently published ARISTOPHANES study provides evidence that apixaban is an appropriate choice in patients with a HAS-BLED score < 3. In this retrospective observational study, more than 70% of patients received standard doses of DOACs (apixaban 5 mg, dabigatran 150 mg, or rivaroxaban 20 mg) and about 20% had a bleeding history, about 30% were on PPIs, less than 25% were on NSAIDs, and about 40% had a HAS-BLED score < 3. The study found that apixaban was more effective (reduced rates of ischemic or hemorrhagic strokes/systemic embolism) and safer (reduced rates of major GI bleed or intracranial bleed) than warfarin.²⁸ Dabigatran and rivaroxaban were also more effective than warfarin for stroke prevention and had a lower risk for major intracranial bleed risk; while the risk of major GI bleed was similar between dabigatran and warfarin, major GI bleed risk was higher for rivaroxaban. When compared with each other, the 3 DOACs were effective at stroke prevention, with apixaban more effective than dabigatran and rivaroxaban; similar efficacy was noted for dabigatran versus rivaroxaban. Apixaban was associated with fewer GI bleeds versus dabigatran and rivaroxaban, but with similar intracranial bleed risks; dabigatran was associated with fewer GI bleeds but similar intracranial bleed risks versus rivaroxaban.²⁸ Efficacy and safety findings from a subgroup analysis based on HAS-BLED scores < 3 and ≥ 3 were generally consistent with the main results.

When bleed risk was equal to stroke risk, the difficulty was determining how OACT in a patient at high stroke risk (CHA₂DS₂VASc score ≥ 2) and high bleed risk (HAS-BLED score ≥ 3) should be managed. Eight respondents chose aspirin and added gastroprotection with either a PPI or H₂RA; however, currently, aspirin is not recommended as the sole antithrombotic for patients with NVAF.²³ With the OACT, an interesting finding was that as the number of patients seen in 12 months increased, pharmacists were almost twice as likely to select a DOAC over warfarin. Moreover, pharmacists were judicious in their recommendation to add gastroprotection, and would consider doing so if there was a specific indication. At the time of our survey, several studies described DOAC-associated GI bleeds,^29-31 but data on the effectiveness of acid-suppressive therapy, specifically with PPIs, in the prevention of upper GI bleeds were sparse.^4,7,32 Respondents most likely were familiar with GI bleed risk factors and prevention strategies from various guidelines published between 2009 and 2010, which did not include DOACs.^33-35

Another important finding was pharmacists’ uncertainty as to the effectiveness of PPIs in preventing GI bleeds in combination with DOACs. The data are conflicting. A meta-analysis of older studies (2007-2015) showed that PPIs (but not H₂RAs) reduced the risk of upper GI bleeds in patients on warfarin but not for dabigatran.³⁶ A retrospective cohort study of Medicare beneficiaries on OACTs (2011-2015) showed the adjusted incidence of hospitalization for upper GI bleeds in patients on PPI co-therapy was significantly lower compared with patients not on PPI co-therapy (76 versus 115 per 10,000 person-years, respectively).⁸ Apixaban without PPI co-therapy was associated with the lowest risk of upper GI bleed hospitalizations (73/10,000 person-years), and PPI co-therapy further reduced this risk (49/10,000 person-years). Warfarin without PPI co-therapy was associated with the next lowest risk (113/10,000 person-years), followed by dabigatran (120/10,000 person-years) and rivaroxaban (144/10,000 person-years). PPI co-therapy significantly reduced the risk of upper GI bleed hospitalizations with all OACTs, but the incidence of upper GI bleed hospitalizations with rivaroxaban was significantly greater than with the other OACTs.⁸ Therefore, if there are concerns about the safety of PPIs,^37-39 or the patient is unable to tolerate a PPI, then apixaban may be the most appropriate DOAC for a patient with high bleed risk. Notably, a 2020 review of data from the PINNACLE registry (average age, 75-77 years; 31% on PPIs) found that the relative GI bleed safety advantage of apixaban and dabigatran versus warfarin was attenuated in patients ≥ 75 years.⁴⁰ Last, since the risk for lower GI bleeds is not reduced by PPIs,⁴¹ consideration of their use should be accompanied by an assessment to detect bleeds (eg, low hemoglobin/hematocrit, presence of bright red blood, hematochezia/melena, fecal occult testing), with prompt management as necessary.⁵

Limitations

Limitations of our survey included an overall low response rate, which can generate a biased sample if respondents are systematically different from nonrespondents. In addition, to maintain simplicity and reduce respondents’ time commitment, our survey did not include actual CHA₂DS₂VASc stroke risk scores, HAS-BLED bleed risk scores, or specific GI bleed risk factors when querying pharmacists about treatment options based on bleed risk. The addition of these variables would have improved the robustness of the data.

Conclusion

In addition to applying BRTs in the management of NVAF patients, pharmacists considered patient-specific variables, prescriber preferences, and evidence-based guidance when recommending OACT with or without gastroprotection. To avoid suboptimal patient management, busy pharmacists should be granted time to attend continuing education programs describing optimal OACT selection and formulation of individualized, evidence-based plans to address modifiable risk factors for bleeding, including the appropriate use of gastroprotection. Randomized, prospective, long-term studies stratified by HAS-BLED and CHA₂DS₂VASc scores are needed to further clarify efficacy, safety, and cost-effectiveness of OACT, with and without PPIs, in patients who may be at risk for upper GI bleeds.

Acknowledgments: The authors thank Robin J. Jacobs, PhD, MSW, MS, MPH, Patrick C. Hardigan, PhD, Steven Brettler, PharmD, MPH, Maria-Isabel A. Cabral, PharmD, and Reginald Gyapong, PharmD, for their participation in this project. The authors also sincerely thank Fabio Franco, BS Computer Science, who organized the database to enable efficient data management.

Corresponding author: Devada Singh-Franco, PharmD, CDE, Nova Southeastern University College of Pharmacy, 3200 S University Drive, Fort Lauderdale, FL 33328; [email protected]

Disclosures: None.

Funding: The study was supported by Nova Southeastern University’s Health Professions Division Internal Research Grant.

From Nova Southeastern University College of Pharmacy, Fort Lauderdale, FL.

Abstract

• Objective: To determine pharmacists’ preferences in bleed risk tool (BRT) usage and gastroprotection when bleed risk was lower than or equal to stroke risk in patients with nonvalvular atrial fibrillation and who were candidates for oral anticoagulation therapy (warfarin or direct oral anticoagulants [DOACs]).
• Methods: A survey consisting of 4 domains (demographics, clinical experience, BRT usage, and treatment preferences based on cases where bleed risk was lower than or equal to stroke risk) was developed. The anonymous survey was disseminated via REDCap software to members of the American College of Clinical Pharmacy ambulatory care and cardiology Practice-based Research Networks. Descriptive statistics were calculated for all study variables and inferential statistics were employed as necessary.
• Results: Of 165 BRT users, 97% preferred HAS-BLED. When bleed risk was lower than stroke risk, 151 respondents chose either DOACs (65%) or warfarin (35%); 15% added gastroprotection. When bleed risk was equal to stroke risk, 141 respondents chose DOACs (50%), warfarin (45%), or aspirin (5%); 40% added gastroprotection.
• Conclusion: In addition to BRT usage, pharmacists were judicious in their recommendation to add gastroprotection and would consider doing so if there was a specific indication. As more than 80% of extracranial bleeds are gastrointestinal bleeds and most BRTs are nonspecific for predicting these bleeds, randomized, prospective studies stratified by HAS-BLED and stroke risk scores are needed to provide further guidance on the efficacy and safety of oral anticoagulation therapy with or without gastroprotection.

Keywords: NVAF; gastroprotection; proton pump inhibitors; warfarin; oral anticoagulants.

Management of patients with nonvalvular atrial fibrillation (NVAF) with oral anticoagulation therapy (OACT) requires constant attention to maintain a balance between preventing strokes and minimizing bleeds. Several validated bleed risk tools (BRTs) available for use in NVAF patients include HAS-BLED, HEMORR₂HAGES, ATRIA, and mOBRI.^1,2 A high bleed risk score is not a contraindication to OACT, but, prior to and throughout therapy, bleed risk should be assessed and modifiable risk factors addressed.³ While intraluminal gastrointestinal (GI) bleeds are not considered a critical bleed site, they are a common complication of chronic OACT and can result in hemodynamic compromise and permanent discontinuation of therapy.^4,5 In 3233 patients with nonvariceal upper GI bleeds (2005-2016), the adjusted odds ratio of hospital admission, transfusion, and re-bleeding while on OACT (warfarin, heparin, or apixaban) was 3.48, 2.53, and 2.26, respectively.⁶ Addition of acid-suppressive therapy with a proton pump inhibitor (PPI) or histamine-2 receptor antagonist (H₂RA) in NVAF patients at increased risk for upper GI bleeds and receiving OACT may result in fewer bleeds.^7,8

Pharmacists play an integral part in managing patients on warfarin,^9-11 and data on their role in managing patients receiving direct oral anticoagulants (DOACs) are increasing.^12-16 Inpatient pharmacists actively participate in multidisciplinary collaborative teams and use clinical decision-support systems or enhanced monitoring to ensure safe prescribing of high-risk medications.^12,15,16 Pharmacist-managed, outpatient-based anticoagulation services in patients on warfarin were associated with lower rates of bleeding and thromboembolic events and lower health care utilization versus routine care.¹⁷ However, it is unclear how pharmacists manage patients who are candidates for OACT but who may be at increased risk for upper GI bleeds. Using a US-based survey, the investigators sought to determine pharmacists’ preferences in BRT usage and gastroprotection when bleed risk was lower than or equal to stroke risk.

Methods

This cross-sectional study was conducted after receiving approval by Nova Southeastern University’s Institutional Review Board. The survey consisted of 16 items divided into 4 domains: demographics, clinical experience, use of BRTs, and treatment preferences based on cases where bleed risk was lower than or equal to stroke risk (Figure 1). Queries were multiple choice and allowed for free-text input when “Other” was selected. Licensed pharmacists ≥ 18 years of age who routinely provided care to patients with NVAF were eligible to participate in the study. Participants who reported using a BRT (users) completed all study domains, while participants who reported not using a BRT (nonusers) completed domains 1 through 3 only.

An invitation containing the survey link was sent to the American College of Clinical Pharmacy ambulatory care (n = 2237) and cardiology (n = 1318) pharmacists listed in the organization’s Practice-based Research Networks. The survey was administered in the United States between April and June 2016 via Research Electronic Data Capture (REDCap) software, a secure Web application for building and managing online surveys designed to support data collection for research studies.¹⁸

Survey responses were downloaded, and data were analyzed using NCSS 2019 Statistical Software, LLC (Kaysville, UT). Descriptive statistics were calculated for all study variables. Demographic and clinical experience data for the group that used a BRT versus the group that did not were compared using Pearson’s chi-square, ANOVA, or the Cochran-Armitage test for trends. Logistic regression with hierarchical forward selection with switching was used to identify predictors of drug selection and use of gastroprotection.

Results

Of 230 respondents who completed the survey (response rate 6.5%), 165 (72%) used a BRT and 65 (28%) did not. No significant differences were found for age, gender, duration in clinical practice, the percentage of time spent in patient care, or practice specialty between users and nonusers (Table). The median age of users was 32 years; 68% were females; the median duration in clinical practice was 6 years; 75% of their time was spent in clinical practice; and clinical settings included ambulatory care, cardiology, and internal medicine. A significant difference was found for practice region between users versus nonusers (P = 0.014). Respondents who managed more than 200 NVAF patients per year used a BRT more often than those who managed fewer than 100 NVAF patients per year (P = 0.001).

Of those who used a BRT, 97% utilized the HAS-BLED tool (n = 160). The remainder used HEMORR₂HAGES (n = 3), ATRIA (n = 1), and mOBRI (n = 1). Reasons for choosing HAS-BLED included “familiarity/ease-of-use,” “preference by institution/clinical team,” and the fact that it was a “validated tool for NVAF.”

When bleed risk was lower than stroke risk, 151 of 165 users (92%) chose a treatment option (Figure 2). Of those, 65% chose a DOAC and 35% chose warfarin. Fourteen respondents chose “other” and explained that they “would initiate OACT after weighing patient factors and preferences.” When a DOAC was selected, 9% (n = 9) chose PPI co-therapy and 4% (n = 4) chose a H₂RA. When warfarin was selected, 13% (n = 7) chose PPI co-therapy and 4% (n = 2) chose a H₂RA. Respondents who chose gastroprotection did not provide reasons for doing so, but those who did not add it explained that they “would add gastroprotection only if patient is also on an NSAID or has a history of GI bleed” or cited “patient preference.” Specific to warfarin, some respondents would not add gastroprotection, as anticoagulation with warfarin is “easily reversed.”

When bleed risk was equal to stroke risk, 141 of 165 users (85%) chose a treatment option (Figure 3). Fifty percent chose DOACs, 45% chose warfarin, and 5% chose aspirin. Logistic regression analysis (outcome DOAC versus warfarin area-under-ROC curve, 0.67) showed that as the number of NVAF patients seen in 12 months increased, respondents were more likely to select a DOAC over warfarin (odds ratio, 1.7; 95% CI, 1.1-2.5). Therefore, for every 50-patient increase per year, the probability of recommending a DOAC increased 1.7-fold.

Of respondents who selected either a DOAC or warfarin, 38% (n = 50) also added gastroprotection (Figure 3). When a DOAC was selected, 34% (n = 24) favored PPI co-therapy and 7% (n = 5) chose a H₂RA. When warfarin was selected, 19% (n = 12) favored PPI co-therapy, while 13% (n = 8) chose a H₂RA. Rationale for choosing gastroprotection, regardless of OACT selection, included “stroke is more devastating, so if patient wants to continue treatment, but knew risks of bleeding were similar, would recommend gastroprotection to help minimize bleeding risk” and “patient-specific consideration.” Rationales for not choosing gastroprotection included “would add gastroprotection only if patient is on dual antiplatelet therapy or has another indication”; “in most patients, stroke risk outweighs bleed risk so no need for gastroprotection unless there is a stated reason”; “would use apixaban as has lowest bleeding rate of all DOACs in clinical trials”; and “gastroprotection has not been shown to be beneficial in large scale trials.” 

Eight respondents chose aspirin because it was “easy and relatively low cost.” Twenty-four respondents chose “other” and explained that the choice of OACT depended on patient preference after they had discussed stroke and bleed risk with the patient and/or determined the etiology driving bleed risk.

Discussion

This is the first national survey exploring US pharmacists’ preferences in BRT usage and treatment based on bleed risk. Pharmacists preferred the HAS-BLED tool and considered patient-specific factors and evidence-based data when weighing the risk-benefit of OACT with or without gastroprotective therapy.

Similar to our findings, where three-quarters of pharmacists used a BRT, a recent Medscape/American College of Cardiology (ACC) survey reported that 74% of cardiologists used a BRT (eg, HAS-BLED) always/most of the time or sometimes to assess a patient’s overall risk of bleeding prior to initiating DOAC therapy; 27% never or rarely used a bleed risk score before prescribing DOACs.¹⁹ Although reasons for BRT preference were not provided, they may be similar to those reported by our respondents (ie, familiarity/ease-of-use). In both surveys, rationales for not using a BRT were not obtained, but possible reasons include lack of confidence with bleed risk calculators,²⁰ inconsistent implementation of comprehensive assessments (stroke risk, bleed risk, and medication-related issues prior to decision-making),²¹ and nonspecific guideline recommendations.²²

More recently, a network meta-analysis found that HAS-BLED and HEMORR₂HAGES had modest but balanced sensitivity (defined as the ratio between the number of major bleeding events in high-risk stratification and the total number of bleeding events) and specificity (defined as the ratio between the number of nonmajor bleeding events in the low-risk population and total nonbleeding events) for predicting major bleeding events.^2,3 Several respondents did comment that, although HAS-BLED was imprecise and only studied with warfarin, it was necessary to identify bleed risk in a patient starting a high-risk medication, and that the ACC anticoagulation application uses HAS-BLED with CHA₂DS₂VASc along with clinical trial data to estimate stroke risk and bleed risk, with projected risk reduction (strokes) and risk increases (bleeds) expected with each treatment (www.acc.org/tools-and-practice-support/mobile-resources/features/anticoag-evaluator). The 2019 AHA/ACC/HRS atrial fibrillation guideline recommends that HAS-BLED scores be used to assess bleed risk in patients for whom anticoagulation is being considered, and that the need for and choice of OACT should be periodically reevaluated to reassess stroke and bleed risks.²³

Although more than 80% of extracranial bleeds are GI bleeds,²⁴ most BRTs are nonspecific for predicting GI bleeds. Indeed, one respondent used a spreadsheet with several BRTs to maximize treatment guidance for patients with multiple risk factors for strokes and bleeds. A comprehensive approach to determining factors that increase bleed risk should be adopted. These factors include age (HAS-BLED, HEMORR₂HAGES, mOBRI, ATRIA); anemia (mOBRI, HEMORR₂HAGES, ATRIA); hepatic/renal disease (HAS-BLED, HEMORR₂HAGES, ATRIA, mOBRI); concomitant medications/alcohol use, including NSAIDs, corticosteroids, and antiplatelet therapy (HAS-BLED, HEMORR₂HAGES); bleed history/rebleeding risk (HEMORR₂HAGES, HAS-BLED, ATRIA); and GI bleeds (mOBRI).^1,2 Additional risk factors for GI bleeds include being a tobacco smoker and/or being infected with Helicobacter pylori. A prospective cohort study that analyzed data from questionnaires completed by 99,359 individuals from the Copenhagen General Population Study reported that the multivariable adjusted hazard ratio for current smokers versus never smokers was 2.20 (95% CI, 1.84-2.62) for GI bleeds.²⁵ Presence of H pylori should be investigated, with a subsequent eradication regimen implemented, as patients with warfarin-associated upper GI bleeds who were H pylori-positive had lower HAS-BLED scores versus those who were negative.²⁶

When bleed risk was lower than stroke risk (eg, HAS-BLED < 3, CHA₂DS₂VASc ≥ 1), respondents appropriately initiated therapy with an OAC (predominantly apixaban); a small proportion also added gastroprotection. If the patient did not have any other GI bleed risk factors (eg, a previous GI bleed or on chronic antiplatelet or NSAID therapy), the choice of OACT depended on the attributes of each OAC and patient preference.²⁷ Selection of warfarin was appropriate if cost, formulary restrictions, and availability of an inexpensive reversal agent were important concerns to patients and/or their health care providers. Rivaroxaban was selected because of its once-daily dosing and low risk for GI bleeding.

The recently published ARISTOPHANES study provides evidence that apixaban is an appropriate choice in patients with a HAS-BLED score < 3. In this retrospective observational study, more than 70% of patients received standard doses of DOACs (apixaban 5 mg, dabigatran 150 mg, or rivaroxaban 20 mg) and about 20% had a bleeding history, about 30% were on PPIs, less than 25% were on NSAIDs, and about 40% had a HAS-BLED score < 3. The study found that apixaban was more effective (reduced rates of ischemic or hemorrhagic strokes/systemic embolism) and safer (reduced rates of major GI bleed or intracranial bleed) than warfarin.²⁸ Dabigatran and rivaroxaban were also more effective than warfarin for stroke prevention and had a lower risk for major intracranial bleed risk; while the risk of major GI bleed was similar between dabigatran and warfarin, major GI bleed risk was higher for rivaroxaban. When compared with each other, the 3 DOACs were effective at stroke prevention, with apixaban more effective than dabigatran and rivaroxaban; similar efficacy was noted for dabigatran versus rivaroxaban. Apixaban was associated with fewer GI bleeds versus dabigatran and rivaroxaban, but with similar intracranial bleed risks; dabigatran was associated with fewer GI bleeds but similar intracranial bleed risks versus rivaroxaban.²⁸ Efficacy and safety findings from a subgroup analysis based on HAS-BLED scores < 3 and ≥ 3 were generally consistent with the main results.

When bleed risk was equal to stroke risk, the difficulty was determining how OACT in a patient at high stroke risk (CHA₂DS₂VASc score ≥ 2) and high bleed risk (HAS-BLED score ≥ 3) should be managed. Eight respondents chose aspirin and added gastroprotection with either a PPI or H₂RA; however, currently, aspirin is not recommended as the sole antithrombotic for patients with NVAF.²³ With the OACT, an interesting finding was that as the number of patients seen in 12 months increased, pharmacists were almost twice as likely to select a DOAC over warfarin. Moreover, pharmacists were judicious in their recommendation to add gastroprotection, and would consider doing so if there was a specific indication. At the time of our survey, several studies described DOAC-associated GI bleeds,^29-31 but data on the effectiveness of acid-suppressive therapy, specifically with PPIs, in the prevention of upper GI bleeds were sparse.^4,7,32 Respondents most likely were familiar with GI bleed risk factors and prevention strategies from various guidelines published between 2009 and 2010, which did not include DOACs.^33-35

Another important finding was pharmacists’ uncertainty as to the effectiveness of PPIs in preventing GI bleeds in combination with DOACs. The data are conflicting. A meta-analysis of older studies (2007-2015) showed that PPIs (but not H₂RAs) reduced the risk of upper GI bleeds in patients on warfarin but not for dabigatran.³⁶ A retrospective cohort study of Medicare beneficiaries on OACTs (2011-2015) showed the adjusted incidence of hospitalization for upper GI bleeds in patients on PPI co-therapy was significantly lower compared with patients not on PPI co-therapy (76 versus 115 per 10,000 person-years, respectively).⁸ Apixaban without PPI co-therapy was associated with the lowest risk of upper GI bleed hospitalizations (73/10,000 person-years), and PPI co-therapy further reduced this risk (49/10,000 person-years). Warfarin without PPI co-therapy was associated with the next lowest risk (113/10,000 person-years), followed by dabigatran (120/10,000 person-years) and rivaroxaban (144/10,000 person-years). PPI co-therapy significantly reduced the risk of upper GI bleed hospitalizations with all OACTs, but the incidence of upper GI bleed hospitalizations with rivaroxaban was significantly greater than with the other OACTs.⁸ Therefore, if there are concerns about the safety of PPIs,^37-39 or the patient is unable to tolerate a PPI, then apixaban may be the most appropriate DOAC for a patient with high bleed risk. Notably, a 2020 review of data from the PINNACLE registry (average age, 75-77 years; 31% on PPIs) found that the relative GI bleed safety advantage of apixaban and dabigatran versus warfarin was attenuated in patients ≥ 75 years.⁴⁰ Last, since the risk for lower GI bleeds is not reduced by PPIs,⁴¹ consideration of their use should be accompanied by an assessment to detect bleeds (eg, low hemoglobin/hematocrit, presence of bright red blood, hematochezia/melena, fecal occult testing), with prompt management as necessary.⁵

Limitations

Limitations of our survey included an overall low response rate, which can generate a biased sample if respondents are systematically different from nonrespondents. In addition, to maintain simplicity and reduce respondents’ time commitment, our survey did not include actual CHA₂DS₂VASc stroke risk scores, HAS-BLED bleed risk scores, or specific GI bleed risk factors when querying pharmacists about treatment options based on bleed risk. The addition of these variables would have improved the robustness of the data.

Conclusion

In addition to applying BRTs in the management of NVAF patients, pharmacists considered patient-specific variables, prescriber preferences, and evidence-based guidance when recommending OACT with or without gastroprotection. To avoid suboptimal patient management, busy pharmacists should be granted time to attend continuing education programs describing optimal OACT selection and formulation of individualized, evidence-based plans to address modifiable risk factors for bleeding, including the appropriate use of gastroprotection. Randomized, prospective, long-term studies stratified by HAS-BLED and CHA₂DS₂VASc scores are needed to further clarify efficacy, safety, and cost-effectiveness of OACT, with and without PPIs, in patients who may be at risk for upper GI bleeds.

Acknowledgments: The authors thank Robin J. Jacobs, PhD, MSW, MS, MPH, Patrick C. Hardigan, PhD, Steven Brettler, PharmD, MPH, Maria-Isabel A. Cabral, PharmD, and Reginald Gyapong, PharmD, for their participation in this project. The authors also sincerely thank Fabio Franco, BS Computer Science, who organized the database to enable efficient data management.

Corresponding author: Devada Singh-Franco, PharmD, CDE, Nova Southeastern University College of Pharmacy, 3200 S University Drive, Fort Lauderdale, FL 33328; [email protected]

Disclosures: None.

Funding: The study was supported by Nova Southeastern University’s Health Professions Division Internal Research Grant.

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

 

 

Commentary

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

 

 

Commentary

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

 

 

Commentary

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

In the midst of a pandemic, the biggest changes health care leaders reported in their medical practices last year revolved around staffing, cost and revenue, practice transformation, information technology, and operations, according to a December 2020 report from the Medical Group Management Association.

The report was assembled from the results of weekly Stat polls by MGMA, which consists of 15,000 group practices representing more than 350,000 physicians. During the course of the year, more than 4,800 practice leaders were surveyed, but the individual polls had far fewer respondents.

The 2020 data represents snapshots from different points in the developing public health crisis. Still, much of what practices experienced earlier in the pandemic continues to apply, and it’s likely to persist this year as long as the coronavirus spreads and its toll deepens.

One top-line conclusion of the report: the economic pain felt by practices has resulted in layoffs, furloughs, and/or reduced compensation for providers and staff.

In the May 19 weekly survey, 82% of respondents said some or all of their providers’ compensation had been affected by the crisis. About 62% said every provider had been affected. Provider compensation was cut in several ways, including reduced hours and salaries, reduced or eliminated bonuses, and lower allowances for continuing medical education.

About 61% of health care leaders said in the June 26 poll that their own compensation had decreased.

In the following week’s survey, one in three managers said their organization had reduced staff compensation. Nearly all of the respondents in this category predicted the salary reductions would be temporary.

As of March 17, early in the pandemic, 40% of health care leaders said they were experiencing staff shortages. An April 21 poll found that 53% of health care leaders were taking steps to address their providers’ and staffers’ mental health.

“The mental and emotional toll on everyone continues to be a concern, as public health authorities continue to report alarming numbers of new [COVID-19] cases, hospitalizations, and deaths,” MGMA commented.
 

Telehealth and remote monitoring

Nearly all of the health care leaders surveyed on March 31 reported that their practices had expanded telehealth access because of COVID-19. The percentage of patient visits handled remotely had dropped substantially by the fall, according to a Harvard University/Commonwealth Fund/Phreesia survey. Still, it remains significantly higher than it was before the pandemic.

“At the end of 2020, telemedicine continues to play a vital role in everyday practice operations and long-term planning,” the MGMA report said. One indication of this, the association said, is that health care leaders are recognizing new best practices in specialty telemedicine, such as pediatrics and ob.gyn.

According to an April 28 poll, the top three coding/billing challenges for telehealth and telephone visits amid COVID-19 were inconsistent payer rules, pay parity and accuracy, and documentation of virtual visits.

While the Centers for Medicare & Medicaid Services has loosened its regulations to allow reimbursement of telehealth in all locations and at the same level as in-person visits, most of those changes will not last beyond the public health crisis without new legislation.

More health care leaders are considering the use of remote patient monitoring, MGMA said, but only 21% of practices offered such services as of Sept. 15. The report drew a connection between these plans and the current challenge of deferred care.

In the July 21 poll, 87% of health care leaders reported that safety concerns were the top reason that patients deferred care amid COVID-19. The MGMA report quoted JaeLynn Williams, CEO of Air Methods, which provides helicopter ambulance services, as saying that many people are staying home even when they face life-threatening conditions such as chest pain, drug symptoms, inflamed appendix, and gallbladder pain.
 

Operational issues

Overall, MGMA said, practices that have taken a financial risk have done better during the pandemic than fee-for-service practices because their monthly capitation revenue has continued unabated. In contrast, “most groups’ struggles to sustain visits and procedures meant less revenue and lower compensation,” the report said.

In the August 18 survey, one in three health care leaders reported their practices were changing their operational metrics and how often they looked at those measures because of the pandemic. “Practice managers are asking for dashboard data in weeks instead of months to measure the drop in charges and forecast the resulting change in collections,” MGMA noted. “The type of data practice managers are asking for has also changed.”

Among the new metrics that practices are interested in, according to an MGMA article, are measures that track telehealth visits, the productivity of staff working at home, and the number of ancillary services and procedures that new patients might need based on historical data.

Nearly all health care leaders surveyed on Aug. 11 said the cost of obtaining personal protective equipment had increased during 2020. MGMA said it expects this situation to worsen if the pandemic lasts through the summer of 2021.

While everyone is talking about the botched launch of the COVID-19 vaccination campaign, there were also problems with flu vaccination in 2020. In the Sept. 25 poll, 34% of health care leaders reported their practices were experiencing delays in getting the flu vaccine.
 

Looking ahead

Looking further ahead, the report recommended that practices make plans to boost staff morale by restoring bonuses.

In addition, MGMA suggested that physician groups reassess their space needs. “The equation is simple – fewer nonclinical staff members at your facility means you should repurpose that office space or consider finding a better fit for your new real estate needs in 2021.”

Finally, MGMA noted that the practices expanding rather than contracting their business are those increasing their value-based revenues by taking on more risk. For those groups, “growing the patient panel can help [them] seek better rates in contract negotiations.”

A version of this article first appeared on Medscape.com.

In the midst of a pandemic, the biggest changes health care leaders reported in their medical practices last year revolved around staffing, cost and revenue, practice transformation, information technology, and operations, according to a December 2020 report from the Medical Group Management Association.

The report was assembled from the results of weekly Stat polls by MGMA, which consists of 15,000 group practices representing more than 350,000 physicians. During the course of the year, more than 4,800 practice leaders were surveyed, but the individual polls had far fewer respondents.

The 2020 data represents snapshots from different points in the developing public health crisis. Still, much of what practices experienced earlier in the pandemic continues to apply, and it’s likely to persist this year as long as the coronavirus spreads and its toll deepens.

One top-line conclusion of the report: the economic pain felt by practices has resulted in layoffs, furloughs, and/or reduced compensation for providers and staff.

In the May 19 weekly survey, 82% of respondents said some or all of their providers’ compensation had been affected by the crisis. About 62% said every provider had been affected. Provider compensation was cut in several ways, including reduced hours and salaries, reduced or eliminated bonuses, and lower allowances for continuing medical education.

About 61% of health care leaders said in the June 26 poll that their own compensation had decreased.

In the following week’s survey, one in three managers said their organization had reduced staff compensation. Nearly all of the respondents in this category predicted the salary reductions would be temporary.

As of March 17, early in the pandemic, 40% of health care leaders said they were experiencing staff shortages. An April 21 poll found that 53% of health care leaders were taking steps to address their providers’ and staffers’ mental health.

“The mental and emotional toll on everyone continues to be a concern, as public health authorities continue to report alarming numbers of new [COVID-19] cases, hospitalizations, and deaths,” MGMA commented.
 

Telehealth and remote monitoring

Nearly all of the health care leaders surveyed on March 31 reported that their practices had expanded telehealth access because of COVID-19. The percentage of patient visits handled remotely had dropped substantially by the fall, according to a Harvard University/Commonwealth Fund/Phreesia survey. Still, it remains significantly higher than it was before the pandemic.

“At the end of 2020, telemedicine continues to play a vital role in everyday practice operations and long-term planning,” the MGMA report said. One indication of this, the association said, is that health care leaders are recognizing new best practices in specialty telemedicine, such as pediatrics and ob.gyn.

According to an April 28 poll, the top three coding/billing challenges for telehealth and telephone visits amid COVID-19 were inconsistent payer rules, pay parity and accuracy, and documentation of virtual visits.

While the Centers for Medicare & Medicaid Services has loosened its regulations to allow reimbursement of telehealth in all locations and at the same level as in-person visits, most of those changes will not last beyond the public health crisis without new legislation.

More health care leaders are considering the use of remote patient monitoring, MGMA said, but only 21% of practices offered such services as of Sept. 15. The report drew a connection between these plans and the current challenge of deferred care.

In the July 21 poll, 87% of health care leaders reported that safety concerns were the top reason that patients deferred care amid COVID-19. The MGMA report quoted JaeLynn Williams, CEO of Air Methods, which provides helicopter ambulance services, as saying that many people are staying home even when they face life-threatening conditions such as chest pain, drug symptoms, inflamed appendix, and gallbladder pain.
 

Operational issues

Overall, MGMA said, practices that have taken a financial risk have done better during the pandemic than fee-for-service practices because their monthly capitation revenue has continued unabated. In contrast, “most groups’ struggles to sustain visits and procedures meant less revenue and lower compensation,” the report said.

In the August 18 survey, one in three health care leaders reported their practices were changing their operational metrics and how often they looked at those measures because of the pandemic. “Practice managers are asking for dashboard data in weeks instead of months to measure the drop in charges and forecast the resulting change in collections,” MGMA noted. “The type of data practice managers are asking for has also changed.”

Among the new metrics that practices are interested in, according to an MGMA article, are measures that track telehealth visits, the productivity of staff working at home, and the number of ancillary services and procedures that new patients might need based on historical data.

Nearly all health care leaders surveyed on Aug. 11 said the cost of obtaining personal protective equipment had increased during 2020. MGMA said it expects this situation to worsen if the pandemic lasts through the summer of 2021.

While everyone is talking about the botched launch of the COVID-19 vaccination campaign, there were also problems with flu vaccination in 2020. In the Sept. 25 poll, 34% of health care leaders reported their practices were experiencing delays in getting the flu vaccine.
 

Looking ahead

Looking further ahead, the report recommended that practices make plans to boost staff morale by restoring bonuses.

In addition, MGMA suggested that physician groups reassess their space needs. “The equation is simple – fewer nonclinical staff members at your facility means you should repurpose that office space or consider finding a better fit for your new real estate needs in 2021.”

Finally, MGMA noted that the practices expanding rather than contracting their business are those increasing their value-based revenues by taking on more risk. For those groups, “growing the patient panel can help [them] seek better rates in contract negotiations.”

A version of this article first appeared on Medscape.com.

In the midst of a pandemic, the biggest changes health care leaders reported in their medical practices last year revolved around staffing, cost and revenue, practice transformation, information technology, and operations, according to a December 2020 report from the Medical Group Management Association.

The report was assembled from the results of weekly Stat polls by MGMA, which consists of 15,000 group practices representing more than 350,000 physicians. During the course of the year, more than 4,800 practice leaders were surveyed, but the individual polls had far fewer respondents.

The 2020 data represents snapshots from different points in the developing public health crisis. Still, much of what practices experienced earlier in the pandemic continues to apply, and it’s likely to persist this year as long as the coronavirus spreads and its toll deepens.

One top-line conclusion of the report: the economic pain felt by practices has resulted in layoffs, furloughs, and/or reduced compensation for providers and staff.

In the May 19 weekly survey, 82% of respondents said some or all of their providers’ compensation had been affected by the crisis. About 62% said every provider had been affected. Provider compensation was cut in several ways, including reduced hours and salaries, reduced or eliminated bonuses, and lower allowances for continuing medical education.

About 61% of health care leaders said in the June 26 poll that their own compensation had decreased.

In the following week’s survey, one in three managers said their organization had reduced staff compensation. Nearly all of the respondents in this category predicted the salary reductions would be temporary.

As of March 17, early in the pandemic, 40% of health care leaders said they were experiencing staff shortages. An April 21 poll found that 53% of health care leaders were taking steps to address their providers’ and staffers’ mental health.

“The mental and emotional toll on everyone continues to be a concern, as public health authorities continue to report alarming numbers of new [COVID-19] cases, hospitalizations, and deaths,” MGMA commented.
 

Telehealth and remote monitoring

Nearly all of the health care leaders surveyed on March 31 reported that their practices had expanded telehealth access because of COVID-19. The percentage of patient visits handled remotely had dropped substantially by the fall, according to a Harvard University/Commonwealth Fund/Phreesia survey. Still, it remains significantly higher than it was before the pandemic.

“At the end of 2020, telemedicine continues to play a vital role in everyday practice operations and long-term planning,” the MGMA report said. One indication of this, the association said, is that health care leaders are recognizing new best practices in specialty telemedicine, such as pediatrics and ob.gyn.

According to an April 28 poll, the top three coding/billing challenges for telehealth and telephone visits amid COVID-19 were inconsistent payer rules, pay parity and accuracy, and documentation of virtual visits.

While the Centers for Medicare & Medicaid Services has loosened its regulations to allow reimbursement of telehealth in all locations and at the same level as in-person visits, most of those changes will not last beyond the public health crisis without new legislation.

More health care leaders are considering the use of remote patient monitoring, MGMA said, but only 21% of practices offered such services as of Sept. 15. The report drew a connection between these plans and the current challenge of deferred care.

In the July 21 poll, 87% of health care leaders reported that safety concerns were the top reason that patients deferred care amid COVID-19. The MGMA report quoted JaeLynn Williams, CEO of Air Methods, which provides helicopter ambulance services, as saying that many people are staying home even when they face life-threatening conditions such as chest pain, drug symptoms, inflamed appendix, and gallbladder pain.
 

Operational issues

Overall, MGMA said, practices that have taken a financial risk have done better during the pandemic than fee-for-service practices because their monthly capitation revenue has continued unabated. In contrast, “most groups’ struggles to sustain visits and procedures meant less revenue and lower compensation,” the report said.

In the August 18 survey, one in three health care leaders reported their practices were changing their operational metrics and how often they looked at those measures because of the pandemic. “Practice managers are asking for dashboard data in weeks instead of months to measure the drop in charges and forecast the resulting change in collections,” MGMA noted. “The type of data practice managers are asking for has also changed.”

Among the new metrics that practices are interested in, according to an MGMA article, are measures that track telehealth visits, the productivity of staff working at home, and the number of ancillary services and procedures that new patients might need based on historical data.

Nearly all health care leaders surveyed on Aug. 11 said the cost of obtaining personal protective equipment had increased during 2020. MGMA said it expects this situation to worsen if the pandemic lasts through the summer of 2021.

While everyone is talking about the botched launch of the COVID-19 vaccination campaign, there were also problems with flu vaccination in 2020. In the Sept. 25 poll, 34% of health care leaders reported their practices were experiencing delays in getting the flu vaccine.
 

Looking ahead

Looking further ahead, the report recommended that practices make plans to boost staff morale by restoring bonuses.

In addition, MGMA suggested that physician groups reassess their space needs. “The equation is simple – fewer nonclinical staff members at your facility means you should repurpose that office space or consider finding a better fit for your new real estate needs in 2021.”

Finally, MGMA noted that the practices expanding rather than contracting their business are those increasing their value-based revenues by taking on more risk. For those groups, “growing the patient panel can help [them] seek better rates in contract negotiations.”

A version of this article first appeared on Medscape.com.

Primary care physicians (PCPs) generate only a small part of the $75 billion to $100 billion wasted every year on low-value care, according to a brief report published online Jan. 18 in Annals of Internal Medicine.

However, one expert said there are better ways to curb low-value care than focusing on which specialties are guilty of the practice.

Analyzing a 20% random sample of Medicare Part B claims, Aaron Baum, PhD, with the Icahn School of Medicine at Mount Sinai, New York, and colleagues found that the services primary care physicians performed or ordered made up on average 8.3% of the low-value care their patients received (interquartile range, 3.9%-15.1%; 95th percentile, 35.6%) and their referrals made up 15.4% (IQR, 6.3%-26.4%; 95th percentile, 44.6%).

By specialty, cardiology had the worst record with 27% of all spending on low-value services ($1.8 billion) attributed to that specialty. Yet, of the 25 highest-spending specialties in the report, 12 of them were associated with 1% or less than 1% each of all low-value spending, indicating the waste was widely distributed.

Dr. Baum said in an interview that though there are some PCPs guilty of high spending on low-value services, overall, most primary care physicians’ low-value services add up to only 0.3% of Part B spending. He noted that Part B spending is about one-third of all Medicare spending.

Primary care is often thought to be at the core of care management and spending and PCPs are often seen as the gatekeepers, but this analysis suggests that efforts to make big differences in curtailing low-value spending might be more effective elsewhere.

“There’s only so much spending you can reduce by changing primary care physicians’ services that they directly perform,” Dr. Baum said.
 

Low-value care is costly, can be harmful

Mark Fendrick, MD, director of the University of Michigan’s Center for Value-Based Insurance Design in Ann Arbor, said in an interview that the report adds confirmation to previous research that has consistently shown low-value care is “extremely common, very costly, and provided by primary care providers and specialists alike.” He noted that it can also be harmful.

“The math is simple,” he said. “If we want to improve coverage and lower patient costs for essential services like visits, diagnostic tests, and drugs, we have to reduce spending on those services that do not make Americans any healthier.”

The study ranked 31 clinical services judged to be low value by physician societies, Medicare and clinical guidelines, and their use among beneficiaries enrolled between 2007 and 2014. Here’s how the top six low-value services compare.

Dr. Fendrick said a weakness of the paper is the years of the data (2007-2014). Some of the criteria around low-value care have changed since then. The age that a prostate-specific antigen test becomes low-value is now 70 years, for instance, instead of 75. He added that some of the figures attributed to non-PCP providers appear out of date.

Dr. Fendrick said, “I understand that there are Medicare patients who end up at a gastroenterologist or surgeon’s office to get colorectal cancer screening, but it would be very hard for me to believe that half of stress tests and over half of colon cancer screening over [age] 85 [years] and half of PSA for people over 75 did not have some type of referring clinicians involved. I certainly don’t think that would be the case in 2020-2021.”

Dr. Baum said those years were the latest years available for the data points needed for this analysis, but he and his colleagues were working to update the data for future publication.

Dr. Fendrick said not much has changed in recent years in terms of waste on low-value care, even with campaigns such as Choosing Wisely dedicated to identifying low-value services or procedures in each specialty.

“I believe there’s not a particular group of clinicians one way or the other who are actually doing any better now than they were 7 years ago,” he said. He would rather focus less on which specialties are associated with the most low-value care and more on the underlying policies that encourage low-value care.

“If you’re going to get paid for doing a stress test and get paid nothing or significantly less if you don’t, the incentives are in the wrong direction,” he said.

Dr. Fendrick said the pandemic era provides an opportunity to eliminate low-value care because use of those services has dropped drastically as resources have been diverted to COVID-19 patients and many services have been delayed or canceled.

He said he has been pushing an approach that providers should be paid more after the pandemic “to do the things we want them to do.”

As an example, he said, instead of paying $886 million on colonoscopies for people over the age of 85, “why don’t we put a policy in place that would make it better for patients by lowering cost sharing and better for providers by paying them more to do the service on the people who need it as opposed to the people who don’t?”

The research was funded by the American Board of Family Medicine Foundation. Dr. Baum and a coauthor reported receiving personal fees from American Board of Family Medicine Foundation during the conduct of the study. Another coauthor reported receiving personal fees from Collective Health, HealthRight 360, PLOS Medicine, and the New England Journal of Medicine, outside the submitted work. Dr. Fendrick disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Primary care physicians (PCPs) generate only a small part of the $75 billion to $100 billion wasted every year on low-value care, according to a brief report published online Jan. 18 in Annals of Internal Medicine.

However, one expert said there are better ways to curb low-value care than focusing on which specialties are guilty of the practice.

Analyzing a 20% random sample of Medicare Part B claims, Aaron Baum, PhD, with the Icahn School of Medicine at Mount Sinai, New York, and colleagues found that the services primary care physicians performed or ordered made up on average 8.3% of the low-value care their patients received (interquartile range, 3.9%-15.1%; 95th percentile, 35.6%) and their referrals made up 15.4% (IQR, 6.3%-26.4%; 95th percentile, 44.6%).

By specialty, cardiology had the worst record with 27% of all spending on low-value services ($1.8 billion) attributed to that specialty. Yet, of the 25 highest-spending specialties in the report, 12 of them were associated with 1% or less than 1% each of all low-value spending, indicating the waste was widely distributed.

Dr. Baum said in an interview that though there are some PCPs guilty of high spending on low-value services, overall, most primary care physicians’ low-value services add up to only 0.3% of Part B spending. He noted that Part B spending is about one-third of all Medicare spending.

Primary care is often thought to be at the core of care management and spending and PCPs are often seen as the gatekeepers, but this analysis suggests that efforts to make big differences in curtailing low-value spending might be more effective elsewhere.

“There’s only so much spending you can reduce by changing primary care physicians’ services that they directly perform,” Dr. Baum said.
 

Low-value care is costly, can be harmful

Mark Fendrick, MD, director of the University of Michigan’s Center for Value-Based Insurance Design in Ann Arbor, said in an interview that the report adds confirmation to previous research that has consistently shown low-value care is “extremely common, very costly, and provided by primary care providers and specialists alike.” He noted that it can also be harmful.

“The math is simple,” he said. “If we want to improve coverage and lower patient costs for essential services like visits, diagnostic tests, and drugs, we have to reduce spending on those services that do not make Americans any healthier.”

The study ranked 31 clinical services judged to be low value by physician societies, Medicare and clinical guidelines, and their use among beneficiaries enrolled between 2007 and 2014. Here’s how the top six low-value services compare.

Dr. Fendrick said a weakness of the paper is the years of the data (2007-2014). Some of the criteria around low-value care have changed since then. The age that a prostate-specific antigen test becomes low-value is now 70 years, for instance, instead of 75. He added that some of the figures attributed to non-PCP providers appear out of date.

Dr. Fendrick said, “I understand that there are Medicare patients who end up at a gastroenterologist or surgeon’s office to get colorectal cancer screening, but it would be very hard for me to believe that half of stress tests and over half of colon cancer screening over [age] 85 [years] and half of PSA for people over 75 did not have some type of referring clinicians involved. I certainly don’t think that would be the case in 2020-2021.”

Dr. Baum said those years were the latest years available for the data points needed for this analysis, but he and his colleagues were working to update the data for future publication.

Dr. Fendrick said not much has changed in recent years in terms of waste on low-value care, even with campaigns such as Choosing Wisely dedicated to identifying low-value services or procedures in each specialty.

“I believe there’s not a particular group of clinicians one way or the other who are actually doing any better now than they were 7 years ago,” he said. He would rather focus less on which specialties are associated with the most low-value care and more on the underlying policies that encourage low-value care.

“If you’re going to get paid for doing a stress test and get paid nothing or significantly less if you don’t, the incentives are in the wrong direction,” he said.

Dr. Fendrick said the pandemic era provides an opportunity to eliminate low-value care because use of those services has dropped drastically as resources have been diverted to COVID-19 patients and many services have been delayed or canceled.

He said he has been pushing an approach that providers should be paid more after the pandemic “to do the things we want them to do.”

As an example, he said, instead of paying $886 million on colonoscopies for people over the age of 85, “why don’t we put a policy in place that would make it better for patients by lowering cost sharing and better for providers by paying them more to do the service on the people who need it as opposed to the people who don’t?”

The research was funded by the American Board of Family Medicine Foundation. Dr. Baum and a coauthor reported receiving personal fees from American Board of Family Medicine Foundation during the conduct of the study. Another coauthor reported receiving personal fees from Collective Health, HealthRight 360, PLOS Medicine, and the New England Journal of Medicine, outside the submitted work. Dr. Fendrick disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Primary care physicians (PCPs) generate only a small part of the $75 billion to $100 billion wasted every year on low-value care, according to a brief report published online Jan. 18 in Annals of Internal Medicine.

However, one expert said there are better ways to curb low-value care than focusing on which specialties are guilty of the practice.

Analyzing a 20% random sample of Medicare Part B claims, Aaron Baum, PhD, with the Icahn School of Medicine at Mount Sinai, New York, and colleagues found that the services primary care physicians performed or ordered made up on average 8.3% of the low-value care their patients received (interquartile range, 3.9%-15.1%; 95th percentile, 35.6%) and their referrals made up 15.4% (IQR, 6.3%-26.4%; 95th percentile, 44.6%).

By specialty, cardiology had the worst record with 27% of all spending on low-value services ($1.8 billion) attributed to that specialty. Yet, of the 25 highest-spending specialties in the report, 12 of them were associated with 1% or less than 1% each of all low-value spending, indicating the waste was widely distributed.

Dr. Baum said in an interview that though there are some PCPs guilty of high spending on low-value services, overall, most primary care physicians’ low-value services add up to only 0.3% of Part B spending. He noted that Part B spending is about one-third of all Medicare spending.

Primary care is often thought to be at the core of care management and spending and PCPs are often seen as the gatekeepers, but this analysis suggests that efforts to make big differences in curtailing low-value spending might be more effective elsewhere.

“There’s only so much spending you can reduce by changing primary care physicians’ services that they directly perform,” Dr. Baum said.
 

Low-value care is costly, can be harmful

Mark Fendrick, MD, director of the University of Michigan’s Center for Value-Based Insurance Design in Ann Arbor, said in an interview that the report adds confirmation to previous research that has consistently shown low-value care is “extremely common, very costly, and provided by primary care providers and specialists alike.” He noted that it can also be harmful.

“The math is simple,” he said. “If we want to improve coverage and lower patient costs for essential services like visits, diagnostic tests, and drugs, we have to reduce spending on those services that do not make Americans any healthier.”

The study ranked 31 clinical services judged to be low value by physician societies, Medicare and clinical guidelines, and their use among beneficiaries enrolled between 2007 and 2014. Here’s how the top six low-value services compare.

Dr. Fendrick said a weakness of the paper is the years of the data (2007-2014). Some of the criteria around low-value care have changed since then. The age that a prostate-specific antigen test becomes low-value is now 70 years, for instance, instead of 75. He added that some of the figures attributed to non-PCP providers appear out of date.

Dr. Fendrick said, “I understand that there are Medicare patients who end up at a gastroenterologist or surgeon’s office to get colorectal cancer screening, but it would be very hard for me to believe that half of stress tests and over half of colon cancer screening over [age] 85 [years] and half of PSA for people over 75 did not have some type of referring clinicians involved. I certainly don’t think that would be the case in 2020-2021.”

Dr. Baum said those years were the latest years available for the data points needed for this analysis, but he and his colleagues were working to update the data for future publication.

Dr. Fendrick said not much has changed in recent years in terms of waste on low-value care, even with campaigns such as Choosing Wisely dedicated to identifying low-value services or procedures in each specialty.

“I believe there’s not a particular group of clinicians one way or the other who are actually doing any better now than they were 7 years ago,” he said. He would rather focus less on which specialties are associated with the most low-value care and more on the underlying policies that encourage low-value care.

“If you’re going to get paid for doing a stress test and get paid nothing or significantly less if you don’t, the incentives are in the wrong direction,” he said.

Dr. Fendrick said the pandemic era provides an opportunity to eliminate low-value care because use of those services has dropped drastically as resources have been diverted to COVID-19 patients and many services have been delayed or canceled.

He said he has been pushing an approach that providers should be paid more after the pandemic “to do the things we want them to do.”

As an example, he said, instead of paying $886 million on colonoscopies for people over the age of 85, “why don’t we put a policy in place that would make it better for patients by lowering cost sharing and better for providers by paying them more to do the service on the people who need it as opposed to the people who don’t?”

The research was funded by the American Board of Family Medicine Foundation. Dr. Baum and a coauthor reported receiving personal fees from American Board of Family Medicine Foundation during the conduct of the study. Another coauthor reported receiving personal fees from Collective Health, HealthRight 360, PLOS Medicine, and the New England Journal of Medicine, outside the submitted work. Dr. Fendrick disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

The pandemic has isolated our patients to an unprecedented degree, forcing them to find other ways to communicate with us, including email. I wondered how private offices were handling the marked increase in email communications since the pandemic began; so I queried several physician blogs and social media pages.

Responses varied all over the map. Some refuse the medium entirely. “I politely say that I don’t practice dermatology via email,” said one. “Please schedule a teledermatology appointment and I’d be happy to help.”

Others are ambivalent: “I do email with some patients who have complex situations or quick questions, but if it gets out of hand then I let them know someone will call to make an appointment.” Another office treats them as a one-way street: “We set up one account to receive patients’ emails, but we tell them clearly that we don’t respond ... my staff or I call them back.”

Still others have assimilated it completely. “Patients email through the portal and my MA routes [them] to me. I answer questions and the MA responds ... staff loves it because it’s so much faster than the phone.”

A 1998 study in JAMA was more scientifically designed, but basically reached the same conclusion. The authors found “a striking lack of consensus” on how to deal with patient emails: 50% responded to them, but 31% of responders refused to give advice without seeing the patient, while 59% offered a diagnosis, and a third of that group went on to provide specific advice about therapy. In response to a follow-up questionnaire, 28% said that they tended not to answer any patient emails, 24% said they usually replied with a standard message, and 24% said they answer each request individually. The authors concluded that “standards for physician response to unsolicited patient emails are needed.”

Indeed, my own unscientific survey suggests that, more than 20 years later, there is still nothing resembling a consensus on this issue. In the interim, several groups, including the American Medical Informatics Association and the American Medical Association have proposed standards, but none have been generally accepted. Until that happens, it seems prudent for each individual practice to adopt its own guidelines. For ideas, take a look at the proposals from the groups I mentioned, plus any others you can find. When you’re done, consider running your list past your attorney to make sure you haven’t forgotten anything, and that there are no unique requirements in your state.

Your guidelines may be very simple (if you decide never to answer any queries) or very complex, depending on your situation and personal philosophy. But all guidelines should cover such issues as authentication of correspondents, informed consent, licensing jurisdiction (if you receive emails from states in which you are not licensed), and of course, confidentiality.

Contrary to popular belief, HIPAA does not prohibit email communication with patients, nor require that it be encrypted. The HIPAA website specifically says: “Patients may initiate communications with a provider using email. If this situation occurs, the health care provider can assume (unless the patient has explicitly stated otherwise) that e-mail communications are acceptable to the individual.”

Still, if you are not comfortable with unencrypted communication, encryption software can be added to your practice’s email system. Proofpoint, Tumbleweed, Zix, and many other vendors sell encryption packages. (As always, I have no financial interest in any product or enterprise mentioned in this column.)

Another option is web-based messaging: Patients enter your website and send a message using an electronic template that you design. A designated staffer will be notified by regular email when messages are received, and can post a reply on a page that can only be accessed by the patient. Besides enhancing privacy and security, you can state your guidelines in plain English to preclude any misunderstanding of what you will and will not address online.

Web-based messaging services can be freestanding or incorporated into existing secure websites. Medfusion and klara are among the leading vendors of secure messaging services.

The important thing is to make a firm decision on how you want to deal with emails, and stick with that method. And follow your guidelines.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at [email protected].

The pandemic has isolated our patients to an unprecedented degree, forcing them to find other ways to communicate with us, including email. I wondered how private offices were handling the marked increase in email communications since the pandemic began; so I queried several physician blogs and social media pages.

Responses varied all over the map. Some refuse the medium entirely. “I politely say that I don’t practice dermatology via email,” said one. “Please schedule a teledermatology appointment and I’d be happy to help.”

Others are ambivalent: “I do email with some patients who have complex situations or quick questions, but if it gets out of hand then I let them know someone will call to make an appointment.” Another office treats them as a one-way street: “We set up one account to receive patients’ emails, but we tell them clearly that we don’t respond ... my staff or I call them back.”

Still others have assimilated it completely. “Patients email through the portal and my MA routes [them] to me. I answer questions and the MA responds ... staff loves it because it’s so much faster than the phone.”

A 1998 study in JAMA was more scientifically designed, but basically reached the same conclusion. The authors found “a striking lack of consensus” on how to deal with patient emails: 50% responded to them, but 31% of responders refused to give advice without seeing the patient, while 59% offered a diagnosis, and a third of that group went on to provide specific advice about therapy. In response to a follow-up questionnaire, 28% said that they tended not to answer any patient emails, 24% said they usually replied with a standard message, and 24% said they answer each request individually. The authors concluded that “standards for physician response to unsolicited patient emails are needed.”

Indeed, my own unscientific survey suggests that, more than 20 years later, there is still nothing resembling a consensus on this issue. In the interim, several groups, including the American Medical Informatics Association and the American Medical Association have proposed standards, but none have been generally accepted. Until that happens, it seems prudent for each individual practice to adopt its own guidelines. For ideas, take a look at the proposals from the groups I mentioned, plus any others you can find. When you’re done, consider running your list past your attorney to make sure you haven’t forgotten anything, and that there are no unique requirements in your state.

Your guidelines may be very simple (if you decide never to answer any queries) or very complex, depending on your situation and personal philosophy. But all guidelines should cover such issues as authentication of correspondents, informed consent, licensing jurisdiction (if you receive emails from states in which you are not licensed), and of course, confidentiality.

Contrary to popular belief, HIPAA does not prohibit email communication with patients, nor require that it be encrypted. The HIPAA website specifically says: “Patients may initiate communications with a provider using email. If this situation occurs, the health care provider can assume (unless the patient has explicitly stated otherwise) that e-mail communications are acceptable to the individual.”

Still, if you are not comfortable with unencrypted communication, encryption software can be added to your practice’s email system. Proofpoint, Tumbleweed, Zix, and many other vendors sell encryption packages. (As always, I have no financial interest in any product or enterprise mentioned in this column.)

Another option is web-based messaging: Patients enter your website and send a message using an electronic template that you design. A designated staffer will be notified by regular email when messages are received, and can post a reply on a page that can only be accessed by the patient. Besides enhancing privacy and security, you can state your guidelines in plain English to preclude any misunderstanding of what you will and will not address online.

Web-based messaging services can be freestanding or incorporated into existing secure websites. Medfusion and klara are among the leading vendors of secure messaging services.

The important thing is to make a firm decision on how you want to deal with emails, and stick with that method. And follow your guidelines.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at [email protected].

The pandemic has isolated our patients to an unprecedented degree, forcing them to find other ways to communicate with us, including email. I wondered how private offices were handling the marked increase in email communications since the pandemic began; so I queried several physician blogs and social media pages.

Responses varied all over the map. Some refuse the medium entirely. “I politely say that I don’t practice dermatology via email,” said one. “Please schedule a teledermatology appointment and I’d be happy to help.”

Others are ambivalent: “I do email with some patients who have complex situations or quick questions, but if it gets out of hand then I let them know someone will call to make an appointment.” Another office treats them as a one-way street: “We set up one account to receive patients’ emails, but we tell them clearly that we don’t respond ... my staff or I call them back.”

Still others have assimilated it completely. “Patients email through the portal and my MA routes [them] to me. I answer questions and the MA responds ... staff loves it because it’s so much faster than the phone.”

A 1998 study in JAMA was more scientifically designed, but basically reached the same conclusion. The authors found “a striking lack of consensus” on how to deal with patient emails: 50% responded to them, but 31% of responders refused to give advice without seeing the patient, while 59% offered a diagnosis, and a third of that group went on to provide specific advice about therapy. In response to a follow-up questionnaire, 28% said that they tended not to answer any patient emails, 24% said they usually replied with a standard message, and 24% said they answer each request individually. The authors concluded that “standards for physician response to unsolicited patient emails are needed.”

Indeed, my own unscientific survey suggests that, more than 20 years later, there is still nothing resembling a consensus on this issue. In the interim, several groups, including the American Medical Informatics Association and the American Medical Association have proposed standards, but none have been generally accepted. Until that happens, it seems prudent for each individual practice to adopt its own guidelines. For ideas, take a look at the proposals from the groups I mentioned, plus any others you can find. When you’re done, consider running your list past your attorney to make sure you haven’t forgotten anything, and that there are no unique requirements in your state.

Your guidelines may be very simple (if you decide never to answer any queries) or very complex, depending on your situation and personal philosophy. But all guidelines should cover such issues as authentication of correspondents, informed consent, licensing jurisdiction (if you receive emails from states in which you are not licensed), and of course, confidentiality.

Contrary to popular belief, HIPAA does not prohibit email communication with patients, nor require that it be encrypted. The HIPAA website specifically says: “Patients may initiate communications with a provider using email. If this situation occurs, the health care provider can assume (unless the patient has explicitly stated otherwise) that e-mail communications are acceptable to the individual.”

Still, if you are not comfortable with unencrypted communication, encryption software can be added to your practice’s email system. Proofpoint, Tumbleweed, Zix, and many other vendors sell encryption packages. (As always, I have no financial interest in any product or enterprise mentioned in this column.)

Another option is web-based messaging: Patients enter your website and send a message using an electronic template that you design. A designated staffer will be notified by regular email when messages are received, and can post a reply on a page that can only be accessed by the patient. Besides enhancing privacy and security, you can state your guidelines in plain English to preclude any misunderstanding of what you will and will not address online.

Web-based messaging services can be freestanding or incorporated into existing secure websites. Medfusion and klara are among the leading vendors of secure messaging services.

The important thing is to make a firm decision on how you want to deal with emails, and stick with that method. And follow your guidelines.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at [email protected].

Malpractice lawsuits related to colonoscopy continue to pose challenges for practitioners, and a new analysis reveals that errors related to sedation are more likely to be awarded to plaintiffs. Primary care physicians and surgeons are often codefendants, which emphasizes the importance of interdisciplinary care in colonoscopy.

Cases involving informed consent were more likely to be ruled for the defendant, while those tied to medication error favored the plaintiff, according to an analysis of cases from the Westlaw legal database. The study, led by Krishan S. Patel and Sushil Ahlawat of Rutgers New Jersey Medical School, Newark, was published in the Journal of Clinical Gastroenterology.

According to the authors, 55% of physicians face a malpractice suit at some point in their careers, and gastroenterology ranks as the sixth most common specialty named in malpractice suits. Every year, about 13% of gastroenterologists confront malpractice allegations, and colonoscopy is the most common reason.

The researchers searched the Westlaw legal database for malpractice cases involving colonoscopy or sigmoidoscopy, identifying 305 cases between 1980 and 2017. The average patient age was 54.9 years, and 52.8% of cases were brought by female patients. The most cases were from New York (21.0%), followed by California (13.4%), Pennsylvania (13.1%), Massachusetts (12.5%), and New Jersey (7.9%). Gastroenterologists were named in 71.1% of cases, internists in 25.6%, and surgeons in 14.8%.

A little more than half (51.8%) of cases were ruled in favor of the defendant, and 25% for the plaintiff; 17% were settled, and 6% had a mixed outcome. Payouts ranged from $30,000 to $500,000,000, with a median of $995,000.

There were multiple causes of litigation listed in 83.6% of cases. The most frequent causes were delayed treatment (65.9%), delayed diagnosis (65.6%), procedural error/negligence (44.3%), and failure to refer/reorder tests (25.6%).

Of 135 cases alleging procedural negligence, 90 (67%) named perforation. Among 79 cases that cited a failure to refer and order appropriate tests, 97% claimed the defendant missed a cancerous lesion. In cases alleging missed cancers, 31% were in the cecum, and 23% in the anus.

A logistic regression analysis of factors associated with a verdict for the defendant found “lack of informed consent” to be an independent predictor of defendant verdict (odds ratio, 4.05; P = .004). “Medication error” was associated with reduced defendant success (OR, 0.17; P=.023). There were nonsignificant trends between reduced odds of a verdict for the defendant and lawsuits that named “delay in diagnosis” (OR, 0.35; P = .060) and “failure to refer” (OR, 0.51; P = .074).

The authors sound a dire note about the number of malpractice suits brought against gastroenterologists, but Lawrence Kosinski, MD, is more sanguine. He notes that gastroenterologists have low insurance premiums, compared with other specialties, but recognizes that colonoscopies are a significant source of risk.

Dr. Kosinski, who is chief medical officer at SonarMD and formerly a managing partner at the Illinois Gastroenterology Group, said in an interview that the study is revealing. “It comes out in the article: Acts of omission are more dangerous to the physician than acts of commission. Not finding that cancer, not acting on that malignant polyp, not pursuing it, is much more likely to get you in trouble than taking it off and perforating a colon,” said Dr. Kosinski, who was not involved in the study.

To gastroenterologists seeking to reduce their risks, he offered advice: You shouldn’t assume that the patient has read the information provided. Risks of anesthesia and the procedure should be directly communicated. It’s also important to document the procedure, including pictures of the cecum and rectal retroflexion. Finally, don’t rush. “This isn’t a race. Clean the colon, make sure you don’t miss something. If that person pops up in 3 years with a cancer, someone may go after you,” said Dr. Kosinski.

No source of funding was disclosed. Dr. Kosinski has no relevant financial disclosures.

Malpractice lawsuits related to colonoscopy continue to pose challenges for practitioners, and a new analysis reveals that errors related to sedation are more likely to be awarded to plaintiffs. Primary care physicians and surgeons are often codefendants, which emphasizes the importance of interdisciplinary care in colonoscopy.

Cases involving informed consent were more likely to be ruled for the defendant, while those tied to medication error favored the plaintiff, according to an analysis of cases from the Westlaw legal database. The study, led by Krishan S. Patel and Sushil Ahlawat of Rutgers New Jersey Medical School, Newark, was published in the Journal of Clinical Gastroenterology.

According to the authors, 55% of physicians face a malpractice suit at some point in their careers, and gastroenterology ranks as the sixth most common specialty named in malpractice suits. Every year, about 13% of gastroenterologists confront malpractice allegations, and colonoscopy is the most common reason.

The researchers searched the Westlaw legal database for malpractice cases involving colonoscopy or sigmoidoscopy, identifying 305 cases between 1980 and 2017. The average patient age was 54.9 years, and 52.8% of cases were brought by female patients. The most cases were from New York (21.0%), followed by California (13.4%), Pennsylvania (13.1%), Massachusetts (12.5%), and New Jersey (7.9%). Gastroenterologists were named in 71.1% of cases, internists in 25.6%, and surgeons in 14.8%.

A little more than half (51.8%) of cases were ruled in favor of the defendant, and 25% for the plaintiff; 17% were settled, and 6% had a mixed outcome. Payouts ranged from $30,000 to $500,000,000, with a median of $995,000.

There were multiple causes of litigation listed in 83.6% of cases. The most frequent causes were delayed treatment (65.9%), delayed diagnosis (65.6%), procedural error/negligence (44.3%), and failure to refer/reorder tests (25.6%).

Of 135 cases alleging procedural negligence, 90 (67%) named perforation. Among 79 cases that cited a failure to refer and order appropriate tests, 97% claimed the defendant missed a cancerous lesion. In cases alleging missed cancers, 31% were in the cecum, and 23% in the anus.

A logistic regression analysis of factors associated with a verdict for the defendant found “lack of informed consent” to be an independent predictor of defendant verdict (odds ratio, 4.05; P = .004). “Medication error” was associated with reduced defendant success (OR, 0.17; P=.023). There were nonsignificant trends between reduced odds of a verdict for the defendant and lawsuits that named “delay in diagnosis” (OR, 0.35; P = .060) and “failure to refer” (OR, 0.51; P = .074).

The authors sound a dire note about the number of malpractice suits brought against gastroenterologists, but Lawrence Kosinski, MD, is more sanguine. He notes that gastroenterologists have low insurance premiums, compared with other specialties, but recognizes that colonoscopies are a significant source of risk.

Dr. Kosinski, who is chief medical officer at SonarMD and formerly a managing partner at the Illinois Gastroenterology Group, said in an interview that the study is revealing. “It comes out in the article: Acts of omission are more dangerous to the physician than acts of commission. Not finding that cancer, not acting on that malignant polyp, not pursuing it, is much more likely to get you in trouble than taking it off and perforating a colon,” said Dr. Kosinski, who was not involved in the study.

To gastroenterologists seeking to reduce their risks, he offered advice: You shouldn’t assume that the patient has read the information provided. Risks of anesthesia and the procedure should be directly communicated. It’s also important to document the procedure, including pictures of the cecum and rectal retroflexion. Finally, don’t rush. “This isn’t a race. Clean the colon, make sure you don’t miss something. If that person pops up in 3 years with a cancer, someone may go after you,” said Dr. Kosinski.

No source of funding was disclosed. Dr. Kosinski has no relevant financial disclosures.

Malpractice lawsuits related to colonoscopy continue to pose challenges for practitioners, and a new analysis reveals that errors related to sedation are more likely to be awarded to plaintiffs. Primary care physicians and surgeons are often codefendants, which emphasizes the importance of interdisciplinary care in colonoscopy.

Cases involving informed consent were more likely to be ruled for the defendant, while those tied to medication error favored the plaintiff, according to an analysis of cases from the Westlaw legal database. The study, led by Krishan S. Patel and Sushil Ahlawat of Rutgers New Jersey Medical School, Newark, was published in the Journal of Clinical Gastroenterology.

According to the authors, 55% of physicians face a malpractice suit at some point in their careers, and gastroenterology ranks as the sixth most common specialty named in malpractice suits. Every year, about 13% of gastroenterologists confront malpractice allegations, and colonoscopy is the most common reason.

The researchers searched the Westlaw legal database for malpractice cases involving colonoscopy or sigmoidoscopy, identifying 305 cases between 1980 and 2017. The average patient age was 54.9 years, and 52.8% of cases were brought by female patients. The most cases were from New York (21.0%), followed by California (13.4%), Pennsylvania (13.1%), Massachusetts (12.5%), and New Jersey (7.9%). Gastroenterologists were named in 71.1% of cases, internists in 25.6%, and surgeons in 14.8%.

A little more than half (51.8%) of cases were ruled in favor of the defendant, and 25% for the plaintiff; 17% were settled, and 6% had a mixed outcome. Payouts ranged from $30,000 to $500,000,000, with a median of $995,000.

There were multiple causes of litigation listed in 83.6% of cases. The most frequent causes were delayed treatment (65.9%), delayed diagnosis (65.6%), procedural error/negligence (44.3%), and failure to refer/reorder tests (25.6%).

Of 135 cases alleging procedural negligence, 90 (67%) named perforation. Among 79 cases that cited a failure to refer and order appropriate tests, 97% claimed the defendant missed a cancerous lesion. In cases alleging missed cancers, 31% were in the cecum, and 23% in the anus.

A logistic regression analysis of factors associated with a verdict for the defendant found “lack of informed consent” to be an independent predictor of defendant verdict (odds ratio, 4.05; P = .004). “Medication error” was associated with reduced defendant success (OR, 0.17; P=.023). There were nonsignificant trends between reduced odds of a verdict for the defendant and lawsuits that named “delay in diagnosis” (OR, 0.35; P = .060) and “failure to refer” (OR, 0.51; P = .074).

The authors sound a dire note about the number of malpractice suits brought against gastroenterologists, but Lawrence Kosinski, MD, is more sanguine. He notes that gastroenterologists have low insurance premiums, compared with other specialties, but recognizes that colonoscopies are a significant source of risk.

Dr. Kosinski, who is chief medical officer at SonarMD and formerly a managing partner at the Illinois Gastroenterology Group, said in an interview that the study is revealing. “It comes out in the article: Acts of omission are more dangerous to the physician than acts of commission. Not finding that cancer, not acting on that malignant polyp, not pursuing it, is much more likely to get you in trouble than taking it off and perforating a colon,” said Dr. Kosinski, who was not involved in the study.

To gastroenterologists seeking to reduce their risks, he offered advice: You shouldn’t assume that the patient has read the information provided. Risks of anesthesia and the procedure should be directly communicated. It’s also important to document the procedure, including pictures of the cecum and rectal retroflexion. Finally, don’t rush. “This isn’t a race. Clean the colon, make sure you don’t miss something. If that person pops up in 3 years with a cancer, someone may go after you,” said Dr. Kosinski.

No source of funding was disclosed. Dr. Kosinski has no relevant financial disclosures.

Patients will soon be able to read the notes that physicians make during an episode of care, as well as information about diagnostic testing and imaging results, tests for STDs, fetal ultrasounds, and cancer biopsies. This open access is raising concerns among physicians.

As part of the 21st Century Cures Act, patients have the right to see their medical notes. Known as Open Notes, the policy will go into effect on April 5, 2021. The Department of Health & Human Services recently changed the original start date, which was to be Nov. 2, 2020.

The mandate has some physicians worrying about potential legal risks and possible violation of doctor-patient confidentiality. When asked to share their views on the new Open Notes mandate, many physicians expressed their concerns but also cited some of the positive effects that could come from this.
 

Potentially more legal woes for physicians?

A key concern raised by one physician commenter is that patients could misunderstand legitimate medical terminology or even put a physician in legal crosshairs. For example, a medical term such as “spontaneous abortion” could be misconstrued by patients. A physician might write notes with the idea that a patient is reading them and thus might alter those notes in a way that creates legal trouble.

“This layers another level of censorship and legal liability onto physicians, who in attempting to be [politically correct], may omit critical information or have to use euphemisms in order to avoid conflict,” one physician said.

She also questioned whether notes might now have to be run through legal counsel before being posted to avoid potential liability.

Another doctor questioned how physicians would be able to document patients suspected of faking injuries for pain medication, for example. Could such documentation lead to lawsuits for the doctor?

As one physician noted, some patients “are drug seekers. Some refuse to aid in their own care. Some are malingerers. Not documenting that is bad medicine.”

The possibility of violating doctor-patient confidentiality laws, particularly for teenagers, could be another negative effect of Open Notes, said one physician.

“Won’t this violate the statutes that teenagers have the right to confidential evaluations?” the commenter mused. “If charts are to be immediately available, then STDs and pregnancies they weren’t ready to talk about will now be suddenly known by their parents.”

One doctor has already faced this issue. “I already ran into this problem once,” he noted. “Now I warn those on their parents’ insurance before I start the visit. I have literally had a patient state, ‘well then we are done,’ and leave without being seen due to it.”

Another physician questioned the possibility of having to write notes differently than they do now, especially if the patients have lower reading comprehension abilities.

One physician who uses Open Notes said he receives patient requests for changes that have little to do with the actual diagnosis and relate to ancillary issues. He highlighted patients who “don’t want psych diagnosis in their chart or are concerned a diagnosis will raise their insurance premium, so they ask me to delete it.”
 

Will Open Notes erode patient communication?

One physician questioned whether it would lead to patients being less open and forthcoming about their medical concerns with doctors.

“The main problem I see is the patient not telling me the whole story, or worse, telling me the story, and then asking me not to document it (as many have done in the past) because they don’t want their spouse, family, etc. to read the notes and they have already given their permission for them to do so, for a variety of reasons,” he commented. “This includes topics of STDs, infidelity, depression, suicidal thoughts, and other symptoms the patient doesn’t want their family to read about.”
 

Some physicians envision positive developments

Many physicians are unconcerned by the new mandate. “I see some potential good in this, such as improving doctor-patient communication and more scrupulous charting,” one physician said.

A doctor working in the U.S. federal health care system noted that open access has been a part of that system for decades.

“Since health care providers work in this unveiled setting for their entire career, they usually know how to write appropriate clinical notes and what information needs to be included in them,” he wrote. “Now it’s time for the rest of the medical community to catch up to a reality that we have worked within for decades now.

“The world did not end, malpractice complaints did not increase, and physician/patient relationships were not damaged. Living in the information age, archaic practices like private notes were surely going to end at some point.”

One doctor who has been using Open Notes has had experiences in which the patient noted an error in the medical chart that needed correcting. “I have had one patient correct me on a timeline in the HPI which was helpful and I made the requested correction in that instance,” he said.

Another physician agreed. “I’ve had patients add or correct valuable information I’ve missed. Good probably outweighs the bad if we set limits on behaviors expressed by the personality disordered group. The majority of people don’t seem to care and still ask me ‘what would you do’ or ‘tell me what to do.’ It’s all about patient/physician trust.”

Another talked about how Open Notes should have little or no impact. “Here’s a novel concept – talking to our patients,” he commented. “There is nothing in every one of my chart notes that has not already been discussed with my patients and I dictate (speech to text) my findings and plan in front of them. So, if they are reviewing my office notes, it will only serve to reinforce what we have already discussed.”

“I don’t intend to change anything,” he added. “Chances are if they were to see a test result before I have a chance to discuss it with them, they will have already ‘Googled’ its meaning and we can have more meaningful interaction if they have a basic understanding of the test.”

“I understand that this is anxiety provoking, but in general I think it is appropriate for patients to have access to their notes,” said another physician. “If physicians write lousy notes that say they did things they didn’t do, that fail to actually state a diagnosis and a plan (and they often do), that is the doc’s problem, not the patient’s.”

A version of this article first appeared on Medscape.com.

Patients will soon be able to read the notes that physicians make during an episode of care, as well as information about diagnostic testing and imaging results, tests for STDs, fetal ultrasounds, and cancer biopsies. This open access is raising concerns among physicians.

As part of the 21st Century Cures Act, patients have the right to see their medical notes. Known as Open Notes, the policy will go into effect on April 5, 2021. The Department of Health & Human Services recently changed the original start date, which was to be Nov. 2, 2020.

The mandate has some physicians worrying about potential legal risks and possible violation of doctor-patient confidentiality. When asked to share their views on the new Open Notes mandate, many physicians expressed their concerns but also cited some of the positive effects that could come from this.
 

Potentially more legal woes for physicians?

A key concern raised by one physician commenter is that patients could misunderstand legitimate medical terminology or even put a physician in legal crosshairs. For example, a medical term such as “spontaneous abortion” could be misconstrued by patients. A physician might write notes with the idea that a patient is reading them and thus might alter those notes in a way that creates legal trouble.

“This layers another level of censorship and legal liability onto physicians, who in attempting to be [politically correct], may omit critical information or have to use euphemisms in order to avoid conflict,” one physician said.

She also questioned whether notes might now have to be run through legal counsel before being posted to avoid potential liability.

Another doctor questioned how physicians would be able to document patients suspected of faking injuries for pain medication, for example. Could such documentation lead to lawsuits for the doctor?

As one physician noted, some patients “are drug seekers. Some refuse to aid in their own care. Some are malingerers. Not documenting that is bad medicine.”

The possibility of violating doctor-patient confidentiality laws, particularly for teenagers, could be another negative effect of Open Notes, said one physician.

“Won’t this violate the statutes that teenagers have the right to confidential evaluations?” the commenter mused. “If charts are to be immediately available, then STDs and pregnancies they weren’t ready to talk about will now be suddenly known by their parents.”

One doctor has already faced this issue. “I already ran into this problem once,” he noted. “Now I warn those on their parents’ insurance before I start the visit. I have literally had a patient state, ‘well then we are done,’ and leave without being seen due to it.”

Another physician questioned the possibility of having to write notes differently than they do now, especially if the patients have lower reading comprehension abilities.

One physician who uses Open Notes said he receives patient requests for changes that have little to do with the actual diagnosis and relate to ancillary issues. He highlighted patients who “don’t want psych diagnosis in their chart or are concerned a diagnosis will raise their insurance premium, so they ask me to delete it.”
 

Will Open Notes erode patient communication?

One physician questioned whether it would lead to patients being less open and forthcoming about their medical concerns with doctors.

“The main problem I see is the patient not telling me the whole story, or worse, telling me the story, and then asking me not to document it (as many have done in the past) because they don’t want their spouse, family, etc. to read the notes and they have already given their permission for them to do so, for a variety of reasons,” he commented. “This includes topics of STDs, infidelity, depression, suicidal thoughts, and other symptoms the patient doesn’t want their family to read about.”
 

Some physicians envision positive developments

Many physicians are unconcerned by the new mandate. “I see some potential good in this, such as improving doctor-patient communication and more scrupulous charting,” one physician said.

A doctor working in the U.S. federal health care system noted that open access has been a part of that system for decades.

“Since health care providers work in this unveiled setting for their entire career, they usually know how to write appropriate clinical notes and what information needs to be included in them,” he wrote. “Now it’s time for the rest of the medical community to catch up to a reality that we have worked within for decades now.

“The world did not end, malpractice complaints did not increase, and physician/patient relationships were not damaged. Living in the information age, archaic practices like private notes were surely going to end at some point.”

One doctor who has been using Open Notes has had experiences in which the patient noted an error in the medical chart that needed correcting. “I have had one patient correct me on a timeline in the HPI which was helpful and I made the requested correction in that instance,” he said.

Another physician agreed. “I’ve had patients add or correct valuable information I’ve missed. Good probably outweighs the bad if we set limits on behaviors expressed by the personality disordered group. The majority of people don’t seem to care and still ask me ‘what would you do’ or ‘tell me what to do.’ It’s all about patient/physician trust.”

Another talked about how Open Notes should have little or no impact. “Here’s a novel concept – talking to our patients,” he commented. “There is nothing in every one of my chart notes that has not already been discussed with my patients and I dictate (speech to text) my findings and plan in front of them. So, if they are reviewing my office notes, it will only serve to reinforce what we have already discussed.”

“I don’t intend to change anything,” he added. “Chances are if they were to see a test result before I have a chance to discuss it with them, they will have already ‘Googled’ its meaning and we can have more meaningful interaction if they have a basic understanding of the test.”

“I understand that this is anxiety provoking, but in general I think it is appropriate for patients to have access to their notes,” said another physician. “If physicians write lousy notes that say they did things they didn’t do, that fail to actually state a diagnosis and a plan (and they often do), that is the doc’s problem, not the patient’s.”

A version of this article first appeared on Medscape.com.

Patients will soon be able to read the notes that physicians make during an episode of care, as well as information about diagnostic testing and imaging results, tests for STDs, fetal ultrasounds, and cancer biopsies. This open access is raising concerns among physicians.

As part of the 21st Century Cures Act, patients have the right to see their medical notes. Known as Open Notes, the policy will go into effect on April 5, 2021. The Department of Health & Human Services recently changed the original start date, which was to be Nov. 2, 2020.

The mandate has some physicians worrying about potential legal risks and possible violation of doctor-patient confidentiality. When asked to share their views on the new Open Notes mandate, many physicians expressed their concerns but also cited some of the positive effects that could come from this.
 

Potentially more legal woes for physicians?

A key concern raised by one physician commenter is that patients could misunderstand legitimate medical terminology or even put a physician in legal crosshairs. For example, a medical term such as “spontaneous abortion” could be misconstrued by patients. A physician might write notes with the idea that a patient is reading them and thus might alter those notes in a way that creates legal trouble.

“This layers another level of censorship and legal liability onto physicians, who in attempting to be [politically correct], may omit critical information or have to use euphemisms in order to avoid conflict,” one physician said.

She also questioned whether notes might now have to be run through legal counsel before being posted to avoid potential liability.

Another doctor questioned how physicians would be able to document patients suspected of faking injuries for pain medication, for example. Could such documentation lead to lawsuits for the doctor?

As one physician noted, some patients “are drug seekers. Some refuse to aid in their own care. Some are malingerers. Not documenting that is bad medicine.”

The possibility of violating doctor-patient confidentiality laws, particularly for teenagers, could be another negative effect of Open Notes, said one physician.

“Won’t this violate the statutes that teenagers have the right to confidential evaluations?” the commenter mused. “If charts are to be immediately available, then STDs and pregnancies they weren’t ready to talk about will now be suddenly known by their parents.”

One doctor has already faced this issue. “I already ran into this problem once,” he noted. “Now I warn those on their parents’ insurance before I start the visit. I have literally had a patient state, ‘well then we are done,’ and leave without being seen due to it.”

Another physician questioned the possibility of having to write notes differently than they do now, especially if the patients have lower reading comprehension abilities.

One physician who uses Open Notes said he receives patient requests for changes that have little to do with the actual diagnosis and relate to ancillary issues. He highlighted patients who “don’t want psych diagnosis in their chart or are concerned a diagnosis will raise their insurance premium, so they ask me to delete it.”
 

Will Open Notes erode patient communication?

One physician questioned whether it would lead to patients being less open and forthcoming about their medical concerns with doctors.

“The main problem I see is the patient not telling me the whole story, or worse, telling me the story, and then asking me not to document it (as many have done in the past) because they don’t want their spouse, family, etc. to read the notes and they have already given their permission for them to do so, for a variety of reasons,” he commented. “This includes topics of STDs, infidelity, depression, suicidal thoughts, and other symptoms the patient doesn’t want their family to read about.”
 

Some physicians envision positive developments

Many physicians are unconcerned by the new mandate. “I see some potential good in this, such as improving doctor-patient communication and more scrupulous charting,” one physician said.

A doctor working in the U.S. federal health care system noted that open access has been a part of that system for decades.

“Since health care providers work in this unveiled setting for their entire career, they usually know how to write appropriate clinical notes and what information needs to be included in them,” he wrote. “Now it’s time for the rest of the medical community to catch up to a reality that we have worked within for decades now.

“The world did not end, malpractice complaints did not increase, and physician/patient relationships were not damaged. Living in the information age, archaic practices like private notes were surely going to end at some point.”

One doctor who has been using Open Notes has had experiences in which the patient noted an error in the medical chart that needed correcting. “I have had one patient correct me on a timeline in the HPI which was helpful and I made the requested correction in that instance,” he said.

Another physician agreed. “I’ve had patients add or correct valuable information I’ve missed. Good probably outweighs the bad if we set limits on behaviors expressed by the personality disordered group. The majority of people don’t seem to care and still ask me ‘what would you do’ or ‘tell me what to do.’ It’s all about patient/physician trust.”

Another talked about how Open Notes should have little or no impact. “Here’s a novel concept – talking to our patients,” he commented. “There is nothing in every one of my chart notes that has not already been discussed with my patients and I dictate (speech to text) my findings and plan in front of them. So, if they are reviewing my office notes, it will only serve to reinforce what we have already discussed.”

“I don’t intend to change anything,” he added. “Chances are if they were to see a test result before I have a chance to discuss it with them, they will have already ‘Googled’ its meaning and we can have more meaningful interaction if they have a basic understanding of the test.”

“I understand that this is anxiety provoking, but in general I think it is appropriate for patients to have access to their notes,” said another physician. “If physicians write lousy notes that say they did things they didn’t do, that fail to actually state a diagnosis and a plan (and they often do), that is the doc’s problem, not the patient’s.”

A version of this article first appeared on Medscape.com.

The ability of advanced practice providers (APPs) to practice independently has been a recent topic of discussion among both the medical community and legislatures. Advanced practice provider is an umbrella term that includes physician assistants (PAs) and advanced practice registered nurses, including nurse practitioners (NPs), clinical nurse specialists, certified nurse-midwives, and certified registered nurse anesthetists. Since Congress passed the Balanced Budget Act of 1997, APPs can bill and be paid independently if they are not practicing incident to a physician or in a facility.¹ Currently, NPs can practice independently in 27 states and Washington, DC. Physician assistants are required to practice under the supervision of a physician; however, the extent of supervision varies by state.² Advocates for broadening the scope of practice for APPs argue that NPs and PAs will help to fill the physician deficit, particularly in primary care and rural regions. It has been projected that by 2025, the United States will require an additional 46,000 primary care providers to meet growing medical needs.³

On October 3, 2019, President Donald Trump issued the Executive Order on Protecting and Improving Medicare for Our Nation’s Seniors, in which he proposed an alternative to “Medicare for all.”⁴ This order instructed the Secretary of Health and Human Services to prepare a regulation that would “eliminate burdensome regulatory billing requirements, conditions of participation, supervision requirements, benefit definitions and all other licensure requirements . . . that are more stringent than applicable Federal or State laws require and that limit professionals from practicing at the top of their field.” Furthermore, President Trump proposed that “services provided by clinicians, including physicians, physician assistants, and nurse practitioners, are appropriately reimbursed in accordance with the work performed rather than the clinician’s occupation.”⁴

In response to the executive order, members of the medical community utilized Reddit, an online public forum, and Medscape, a medical news website, to vocalize opinions on the executive order.^5,6 Our goal was to analyze the characteristics of those who participated in the discussion and their points of view on the plan to broaden the scope of practice and change the Medicare reimbursement plans for APPs.

Methods

All comments on the October 3, 2019, Medscape article, “Trump Executive Order Seeks Proposals on Medicare Pay for NPs, PAs,”⁵ and the corresponding Reddit discussion on this article⁶ were reviewed and characterized by the type of commenter—doctor of medicine (MD)/doctor of osteopathic medicine (DO), NP/RN/certified registered nurse anesthetist, PA, medical student, PA student, NP student, pharmacist, dietician, emergency medical technician, scribe, or unknown—as identified in their username, title, or in the text of the comment. Gender of the commenter was recorded when provided. Commenters were further grouped by their support or lack of support for the executive order based on their comments. Patients’ comments underwent further qualitative analysis to identify general themes.

All analyses were conducted with RStudio statistical software. Analyses were reported as proportions. Variables were compared by χ² and Fisher exact tests. Odds ratios with 95% CIs were calculated. P<.05 was considered statistically significant.

Results

A total of 352 comments (130 on Medscape and 222 on Reddit) posted by 155 unique users (57 on Medscape and 98 on Reddit) were included in the analysis (Table 1). Of the 51 Medscape commenters who identified a gender, 60.7% were male and 39.2% were female. Reddit commenters did not identify a gender. Commenters included MD and DO physicians (43.2%), NPs/RNs/certified registered nurse anesthetists (13.5%), medical students (11.0%), PAs (9.7%), pharmacists (3.2%), NP students (1.9%), PA students (1.3%), emergency medical technicians (1.3%), dieticians (0.6%), and scribes (0.6%). Physicians (54.5% vs 36.73%; P=.032) and NPs (22.8% vs 8.2%; P=.009) made up a larger percentage of all comments on Medscape compared to Reddit, where medical students were more prevalent (16.3% vs 1.8%; P=.005). Nursing students and PA students more commonly posted on Reddit (4.08% of Reddit commenters vs 1.75% of Medscape commenters), though this difference did not achieve statistical significance.

A majority of commenters did not support the executive order, with only 20.6% approving of the plan, 54.8% disapproving, and 24.5% remaining neutral (Figure). Advanced practice providers—NPs, PAs, NP/PA students, and APPs not otherwise specified—were more likely to support the executive order, with 52.3% voicing their support compared to only 4.8% of physicians and medical students expressing support (P<.0001). Similarly, physicians and medical students were more likely to disapprove of the order, with 75.0% voicing concerns compared to only 27.3% of APPs dissenting (P<.0001). A similar percentage of both physicians/medical students and APPs remained neutral (20.2% vs 18.2%). Commenters on Medscape were more likely to voice support for the executive order than those on Reddit (36.8% vs 11.2%; P=.0002), likely due to the higher percentage of NP and PA comments on the former.

Comment

President Trump’s executive order follows a trend of decreasing required oversight of APPs; however, this study indicates that these policies would face pushback from many physicians. These results are consistent with a prior study that analyzed 309 comments on an article in The New York Times made by physicians, APPs, patients, and laypeople, in which 24.7% had mistrust of APPs and 14.9% had concerns over APP supervision compared to 9% who supported APP independent practice.⁷ It is clear that there is a serious divide in opinion that threatens to harm the existing collaborations between physicians and APPs.

Primary Care Coverage With APPs
In the comments analyzed in our study, supporters of the executive order argued that an increase in APPs practicing independently would provide much-needed primary care coverage to patients in underserved regions. However, APPs are instead well represented across most specialties, with a majority in dermatology. Of the 4 million procedures billed independently by APPs in 2012, 54.8% were in the field of dermatology.⁸ The employment of APPs by dermatologists has grown from 28% of practices in 2005 to 46% in 2014, making this issue of particular importance to our field.^9,10

Education and Training of APPs
In our analysis, many physicians cited concerns about the education and training of APPs. Dermatologists receive approximately 10,000 hours of training over the course of residency. Per the American Academy of Physician Assistants, PAs spend more than 2000 hours over a 26-month period on various clinical rotations, “with an emphasis on primary care.”¹¹ There are multiple routes to become an advanced practice RN with varying classroom and clinical requirements, with one pathway requiring a bachelor of science in nursing, followed by a master’s degree requiring 500 to 700 hours of supervised clinical work. Although the Dermatology Nurses’ Association and Society of Dermatology Physician Assistants (http://www.dermpa.org) provide online modules, annual conventions with training workshops, and short fellowship programs, neither have formal guidelines on minimum requirements to diagnose and treat dermatologic conditions.² Despite the lack of formalized dermatologic training, APPs billed for 13.4% of all dermatology procedures submitted to Medicare in 2015.¹²

Quality of Patient Care
In our study, physicians also voiced concern over reduced quality of patient care. In a review of 33,647 skin cancer screening examinations, PAs biopsied an average of 39.4 skin lesions, while dermatologists biopsied an average of 25.4 skin lesions to diagnose 1 case of melanoma.¹³ In addition, nonphysician providers accounted for 37.9% of defendants in 174 legal cases related to injury from cutaneous laser surgery.¹⁴ Before further laws are enacted regarding the independent practice and billing by NPs and PAs in the field of dermatology, further research is needed to address patient outcomes and safety.

Limitations
This study was subject to several limitations. Because of a lack of other sources offering discussions on the topic, our sample size was limited. Self-identification of users presents a challenge, as an individual can pose as a physician or APP without validation of credentials. Although great care was taken to minimize bias, grouping comments into broad categories may misinterpret a poster’s intentions. Furthermore, the data collected represent only a small proportion of the medical community—readers of Medscape and Reddit who have the motivation to create a user profile and post a comment rather than put their efforts into lobbying or contacting legislators. Those posting may have stronger political opinions or more poignant experiences than the general public. Although selection bias impacts the generalizability of our findings, this analysis allows for deeper insight into the beliefs of a vocal subset of the medical community who may not have the opportunity to present their opinions elsewhere.

Conclusion

Our analysis of the response to President Trump’s executive order reveals that a rollout of these regulations would be met with strong opposition. On October 29, 2019, more than 100 professional organizations, including the American Medical Association and the American Academy of Dermatology, wrote a letter to the Secretary of Health and Human Services that eloquently echoed the sentiments of the physician commenters in this study: “Scope of practice of health care professionals should be based on standardized, adequate training and demonstrated competence in patient care, not politics. While all health care professionals share an important role in providing care to patients, their skillset is not interchangeable with that of a fully trained physician.”¹⁵ The executive order would lead to a major shift in the current medical landscape, and as such, it is prudent that these concerns are addressed.

The ability of advanced practice providers (APPs) to practice independently has been a recent topic of discussion among both the medical community and legislatures. Advanced practice provider is an umbrella term that includes physician assistants (PAs) and advanced practice registered nurses, including nurse practitioners (NPs), clinical nurse specialists, certified nurse-midwives, and certified registered nurse anesthetists. Since Congress passed the Balanced Budget Act of 1997, APPs can bill and be paid independently if they are not practicing incident to a physician or in a facility.¹ Currently, NPs can practice independently in 27 states and Washington, DC. Physician assistants are required to practice under the supervision of a physician; however, the extent of supervision varies by state.² Advocates for broadening the scope of practice for APPs argue that NPs and PAs will help to fill the physician deficit, particularly in primary care and rural regions. It has been projected that by 2025, the United States will require an additional 46,000 primary care providers to meet growing medical needs.³

On October 3, 2019, President Donald Trump issued the Executive Order on Protecting and Improving Medicare for Our Nation’s Seniors, in which he proposed an alternative to “Medicare for all.”⁴ This order instructed the Secretary of Health and Human Services to prepare a regulation that would “eliminate burdensome regulatory billing requirements, conditions of participation, supervision requirements, benefit definitions and all other licensure requirements . . . that are more stringent than applicable Federal or State laws require and that limit professionals from practicing at the top of their field.” Furthermore, President Trump proposed that “services provided by clinicians, including physicians, physician assistants, and nurse practitioners, are appropriately reimbursed in accordance with the work performed rather than the clinician’s occupation.”⁴

In response to the executive order, members of the medical community utilized Reddit, an online public forum, and Medscape, a medical news website, to vocalize opinions on the executive order.^5,6 Our goal was to analyze the characteristics of those who participated in the discussion and their points of view on the plan to broaden the scope of practice and change the Medicare reimbursement plans for APPs.

Methods

All comments on the October 3, 2019, Medscape article, “Trump Executive Order Seeks Proposals on Medicare Pay for NPs, PAs,”⁵ and the corresponding Reddit discussion on this article⁶ were reviewed and characterized by the type of commenter—doctor of medicine (MD)/doctor of osteopathic medicine (DO), NP/RN/certified registered nurse anesthetist, PA, medical student, PA student, NP student, pharmacist, dietician, emergency medical technician, scribe, or unknown—as identified in their username, title, or in the text of the comment. Gender of the commenter was recorded when provided. Commenters were further grouped by their support or lack of support for the executive order based on their comments. Patients’ comments underwent further qualitative analysis to identify general themes.

All analyses were conducted with RStudio statistical software. Analyses were reported as proportions. Variables were compared by χ² and Fisher exact tests. Odds ratios with 95% CIs were calculated. P<.05 was considered statistically significant.

Results

A total of 352 comments (130 on Medscape and 222 on Reddit) posted by 155 unique users (57 on Medscape and 98 on Reddit) were included in the analysis (Table 1). Of the 51 Medscape commenters who identified a gender, 60.7% were male and 39.2% were female. Reddit commenters did not identify a gender. Commenters included MD and DO physicians (43.2%), NPs/RNs/certified registered nurse anesthetists (13.5%), medical students (11.0%), PAs (9.7%), pharmacists (3.2%), NP students (1.9%), PA students (1.3%), emergency medical technicians (1.3%), dieticians (0.6%), and scribes (0.6%). Physicians (54.5% vs 36.73%; P=.032) and NPs (22.8% vs 8.2%; P=.009) made up a larger percentage of all comments on Medscape compared to Reddit, where medical students were more prevalent (16.3% vs 1.8%; P=.005). Nursing students and PA students more commonly posted on Reddit (4.08% of Reddit commenters vs 1.75% of Medscape commenters), though this difference did not achieve statistical significance.

A majority of commenters did not support the executive order, with only 20.6% approving of the plan, 54.8% disapproving, and 24.5% remaining neutral (Figure). Advanced practice providers—NPs, PAs, NP/PA students, and APPs not otherwise specified—were more likely to support the executive order, with 52.3% voicing their support compared to only 4.8% of physicians and medical students expressing support (P<.0001). Similarly, physicians and medical students were more likely to disapprove of the order, with 75.0% voicing concerns compared to only 27.3% of APPs dissenting (P<.0001). A similar percentage of both physicians/medical students and APPs remained neutral (20.2% vs 18.2%). Commenters on Medscape were more likely to voice support for the executive order than those on Reddit (36.8% vs 11.2%; P=.0002), likely due to the higher percentage of NP and PA comments on the former.

Comment

President Trump’s executive order follows a trend of decreasing required oversight of APPs; however, this study indicates that these policies would face pushback from many physicians. These results are consistent with a prior study that analyzed 309 comments on an article in The New York Times made by physicians, APPs, patients, and laypeople, in which 24.7% had mistrust of APPs and 14.9% had concerns over APP supervision compared to 9% who supported APP independent practice.⁷ It is clear that there is a serious divide in opinion that threatens to harm the existing collaborations between physicians and APPs.

Primary Care Coverage With APPs
In the comments analyzed in our study, supporters of the executive order argued that an increase in APPs practicing independently would provide much-needed primary care coverage to patients in underserved regions. However, APPs are instead well represented across most specialties, with a majority in dermatology. Of the 4 million procedures billed independently by APPs in 2012, 54.8% were in the field of dermatology.⁸ The employment of APPs by dermatologists has grown from 28% of practices in 2005 to 46% in 2014, making this issue of particular importance to our field.^9,10

Education and Training of APPs
In our analysis, many physicians cited concerns about the education and training of APPs. Dermatologists receive approximately 10,000 hours of training over the course of residency. Per the American Academy of Physician Assistants, PAs spend more than 2000 hours over a 26-month period on various clinical rotations, “with an emphasis on primary care.”¹¹ There are multiple routes to become an advanced practice RN with varying classroom and clinical requirements, with one pathway requiring a bachelor of science in nursing, followed by a master’s degree requiring 500 to 700 hours of supervised clinical work. Although the Dermatology Nurses’ Association and Society of Dermatology Physician Assistants (http://www.dermpa.org) provide online modules, annual conventions with training workshops, and short fellowship programs, neither have formal guidelines on minimum requirements to diagnose and treat dermatologic conditions.² Despite the lack of formalized dermatologic training, APPs billed for 13.4% of all dermatology procedures submitted to Medicare in 2015.¹²

Quality of Patient Care
In our study, physicians also voiced concern over reduced quality of patient care. In a review of 33,647 skin cancer screening examinations, PAs biopsied an average of 39.4 skin lesions, while dermatologists biopsied an average of 25.4 skin lesions to diagnose 1 case of melanoma.¹³ In addition, nonphysician providers accounted for 37.9% of defendants in 174 legal cases related to injury from cutaneous laser surgery.¹⁴ Before further laws are enacted regarding the independent practice and billing by NPs and PAs in the field of dermatology, further research is needed to address patient outcomes and safety.

Limitations
This study was subject to several limitations. Because of a lack of other sources offering discussions on the topic, our sample size was limited. Self-identification of users presents a challenge, as an individual can pose as a physician or APP without validation of credentials. Although great care was taken to minimize bias, grouping comments into broad categories may misinterpret a poster’s intentions. Furthermore, the data collected represent only a small proportion of the medical community—readers of Medscape and Reddit who have the motivation to create a user profile and post a comment rather than put their efforts into lobbying or contacting legislators. Those posting may have stronger political opinions or more poignant experiences than the general public. Although selection bias impacts the generalizability of our findings, this analysis allows for deeper insight into the beliefs of a vocal subset of the medical community who may not have the opportunity to present their opinions elsewhere.

Conclusion

Our analysis of the response to President Trump’s executive order reveals that a rollout of these regulations would be met with strong opposition. On October 29, 2019, more than 100 professional organizations, including the American Medical Association and the American Academy of Dermatology, wrote a letter to the Secretary of Health and Human Services that eloquently echoed the sentiments of the physician commenters in this study: “Scope of practice of health care professionals should be based on standardized, adequate training and demonstrated competence in patient care, not politics. While all health care professionals share an important role in providing care to patients, their skillset is not interchangeable with that of a fully trained physician.”¹⁵ The executive order would lead to a major shift in the current medical landscape, and as such, it is prudent that these concerns are addressed.

The ability of advanced practice providers (APPs) to practice independently has been a recent topic of discussion among both the medical community and legislatures. Advanced practice provider is an umbrella term that includes physician assistants (PAs) and advanced practice registered nurses, including nurse practitioners (NPs), clinical nurse specialists, certified nurse-midwives, and certified registered nurse anesthetists. Since Congress passed the Balanced Budget Act of 1997, APPs can bill and be paid independently if they are not practicing incident to a physician or in a facility.¹ Currently, NPs can practice independently in 27 states and Washington, DC. Physician assistants are required to practice under the supervision of a physician; however, the extent of supervision varies by state.² Advocates for broadening the scope of practice for APPs argue that NPs and PAs will help to fill the physician deficit, particularly in primary care and rural regions. It has been projected that by 2025, the United States will require an additional 46,000 primary care providers to meet growing medical needs.³

On October 3, 2019, President Donald Trump issued the Executive Order on Protecting and Improving Medicare for Our Nation’s Seniors, in which he proposed an alternative to “Medicare for all.”⁴ This order instructed the Secretary of Health and Human Services to prepare a regulation that would “eliminate burdensome regulatory billing requirements, conditions of participation, supervision requirements, benefit definitions and all other licensure requirements . . . that are more stringent than applicable Federal or State laws require and that limit professionals from practicing at the top of their field.” Furthermore, President Trump proposed that “services provided by clinicians, including physicians, physician assistants, and nurse practitioners, are appropriately reimbursed in accordance with the work performed rather than the clinician’s occupation.”⁴

In response to the executive order, members of the medical community utilized Reddit, an online public forum, and Medscape, a medical news website, to vocalize opinions on the executive order.^5,6 Our goal was to analyze the characteristics of those who participated in the discussion and their points of view on the plan to broaden the scope of practice and change the Medicare reimbursement plans for APPs.

Methods

All comments on the October 3, 2019, Medscape article, “Trump Executive Order Seeks Proposals on Medicare Pay for NPs, PAs,”⁵ and the corresponding Reddit discussion on this article⁶ were reviewed and characterized by the type of commenter—doctor of medicine (MD)/doctor of osteopathic medicine (DO), NP/RN/certified registered nurse anesthetist, PA, medical student, PA student, NP student, pharmacist, dietician, emergency medical technician, scribe, or unknown—as identified in their username, title, or in the text of the comment. Gender of the commenter was recorded when provided. Commenters were further grouped by their support or lack of support for the executive order based on their comments. Patients’ comments underwent further qualitative analysis to identify general themes.

All analyses were conducted with RStudio statistical software. Analyses were reported as proportions. Variables were compared by χ² and Fisher exact tests. Odds ratios with 95% CIs were calculated. P<.05 was considered statistically significant.

Results

A total of 352 comments (130 on Medscape and 222 on Reddit) posted by 155 unique users (57 on Medscape and 98 on Reddit) were included in the analysis (Table 1). Of the 51 Medscape commenters who identified a gender, 60.7% were male and 39.2% were female. Reddit commenters did not identify a gender. Commenters included MD and DO physicians (43.2%), NPs/RNs/certified registered nurse anesthetists (13.5%), medical students (11.0%), PAs (9.7%), pharmacists (3.2%), NP students (1.9%), PA students (1.3%), emergency medical technicians (1.3%), dieticians (0.6%), and scribes (0.6%). Physicians (54.5% vs 36.73%; P=.032) and NPs (22.8% vs 8.2%; P=.009) made up a larger percentage of all comments on Medscape compared to Reddit, where medical students were more prevalent (16.3% vs 1.8%; P=.005). Nursing students and PA students more commonly posted on Reddit (4.08% of Reddit commenters vs 1.75% of Medscape commenters), though this difference did not achieve statistical significance.

A majority of commenters did not support the executive order, with only 20.6% approving of the plan, 54.8% disapproving, and 24.5% remaining neutral (Figure). Advanced practice providers—NPs, PAs, NP/PA students, and APPs not otherwise specified—were more likely to support the executive order, with 52.3% voicing their support compared to only 4.8% of physicians and medical students expressing support (P<.0001). Similarly, physicians and medical students were more likely to disapprove of the order, with 75.0% voicing concerns compared to only 27.3% of APPs dissenting (P<.0001). A similar percentage of both physicians/medical students and APPs remained neutral (20.2% vs 18.2%). Commenters on Medscape were more likely to voice support for the executive order than those on Reddit (36.8% vs 11.2%; P=.0002), likely due to the higher percentage of NP and PA comments on the former.

Comment

President Trump’s executive order follows a trend of decreasing required oversight of APPs; however, this study indicates that these policies would face pushback from many physicians. These results are consistent with a prior study that analyzed 309 comments on an article in The New York Times made by physicians, APPs, patients, and laypeople, in which 24.7% had mistrust of APPs and 14.9% had concerns over APP supervision compared to 9% who supported APP independent practice.⁷ It is clear that there is a serious divide in opinion that threatens to harm the existing collaborations between physicians and APPs.

Primary Care Coverage With APPs
In the comments analyzed in our study, supporters of the executive order argued that an increase in APPs practicing independently would provide much-needed primary care coverage to patients in underserved regions. However, APPs are instead well represented across most specialties, with a majority in dermatology. Of the 4 million procedures billed independently by APPs in 2012, 54.8% were in the field of dermatology.⁸ The employment of APPs by dermatologists has grown from 28% of practices in 2005 to 46% in 2014, making this issue of particular importance to our field.^9,10

Education and Training of APPs
In our analysis, many physicians cited concerns about the education and training of APPs. Dermatologists receive approximately 10,000 hours of training over the course of residency. Per the American Academy of Physician Assistants, PAs spend more than 2000 hours over a 26-month period on various clinical rotations, “with an emphasis on primary care.”¹¹ There are multiple routes to become an advanced practice RN with varying classroom and clinical requirements, with one pathway requiring a bachelor of science in nursing, followed by a master’s degree requiring 500 to 700 hours of supervised clinical work. Although the Dermatology Nurses’ Association and Society of Dermatology Physician Assistants (http://www.dermpa.org) provide online modules, annual conventions with training workshops, and short fellowship programs, neither have formal guidelines on minimum requirements to diagnose and treat dermatologic conditions.² Despite the lack of formalized dermatologic training, APPs billed for 13.4% of all dermatology procedures submitted to Medicare in 2015.¹²

Quality of Patient Care
In our study, physicians also voiced concern over reduced quality of patient care. In a review of 33,647 skin cancer screening examinations, PAs biopsied an average of 39.4 skin lesions, while dermatologists biopsied an average of 25.4 skin lesions to diagnose 1 case of melanoma.¹³ In addition, nonphysician providers accounted for 37.9% of defendants in 174 legal cases related to injury from cutaneous laser surgery.¹⁴ Before further laws are enacted regarding the independent practice and billing by NPs and PAs in the field of dermatology, further research is needed to address patient outcomes and safety.

Limitations
This study was subject to several limitations. Because of a lack of other sources offering discussions on the topic, our sample size was limited. Self-identification of users presents a challenge, as an individual can pose as a physician or APP without validation of credentials. Although great care was taken to minimize bias, grouping comments into broad categories may misinterpret a poster’s intentions. Furthermore, the data collected represent only a small proportion of the medical community—readers of Medscape and Reddit who have the motivation to create a user profile and post a comment rather than put their efforts into lobbying or contacting legislators. Those posting may have stronger political opinions or more poignant experiences than the general public. Although selection bias impacts the generalizability of our findings, this analysis allows for deeper insight into the beliefs of a vocal subset of the medical community who may not have the opportunity to present their opinions elsewhere.

Conclusion

Our analysis of the response to President Trump’s executive order reveals that a rollout of these regulations would be met with strong opposition. On October 29, 2019, more than 100 professional organizations, including the American Medical Association and the American Academy of Dermatology, wrote a letter to the Secretary of Health and Human Services that eloquently echoed the sentiments of the physician commenters in this study: “Scope of practice of health care professionals should be based on standardized, adequate training and demonstrated competence in patient care, not politics. While all health care professionals share an important role in providing care to patients, their skillset is not interchangeable with that of a fully trained physician.”¹⁵ The executive order would lead to a major shift in the current medical landscape, and as such, it is prudent that these concerns are addressed.

Congress has ordered the holdouts among U.S. states to have their Medicaid programs cover expenses related to participation in certain clinical trials, a move that was hailed by the American Society of Clinical Oncology and other groups as a boost to trials as well as to patients with serious illness who have lower incomes.

A massive wrap-up spending/COVID-19 relief bill that was signed into law Dec. 27 carried with it a mandate on Medicaid. States are ordered to put in place Medicaid payment policies for routine items and services, such as the cost of physician visits or laboratory tests, that are provided in connection with participation in clinical trials for serious and life-threatening conditions. The law includes a January 2022 target date for this coverage through Medicaid.

Medicare and other large insurers already pick up the tab for these kinds of expenses, leaving Medicaid as an outlier, ASCO noted in a press statement. ASCO and other cancer groups have for years pressed Medicaid to cover routine expenses for people participating in clinical trials. Already, 15 states, including California, require their Medicaid programs to cover these expenses, according to ASCO.

“We believe that the trials can bring extra benefits to patients,” said Monica M. Bertagnolli, MD, of Dana-Farber Cancer Institute, Boston. Dr. Bertagnolli has worked for years to secure Medicaid coverage for expenses connected to clinical trials.

Although Medicaid covers costs of standard care for cancer patients, people enrolled in the program may have concerns about participating in clinical studies, said Dr. Bertagnolli, chair of the Association for Clinical Oncology, which was established by ASCO to promote wider access to cancer care. Having extra medical expenses may be more than these patients can tolerate.

“Many of them just say, ‘I can’t take that financial risk, so I’ll just stay with standard of care,’ “ Dr. Bertagnolli said in an interview.
 

Equity issues

Medicaid has expanded greatly, owing to financial aid provided to states through the Affordable Care Act of 2010.

To date, 38 of 50 U.S. states have accepted federal aid to lift income limits for Medicaid eligibility, according to a tally kept by the nonprofit Kaiser Family Foundation. This Medicaid expansion has given more of the nation’s working poor access to health.care, including cancer treatment. Between 2013 and January 2020, enrollment in Medicaid in expansion states increased by about 12.4 million, according to the Medicaid and CHIP Payment and Access Commission.

Medicaid is the nation’s dominant health insurer. Enrollment has been around 70 million in recent months.

That tops the 61 million enrolled in Medicare, the federal program for people aged 65 and older and those with disabilities. (There’s some overlap between Medicare and Medicaid. About 12.8 million persons were dually eligible for these programs in 2018.) UnitedHealth, a giant private insurer, has about 43 million domestic customers.

Medicaid also serves many of the groups of people for which researchers have been seeking to increase participation in clinical trials. ASCO’s Association for Clinical Oncology and dozens of its partners raised this point in a letter to congressional leaders on Feb. 15, 2020.

“Lack of participation in clinical trials from the Medicaid population means these patients are being excluded from potentially life-saving trials and are not reflected in the outcome of the clinical research,” the groups wrote. “Increased access to clinical trial participation for Medicaid enrollees helps ensure medical research results more accurately capture and reflect the populations of this country.”

The ACA’s Medicaid expansion is working to address some of the racial gaps in insurance coverage, according to a January 2020 report from the nonprofit Commonwealth Fund.

Black and Hispanic adults are almost twice as likely as are White adults to have incomes that are less than 200% of the federal poverty level, according to the Commonwealth Fund report. The report also said that people in these groups reported significantly higher rates of cost-related problems in receiving care before the Medicaid expansion began in 2014.

The uninsured rate for Black adults dropped from 24.4% in 2013 to 14.4% in 2018; the rate for Hispanic adults fell from 40.2% to 24.9%, according to the Commonwealth Fund report.

There are concerns, though, about attempts by some governors to impose onerous restrictions on adults enrolled in Medicaid, Dr. Bertagnolli said. She was president of ASCO in 2018 when the group called on the Centers for Medicare & Medicaid Services to reject state requests to create restrictions that could hinder people’s access to cancer screening or care.

The Trump administration encouraged governors to adopt work requirements. As a result, a dozen states approved these policies, according to a November report from the nonprofit Center on Budget and Policy Priorities. The efforts were blocked by courts.

Data from the limited period of implementation in Arkansas, Michigan, and New Hampshire provide evidence that these kinds of requirements don’t work as intended, according to the CBPP report.

“In all three states, evidence suggests that people who were working and people with serious health needs who should have been eligible for exemptions lost coverage or were at risk of losing coverage due to red tape,” CBPP analysts Jennifer Wagner and Jessica Schubel wrote in their report.

In 2019, The New England Journal of Medicine published an article about the early stages of the Arkansas experiment with Medicaid work rules. Almost 17,000 adults lost their health care coverage in the initial months of implementation, but there appeared to be no significant difference in employment, Benjamin Sommers, MD, PhD, of the Harvard School of Public Health, Boston, and colleagues wrote in their article.

For many people in Arkansas, coverage was lost because of difficulties in reporting compliance with the Medicaid work rule, not because of the employment mandate itself, according to the authors. More than 95% of persons who were targeted by Arkansas’ Medicaid work policy already met its requirements or should have been exempt, they wrote.

Democrats have tended to oppose efforts to attach work requirements, which can include volunteer activities or career training, to Medicaid. Dr. Bertagnolli said there is a need to guard against any future bid to add work requirements to the program.

Extra bureaucratic hurdles may pose an especially tough burden on working adults enrolled in Medicaid, she said.

People who qualify for the program may already be worried about their finances while juggling continued demands of child care and employment, she said. They don’t need to be put at risk of losing access to medical care over administrative rules while undergoing cancer treatment, she said.

“We have to take care of people who are sick. That’s just the way it is,” Dr. Bertagnolli said.

A version of this article first appeared on Medscape.com.

Congress has ordered the holdouts among U.S. states to have their Medicaid programs cover expenses related to participation in certain clinical trials, a move that was hailed by the American Society of Clinical Oncology and other groups as a boost to trials as well as to patients with serious illness who have lower incomes.

A massive wrap-up spending/COVID-19 relief bill that was signed into law Dec. 27 carried with it a mandate on Medicaid. States are ordered to put in place Medicaid payment policies for routine items and services, such as the cost of physician visits or laboratory tests, that are provided in connection with participation in clinical trials for serious and life-threatening conditions. The law includes a January 2022 target date for this coverage through Medicaid.

Medicare and other large insurers already pick up the tab for these kinds of expenses, leaving Medicaid as an outlier, ASCO noted in a press statement. ASCO and other cancer groups have for years pressed Medicaid to cover routine expenses for people participating in clinical trials. Already, 15 states, including California, require their Medicaid programs to cover these expenses, according to ASCO.

“We believe that the trials can bring extra benefits to patients,” said Monica M. Bertagnolli, MD, of Dana-Farber Cancer Institute, Boston. Dr. Bertagnolli has worked for years to secure Medicaid coverage for expenses connected to clinical trials.

Although Medicaid covers costs of standard care for cancer patients, people enrolled in the program may have concerns about participating in clinical studies, said Dr. Bertagnolli, chair of the Association for Clinical Oncology, which was established by ASCO to promote wider access to cancer care. Having extra medical expenses may be more than these patients can tolerate.

“Many of them just say, ‘I can’t take that financial risk, so I’ll just stay with standard of care,’ “ Dr. Bertagnolli said in an interview.
 

Equity issues

Medicaid has expanded greatly, owing to financial aid provided to states through the Affordable Care Act of 2010.

To date, 38 of 50 U.S. states have accepted federal aid to lift income limits for Medicaid eligibility, according to a tally kept by the nonprofit Kaiser Family Foundation. This Medicaid expansion has given more of the nation’s working poor access to health.care, including cancer treatment. Between 2013 and January 2020, enrollment in Medicaid in expansion states increased by about 12.4 million, according to the Medicaid and CHIP Payment and Access Commission.

Medicaid is the nation’s dominant health insurer. Enrollment has been around 70 million in recent months.

That tops the 61 million enrolled in Medicare, the federal program for people aged 65 and older and those with disabilities. (There’s some overlap between Medicare and Medicaid. About 12.8 million persons were dually eligible for these programs in 2018.) UnitedHealth, a giant private insurer, has about 43 million domestic customers.

Medicaid also serves many of the groups of people for which researchers have been seeking to increase participation in clinical trials. ASCO’s Association for Clinical Oncology and dozens of its partners raised this point in a letter to congressional leaders on Feb. 15, 2020.

“Lack of participation in clinical trials from the Medicaid population means these patients are being excluded from potentially life-saving trials and are not reflected in the outcome of the clinical research,” the groups wrote. “Increased access to clinical trial participation for Medicaid enrollees helps ensure medical research results more accurately capture and reflect the populations of this country.”

The ACA’s Medicaid expansion is working to address some of the racial gaps in insurance coverage, according to a January 2020 report from the nonprofit Commonwealth Fund.

Black and Hispanic adults are almost twice as likely as are White adults to have incomes that are less than 200% of the federal poverty level, according to the Commonwealth Fund report. The report also said that people in these groups reported significantly higher rates of cost-related problems in receiving care before the Medicaid expansion began in 2014.

The uninsured rate for Black adults dropped from 24.4% in 2013 to 14.4% in 2018; the rate for Hispanic adults fell from 40.2% to 24.9%, according to the Commonwealth Fund report.

There are concerns, though, about attempts by some governors to impose onerous restrictions on adults enrolled in Medicaid, Dr. Bertagnolli said. She was president of ASCO in 2018 when the group called on the Centers for Medicare & Medicaid Services to reject state requests to create restrictions that could hinder people’s access to cancer screening or care.

The Trump administration encouraged governors to adopt work requirements. As a result, a dozen states approved these policies, according to a November report from the nonprofit Center on Budget and Policy Priorities. The efforts were blocked by courts.

Data from the limited period of implementation in Arkansas, Michigan, and New Hampshire provide evidence that these kinds of requirements don’t work as intended, according to the CBPP report.

“In all three states, evidence suggests that people who were working and people with serious health needs who should have been eligible for exemptions lost coverage or were at risk of losing coverage due to red tape,” CBPP analysts Jennifer Wagner and Jessica Schubel wrote in their report.

In 2019, The New England Journal of Medicine published an article about the early stages of the Arkansas experiment with Medicaid work rules. Almost 17,000 adults lost their health care coverage in the initial months of implementation, but there appeared to be no significant difference in employment, Benjamin Sommers, MD, PhD, of the Harvard School of Public Health, Boston, and colleagues wrote in their article.

For many people in Arkansas, coverage was lost because of difficulties in reporting compliance with the Medicaid work rule, not because of the employment mandate itself, according to the authors. More than 95% of persons who were targeted by Arkansas’ Medicaid work policy already met its requirements or should have been exempt, they wrote.

Democrats have tended to oppose efforts to attach work requirements, which can include volunteer activities or career training, to Medicaid. Dr. Bertagnolli said there is a need to guard against any future bid to add work requirements to the program.

Extra bureaucratic hurdles may pose an especially tough burden on working adults enrolled in Medicaid, she said.

People who qualify for the program may already be worried about their finances while juggling continued demands of child care and employment, she said. They don’t need to be put at risk of losing access to medical care over administrative rules while undergoing cancer treatment, she said.

“We have to take care of people who are sick. That’s just the way it is,” Dr. Bertagnolli said.

A version of this article first appeared on Medscape.com.

Congress has ordered the holdouts among U.S. states to have their Medicaid programs cover expenses related to participation in certain clinical trials, a move that was hailed by the American Society of Clinical Oncology and other groups as a boost to trials as well as to patients with serious illness who have lower incomes.

A massive wrap-up spending/COVID-19 relief bill that was signed into law Dec. 27 carried with it a mandate on Medicaid. States are ordered to put in place Medicaid payment policies for routine items and services, such as the cost of physician visits or laboratory tests, that are provided in connection with participation in clinical trials for serious and life-threatening conditions. The law includes a January 2022 target date for this coverage through Medicaid.

Medicare and other large insurers already pick up the tab for these kinds of expenses, leaving Medicaid as an outlier, ASCO noted in a press statement. ASCO and other cancer groups have for years pressed Medicaid to cover routine expenses for people participating in clinical trials. Already, 15 states, including California, require their Medicaid programs to cover these expenses, according to ASCO.

“We believe that the trials can bring extra benefits to patients,” said Monica M. Bertagnolli, MD, of Dana-Farber Cancer Institute, Boston. Dr. Bertagnolli has worked for years to secure Medicaid coverage for expenses connected to clinical trials.

Although Medicaid covers costs of standard care for cancer patients, people enrolled in the program may have concerns about participating in clinical studies, said Dr. Bertagnolli, chair of the Association for Clinical Oncology, which was established by ASCO to promote wider access to cancer care. Having extra medical expenses may be more than these patients can tolerate.

“Many of them just say, ‘I can’t take that financial risk, so I’ll just stay with standard of care,’ “ Dr. Bertagnolli said in an interview.
 

Equity issues

Medicaid has expanded greatly, owing to financial aid provided to states through the Affordable Care Act of 2010.

To date, 38 of 50 U.S. states have accepted federal aid to lift income limits for Medicaid eligibility, according to a tally kept by the nonprofit Kaiser Family Foundation. This Medicaid expansion has given more of the nation’s working poor access to health.care, including cancer treatment. Between 2013 and January 2020, enrollment in Medicaid in expansion states increased by about 12.4 million, according to the Medicaid and CHIP Payment and Access Commission.

Medicaid is the nation’s dominant health insurer. Enrollment has been around 70 million in recent months.

That tops the 61 million enrolled in Medicare, the federal program for people aged 65 and older and those with disabilities. (There’s some overlap between Medicare and Medicaid. About 12.8 million persons were dually eligible for these programs in 2018.) UnitedHealth, a giant private insurer, has about 43 million domestic customers.

Medicaid also serves many of the groups of people for which researchers have been seeking to increase participation in clinical trials. ASCO’s Association for Clinical Oncology and dozens of its partners raised this point in a letter to congressional leaders on Feb. 15, 2020.

“Lack of participation in clinical trials from the Medicaid population means these patients are being excluded from potentially life-saving trials and are not reflected in the outcome of the clinical research,” the groups wrote. “Increased access to clinical trial participation for Medicaid enrollees helps ensure medical research results more accurately capture and reflect the populations of this country.”

The ACA’s Medicaid expansion is working to address some of the racial gaps in insurance coverage, according to a January 2020 report from the nonprofit Commonwealth Fund.

Black and Hispanic adults are almost twice as likely as are White adults to have incomes that are less than 200% of the federal poverty level, according to the Commonwealth Fund report. The report also said that people in these groups reported significantly higher rates of cost-related problems in receiving care before the Medicaid expansion began in 2014.

The uninsured rate for Black adults dropped from 24.4% in 2013 to 14.4% in 2018; the rate for Hispanic adults fell from 40.2% to 24.9%, according to the Commonwealth Fund report.

There are concerns, though, about attempts by some governors to impose onerous restrictions on adults enrolled in Medicaid, Dr. Bertagnolli said. She was president of ASCO in 2018 when the group called on the Centers for Medicare & Medicaid Services to reject state requests to create restrictions that could hinder people’s access to cancer screening or care.

The Trump administration encouraged governors to adopt work requirements. As a result, a dozen states approved these policies, according to a November report from the nonprofit Center on Budget and Policy Priorities. The efforts were blocked by courts.

Data from the limited period of implementation in Arkansas, Michigan, and New Hampshire provide evidence that these kinds of requirements don’t work as intended, according to the CBPP report.

“In all three states, evidence suggests that people who were working and people with serious health needs who should have been eligible for exemptions lost coverage or were at risk of losing coverage due to red tape,” CBPP analysts Jennifer Wagner and Jessica Schubel wrote in their report.

In 2019, The New England Journal of Medicine published an article about the early stages of the Arkansas experiment with Medicaid work rules. Almost 17,000 adults lost their health care coverage in the initial months of implementation, but there appeared to be no significant difference in employment, Benjamin Sommers, MD, PhD, of the Harvard School of Public Health, Boston, and colleagues wrote in their article.

For many people in Arkansas, coverage was lost because of difficulties in reporting compliance with the Medicaid work rule, not because of the employment mandate itself, according to the authors. More than 95% of persons who were targeted by Arkansas’ Medicaid work policy already met its requirements or should have been exempt, they wrote.

Democrats have tended to oppose efforts to attach work requirements, which can include volunteer activities or career training, to Medicaid. Dr. Bertagnolli said there is a need to guard against any future bid to add work requirements to the program.

Extra bureaucratic hurdles may pose an especially tough burden on working adults enrolled in Medicaid, she said.

People who qualify for the program may already be worried about their finances while juggling continued demands of child care and employment, she said. They don’t need to be put at risk of losing access to medical care over administrative rules while undergoing cancer treatment, she said.

“We have to take care of people who are sick. That’s just the way it is,” Dr. Bertagnolli said.

A version of this article first appeared on Medscape.com.