Research Letters

Urinary catheter use can be associated with urinary tract infections, delirium, trauma, and immobility.[1] Evidence‐based strategies to reduce inappropriate use are available[2]; however, their application across centers is variable.[3] We aimed to characterize the prevalence and indication for catheters among Canadian teaching hospitals with and without catheter reduction programs.

METHODS

Twelve of 17 postgraduate internal medicine training program directors agreed to participate, and 9 Canadian teaching hospitals enrolled in this prevalence study of urinary catheter use among medical inpatients. Data collection used a standardized form and took place over 5 consecutive weekdays during August 2015. Each site anonymously collected the total number of catheters, total number of inpatient‐days, and indications for use from either the bedside nurse or physician. Appropriate clinical indications were based on the 2009 guidelines from the Healthcare Infection Control Practice Advisory Committee.[4] Potentially inappropriate indications included urine output measurement in noncritically ill patients, and other or unknown indications.[4, 5] A catheter reduction program was defined as the presence of a structured system to monitor and reduce use via: nurse‐directed catheter removal, audit‐feedback of use to providers, physician reminders, and/or automatic stop orders.

The primary outcome was the number of catheter days per 100 inpatient‐days. We used generalized estimating equations to adjust the 95% confidence interval (CI) and P value to account for hospital‐level clustering of the responses. The P values are from a 2‐tailed Wald test against the true log scale parameter being equal to zero. The analysis was performed using R version 3.0.2 using the geepack package (Free Software Foundation, Boston, MA).

The McGill University Health Centre Research Ethics Board approved this study with concomitant authorization at participating sites.

RESULTS

The characteristics of participating hospitals are displayed in Table 1. Those with active catheter reduction programs reported established systems for monitoring catheter placement, duration, and catheter‐associated urinary tract infections. More than half of the hospitals lacked a catheter reduction program. Overall, catheters were present on 13.6% of patient‐days (range, 2.3%32.4%). Centers without reduction programs reported higher rates of catheter use both overall and for potentially inappropriate indications. After adjustment for clustering, those with a formal intervention had 8.8 fewer catheter days per 100 patient‐days as compared to those without (9.8 [95% CI: 6.0‐15.6] vs 18.6 [95% CI: 13.0‐26.1], P = 0.03). This meant that the odds of a urinary catheter being present were 2 times (95% CI: 1.0‐3.4) greater in hospitals without reduction programs. Differences in appropriate catheter use did not reach statistical significance.

DISCUSSION

Despite the availability of consensus guidelines for appropriate use and the efforts of movements like Choosing Wisely, many Canadian teaching hospitals have not yet established a urinary catheter reduction program for medical inpatients. Our findings are similar to 2 non‐Canadian studies, which demonstrated that fewer than half of hospitals had implemented control measures.[4, 6] In contrast to those other studies, our study demonstrated that hospitals that employed control measures had reduced rates of catheter use suggesting that systematic, structured efforts are necessary to improve practice.[7, 8]

Ours is the first nation‐wide study in Canada to report urinary catheter rates and the effect of associated reduction programs. Data from the National Healthcare Safety Network suggest our Canadian estimates of urinary catheter rates in medical inpatients are similar to those of the United States (13.6 vs 14.8 catheter days per 100 inpatient‐days, respectively, for general medical inpatients).[9, 10]

Several limitations of this study warrant discussion. First, we sampled only academic institutions at 1 time point, which may not represent annualized rates or rates in community hospitals. However, our findings are similar to those reported in previous studies.[10] Second, our method of consecutive daily audits may have caused individuals to change their behavior knowing that they were being observed, resulting in lower catheter utilization than would have been otherwise present and biasing our estimates of catheter overuse downward. Third, we collected point prevalence data, limiting our ability to make inferences on causality. The key factor(s) contributing to observed differences between hospitals remains unknown. However, pre‐post intervention data available for 3 hospitals suggest that improvements followed active catheter reduction efforts.[7, 8] Fourth, we were unable to obtain outcome data such as catheter‐associated urinary tract infection, delirium, or fall rates. However, catheter reduction is widely recognized as an important first step to reducing preventable harm for hospital patients.

We suggest that the broader uptake of structured models of care that promote early discontinuation of urinary catheters on medical wards is needed to improve their appropriateness. Fortunately, it appears as though a variety of models are effective. Therefore, when it comes to adopting Choosing Wisely's less is more philosophy toward urinary catheter utilization, we suggest that less time be allowed to pass before more proven and structured interventions are universally implemented.

Urinary catheter use can be associated with urinary tract infections, delirium, trauma, and immobility.[1] Evidence‐based strategies to reduce inappropriate use are available[2]; however, their application across centers is variable.[3] We aimed to characterize the prevalence and indication for catheters among Canadian teaching hospitals with and without catheter reduction programs.

METHODS

Twelve of 17 postgraduate internal medicine training program directors agreed to participate, and 9 Canadian teaching hospitals enrolled in this prevalence study of urinary catheter use among medical inpatients. Data collection used a standardized form and took place over 5 consecutive weekdays during August 2015. Each site anonymously collected the total number of catheters, total number of inpatient‐days, and indications for use from either the bedside nurse or physician. Appropriate clinical indications were based on the 2009 guidelines from the Healthcare Infection Control Practice Advisory Committee.[4] Potentially inappropriate indications included urine output measurement in noncritically ill patients, and other or unknown indications.[4, 5] A catheter reduction program was defined as the presence of a structured system to monitor and reduce use via: nurse‐directed catheter removal, audit‐feedback of use to providers, physician reminders, and/or automatic stop orders.

The primary outcome was the number of catheter days per 100 inpatient‐days. We used generalized estimating equations to adjust the 95% confidence interval (CI) and P value to account for hospital‐level clustering of the responses. The P values are from a 2‐tailed Wald test against the true log scale parameter being equal to zero. The analysis was performed using R version 3.0.2 using the geepack package (Free Software Foundation, Boston, MA).

The McGill University Health Centre Research Ethics Board approved this study with concomitant authorization at participating sites.

RESULTS

The characteristics of participating hospitals are displayed in Table 1. Those with active catheter reduction programs reported established systems for monitoring catheter placement, duration, and catheter‐associated urinary tract infections. More than half of the hospitals lacked a catheter reduction program. Overall, catheters were present on 13.6% of patient‐days (range, 2.3%32.4%). Centers without reduction programs reported higher rates of catheter use both overall and for potentially inappropriate indications. After adjustment for clustering, those with a formal intervention had 8.8 fewer catheter days per 100 patient‐days as compared to those without (9.8 [95% CI: 6.0‐15.6] vs 18.6 [95% CI: 13.0‐26.1], P = 0.03). This meant that the odds of a urinary catheter being present were 2 times (95% CI: 1.0‐3.4) greater in hospitals without reduction programs. Differences in appropriate catheter use did not reach statistical significance.

DISCUSSION

Despite the availability of consensus guidelines for appropriate use and the efforts of movements like Choosing Wisely, many Canadian teaching hospitals have not yet established a urinary catheter reduction program for medical inpatients. Our findings are similar to 2 non‐Canadian studies, which demonstrated that fewer than half of hospitals had implemented control measures.[4, 6] In contrast to those other studies, our study demonstrated that hospitals that employed control measures had reduced rates of catheter use suggesting that systematic, structured efforts are necessary to improve practice.[7, 8]

Ours is the first nation‐wide study in Canada to report urinary catheter rates and the effect of associated reduction programs. Data from the National Healthcare Safety Network suggest our Canadian estimates of urinary catheter rates in medical inpatients are similar to those of the United States (13.6 vs 14.8 catheter days per 100 inpatient‐days, respectively, for general medical inpatients).[9, 10]

Several limitations of this study warrant discussion. First, we sampled only academic institutions at 1 time point, which may not represent annualized rates or rates in community hospitals. However, our findings are similar to those reported in previous studies.[10] Second, our method of consecutive daily audits may have caused individuals to change their behavior knowing that they were being observed, resulting in lower catheter utilization than would have been otherwise present and biasing our estimates of catheter overuse downward. Third, we collected point prevalence data, limiting our ability to make inferences on causality. The key factor(s) contributing to observed differences between hospitals remains unknown. However, pre‐post intervention data available for 3 hospitals suggest that improvements followed active catheter reduction efforts.[7, 8] Fourth, we were unable to obtain outcome data such as catheter‐associated urinary tract infection, delirium, or fall rates. However, catheter reduction is widely recognized as an important first step to reducing preventable harm for hospital patients.

We suggest that the broader uptake of structured models of care that promote early discontinuation of urinary catheters on medical wards is needed to improve their appropriateness. Fortunately, it appears as though a variety of models are effective. Therefore, when it comes to adopting Choosing Wisely's less is more philosophy toward urinary catheter utilization, we suggest that less time be allowed to pass before more proven and structured interventions are universally implemented.

Urinary catheter use can be associated with urinary tract infections, delirium, trauma, and immobility.[1] Evidence‐based strategies to reduce inappropriate use are available[2]; however, their application across centers is variable.[3] We aimed to characterize the prevalence and indication for catheters among Canadian teaching hospitals with and without catheter reduction programs.

METHODS

Twelve of 17 postgraduate internal medicine training program directors agreed to participate, and 9 Canadian teaching hospitals enrolled in this prevalence study of urinary catheter use among medical inpatients. Data collection used a standardized form and took place over 5 consecutive weekdays during August 2015. Each site anonymously collected the total number of catheters, total number of inpatient‐days, and indications for use from either the bedside nurse or physician. Appropriate clinical indications were based on the 2009 guidelines from the Healthcare Infection Control Practice Advisory Committee.[4] Potentially inappropriate indications included urine output measurement in noncritically ill patients, and other or unknown indications.[4, 5] A catheter reduction program was defined as the presence of a structured system to monitor and reduce use via: nurse‐directed catheter removal, audit‐feedback of use to providers, physician reminders, and/or automatic stop orders.

The primary outcome was the number of catheter days per 100 inpatient‐days. We used generalized estimating equations to adjust the 95% confidence interval (CI) and P value to account for hospital‐level clustering of the responses. The P values are from a 2‐tailed Wald test against the true log scale parameter being equal to zero. The analysis was performed using R version 3.0.2 using the geepack package (Free Software Foundation, Boston, MA).

The McGill University Health Centre Research Ethics Board approved this study with concomitant authorization at participating sites.

RESULTS

The characteristics of participating hospitals are displayed in Table 1. Those with active catheter reduction programs reported established systems for monitoring catheter placement, duration, and catheter‐associated urinary tract infections. More than half of the hospitals lacked a catheter reduction program. Overall, catheters were present on 13.6% of patient‐days (range, 2.3%32.4%). Centers without reduction programs reported higher rates of catheter use both overall and for potentially inappropriate indications. After adjustment for clustering, those with a formal intervention had 8.8 fewer catheter days per 100 patient‐days as compared to those without (9.8 [95% CI: 6.0‐15.6] vs 18.6 [95% CI: 13.0‐26.1], P = 0.03). This meant that the odds of a urinary catheter being present were 2 times (95% CI: 1.0‐3.4) greater in hospitals without reduction programs. Differences in appropriate catheter use did not reach statistical significance.

DISCUSSION

Despite the availability of consensus guidelines for appropriate use and the efforts of movements like Choosing Wisely, many Canadian teaching hospitals have not yet established a urinary catheter reduction program for medical inpatients. Our findings are similar to 2 non‐Canadian studies, which demonstrated that fewer than half of hospitals had implemented control measures.[4, 6] In contrast to those other studies, our study demonstrated that hospitals that employed control measures had reduced rates of catheter use suggesting that systematic, structured efforts are necessary to improve practice.[7, 8]

Ours is the first nation‐wide study in Canada to report urinary catheter rates and the effect of associated reduction programs. Data from the National Healthcare Safety Network suggest our Canadian estimates of urinary catheter rates in medical inpatients are similar to those of the United States (13.6 vs 14.8 catheter days per 100 inpatient‐days, respectively, for general medical inpatients).[9, 10]

Several limitations of this study warrant discussion. First, we sampled only academic institutions at 1 time point, which may not represent annualized rates or rates in community hospitals. However, our findings are similar to those reported in previous studies.[10] Second, our method of consecutive daily audits may have caused individuals to change their behavior knowing that they were being observed, resulting in lower catheter utilization than would have been otherwise present and biasing our estimates of catheter overuse downward. Third, we collected point prevalence data, limiting our ability to make inferences on causality. The key factor(s) contributing to observed differences between hospitals remains unknown. However, pre‐post intervention data available for 3 hospitals suggest that improvements followed active catheter reduction efforts.[7, 8] Fourth, we were unable to obtain outcome data such as catheter‐associated urinary tract infection, delirium, or fall rates. However, catheter reduction is widely recognized as an important first step to reducing preventable harm for hospital patients.

We suggest that the broader uptake of structured models of care that promote early discontinuation of urinary catheters on medical wards is needed to improve their appropriateness. Fortunately, it appears as though a variety of models are effective. Therefore, when it comes to adopting Choosing Wisely's less is more philosophy toward urinary catheter utilization, we suggest that less time be allowed to pass before more proven and structured interventions are universally implemented.

Physiologic monitor alarms are an inescapable part of the soundtrack for hospitals. Data from primarily adult hospitals have shown that alarms occur at high rates, and most alarms are not actionable.[1] Small studies have suggested that high alarm rates can lead to alarm fatigue.[2, 3] To prioritize alarm types to target in future intervention studies, in this study we aimed to investigate the alarm rates on all inpatient units and the most common causes of alarms at a children's hospital.

METHODS

This was a cross‐sectional study of audible physiologic monitor alarms at Cincinnati Children's Hospital Medical Center (CCHMC) over 7 consecutive days during August 2014. CCHMC is a 522‐bed free‐standing children's hospital. Inpatient beds are equipped with GE Healthcare (Little Chalfont, United Kingdom) bedside monitors (models Dash 3000, 4000, and 5000, and Solar 8000). Age‐specific vital sign parameters were employed for monitors on all units.

We obtained date, time, and type of alarm from bedside physiologic monitors using Connexall middleware (GlobeStar Systems, Toronto, Ontario, Canada).

We determined unit census using the electronic health records for the time period concurrent with the alarm data collection. Given previously described variation in hospital census over the day,[4] we used 4 daily census measurements (6:00 _am, 12:00 _pm, 6:00 _pm, and 11:00 _pm) rather than 1 single measurement to more accurately reflect the hospital census.

The CCHMC Institutional Review Board determined this work to be not human subjects research.

RESULTS

There were a total of 220,813 audible alarms over 1 week. Median alarm rate per patient‐day by unit ranged from 30.4 to 228.5; the highest alarm rates occurred in the cardiac intensive care unit, with a median of 228.5 (interquartile range [IQR], 193275) followed by the pediatric intensive care unit (172.4; IQR, 141188) (Figure 1). The average alarm rate was significantly different among the units (P < 0.01).

Figure 1

Technical alarms (eg, alarms for artifact, lead failure), comprised 33% of the total number of alarms. The remaining 67% of alarms were for clinical conditions, the most common of which was low oxygen saturation (30% of clinical alarms) (Figure 2).

Figure 2

DISCUSSION

We described alarm rates and causes over multiple units at a large children's hospital. To our knowledge, this is the first description of alarm rates across multiple pediatric inpatient units. Alarm counts were high even for the general units, indicating that a nurse taking care of 4 monitored patients would need to process a physiologic monitor alarm every 4 minutes on average, in addition to other sources of alarms such as infusion pumps.

Alarm rates were highest in the intensive care unit areas, which may be attributable to both higher rates of monitoring and sicker patients. Importantly, however, alarms were quite high and variable on the acute care units. This suggests that factors other than patient acuity may have substantial influence on alarm rates.

Technical alarms, alarms that do not indicate a change in patient condition, accounted for the largest percentage of alarms during the study period. This is consistent with prior literature that has suggested that regular electrode replacement, which decreases technical alarms, can be effective in reducing alarm rates.[5, 6] The most common vital sign change to cause alarms was low oxygen saturation, followed by elevated heart rate and elevated respiratory rate. Whereas in most healthy patients, certain low oxygen levels would prompt initiation of supplemental oxygen, there are many conditions in which elevated heart rate and respiratory rate may not require titration of any particular therapy. These may be potential intervention targets for hospitals trying to improve alarm rates.

CONCLUSION

Alarm rates at a single children's hospital varied depending on the unit. Strategies targeted at reducing technical alarms and reducing nonactionable clinical alarms for low oxygen saturation, high heart rate, and high respiratory rate may offer the greatest opportunity to reduce alarm rates.

Physiologic monitor alarms are an inescapable part of the soundtrack for hospitals. Data from primarily adult hospitals have shown that alarms occur at high rates, and most alarms are not actionable.[1] Small studies have suggested that high alarm rates can lead to alarm fatigue.[2, 3] To prioritize alarm types to target in future intervention studies, in this study we aimed to investigate the alarm rates on all inpatient units and the most common causes of alarms at a children's hospital.

METHODS

This was a cross‐sectional study of audible physiologic monitor alarms at Cincinnati Children's Hospital Medical Center (CCHMC) over 7 consecutive days during August 2014. CCHMC is a 522‐bed free‐standing children's hospital. Inpatient beds are equipped with GE Healthcare (Little Chalfont, United Kingdom) bedside monitors (models Dash 3000, 4000, and 5000, and Solar 8000). Age‐specific vital sign parameters were employed for monitors on all units.

We obtained date, time, and type of alarm from bedside physiologic monitors using Connexall middleware (GlobeStar Systems, Toronto, Ontario, Canada).

We determined unit census using the electronic health records for the time period concurrent with the alarm data collection. Given previously described variation in hospital census over the day,[4] we used 4 daily census measurements (6:00 _am, 12:00 _pm, 6:00 _pm, and 11:00 _pm) rather than 1 single measurement to more accurately reflect the hospital census.

The CCHMC Institutional Review Board determined this work to be not human subjects research.

RESULTS

There were a total of 220,813 audible alarms over 1 week. Median alarm rate per patient‐day by unit ranged from 30.4 to 228.5; the highest alarm rates occurred in the cardiac intensive care unit, with a median of 228.5 (interquartile range [IQR], 193275) followed by the pediatric intensive care unit (172.4; IQR, 141188) (Figure 1). The average alarm rate was significantly different among the units (P < 0.01).

Figure 1

Technical alarms (eg, alarms for artifact, lead failure), comprised 33% of the total number of alarms. The remaining 67% of alarms were for clinical conditions, the most common of which was low oxygen saturation (30% of clinical alarms) (Figure 2).

Figure 2

DISCUSSION

We described alarm rates and causes over multiple units at a large children's hospital. To our knowledge, this is the first description of alarm rates across multiple pediatric inpatient units. Alarm counts were high even for the general units, indicating that a nurse taking care of 4 monitored patients would need to process a physiologic monitor alarm every 4 minutes on average, in addition to other sources of alarms such as infusion pumps.

Alarm rates were highest in the intensive care unit areas, which may be attributable to both higher rates of monitoring and sicker patients. Importantly, however, alarms were quite high and variable on the acute care units. This suggests that factors other than patient acuity may have substantial influence on alarm rates.

Technical alarms, alarms that do not indicate a change in patient condition, accounted for the largest percentage of alarms during the study period. This is consistent with prior literature that has suggested that regular electrode replacement, which decreases technical alarms, can be effective in reducing alarm rates.[5, 6] The most common vital sign change to cause alarms was low oxygen saturation, followed by elevated heart rate and elevated respiratory rate. Whereas in most healthy patients, certain low oxygen levels would prompt initiation of supplemental oxygen, there are many conditions in which elevated heart rate and respiratory rate may not require titration of any particular therapy. These may be potential intervention targets for hospitals trying to improve alarm rates.

CONCLUSION

Alarm rates at a single children's hospital varied depending on the unit. Strategies targeted at reducing technical alarms and reducing nonactionable clinical alarms for low oxygen saturation, high heart rate, and high respiratory rate may offer the greatest opportunity to reduce alarm rates.

Physiologic monitor alarms are an inescapable part of the soundtrack for hospitals. Data from primarily adult hospitals have shown that alarms occur at high rates, and most alarms are not actionable.[1] Small studies have suggested that high alarm rates can lead to alarm fatigue.[2, 3] To prioritize alarm types to target in future intervention studies, in this study we aimed to investigate the alarm rates on all inpatient units and the most common causes of alarms at a children's hospital.

METHODS

This was a cross‐sectional study of audible physiologic monitor alarms at Cincinnati Children's Hospital Medical Center (CCHMC) over 7 consecutive days during August 2014. CCHMC is a 522‐bed free‐standing children's hospital. Inpatient beds are equipped with GE Healthcare (Little Chalfont, United Kingdom) bedside monitors (models Dash 3000, 4000, and 5000, and Solar 8000). Age‐specific vital sign parameters were employed for monitors on all units.

We obtained date, time, and type of alarm from bedside physiologic monitors using Connexall middleware (GlobeStar Systems, Toronto, Ontario, Canada).

We determined unit census using the electronic health records for the time period concurrent with the alarm data collection. Given previously described variation in hospital census over the day,[4] we used 4 daily census measurements (6:00 _am, 12:00 _pm, 6:00 _pm, and 11:00 _pm) rather than 1 single measurement to more accurately reflect the hospital census.

The CCHMC Institutional Review Board determined this work to be not human subjects research.

RESULTS

There were a total of 220,813 audible alarms over 1 week. Median alarm rate per patient‐day by unit ranged from 30.4 to 228.5; the highest alarm rates occurred in the cardiac intensive care unit, with a median of 228.5 (interquartile range [IQR], 193275) followed by the pediatric intensive care unit (172.4; IQR, 141188) (Figure 1). The average alarm rate was significantly different among the units (P < 0.01).

Figure 1

Technical alarms (eg, alarms for artifact, lead failure), comprised 33% of the total number of alarms. The remaining 67% of alarms were for clinical conditions, the most common of which was low oxygen saturation (30% of clinical alarms) (Figure 2).

Figure 2

DISCUSSION

We described alarm rates and causes over multiple units at a large children's hospital. To our knowledge, this is the first description of alarm rates across multiple pediatric inpatient units. Alarm counts were high even for the general units, indicating that a nurse taking care of 4 monitored patients would need to process a physiologic monitor alarm every 4 minutes on average, in addition to other sources of alarms such as infusion pumps.

Alarm rates were highest in the intensive care unit areas, which may be attributable to both higher rates of monitoring and sicker patients. Importantly, however, alarms were quite high and variable on the acute care units. This suggests that factors other than patient acuity may have substantial influence on alarm rates.

Technical alarms, alarms that do not indicate a change in patient condition, accounted for the largest percentage of alarms during the study period. This is consistent with prior literature that has suggested that regular electrode replacement, which decreases technical alarms, can be effective in reducing alarm rates.[5, 6] The most common vital sign change to cause alarms was low oxygen saturation, followed by elevated heart rate and elevated respiratory rate. Whereas in most healthy patients, certain low oxygen levels would prompt initiation of supplemental oxygen, there are many conditions in which elevated heart rate and respiratory rate may not require titration of any particular therapy. These may be potential intervention targets for hospitals trying to improve alarm rates.

CONCLUSION

Alarm rates at a single children's hospital varied depending on the unit. Strategies targeted at reducing technical alarms and reducing nonactionable clinical alarms for low oxygen saturation, high heart rate, and high respiratory rate may offer the greatest opportunity to reduce alarm rates.

The Centers for Medicare & Medicaid Services (CMS) have sought to reduce readmissions in the 30 days following hospital discharge through penalties applied to hospitals with readmission rates that are higher than expected. Expected readmission rates for Medicare fee‐for‐service beneficiaries are calculated from models that use patient‐level administrative data to account for patient morbidities. Readmitted patients are defined as those who are discharged from the hospital alive and then rehospitalized at any acute care facility within 30 days of discharge. These models explicitly exclude sociodemographic variables that may impact quality of and access to outpatient care. Specific exclusions are also applied based on diagnosis codes so as to avoid penalizing hospitals for rehospitalizations that are likely to have been planned.

More recently, a hospital‐wide readmission measure has been developed, which seeks to provide a comprehensive view of each hospital's readmission rate by including the vast majority of Medicare patients. Like the condition‐specific readmission measures, the hospital‐wide readmission measure also excludes sociodemographic variables and incorporates specific condition‐based exclusions so as to avoid counting planned rehospitalizations (e.g., an admission for cholecystectomy following an admission for biliary sepsis). Although not currently used for pay‐for‐performance, this measure has been included in the CMS Star Report along with other readmission measures.[1] CMS does not currently disseminate a hospital‐wide mortality measure, but does disseminate hospital‐level adjusted 30‐day mortality rates for Medicare beneficiaries with discharge diagnoses of stroke, heart failure, myocardial infarction (MI), chronic obstructive pulmonary disease (COPD) and pneumonia, and principal procedure of coronary artery bypass grafting (CABG).

It is conceivable that aggressive efforts to reduce readmissions might delay life‐saving acute care in some scenarios,[2] and there is prior evidence that heart failure readmissions are inversely (but weakly) related to heart failure mortality.[3] It is also plausible that keeping tenuous patients alive until discharge might result in higher readmission rates. We sought to examine the relationship between hospital‐wide adjusted 30‐day readmissions and death rates across the acute care hospitals in the United States. Lacking a measure of hospital‐wide death rates, we examined the relation between hospital‐wide readmissions and each of the 6 condition‐specific mortality measures. For comparison, we also examined the relationships between condition‐specific readmission rates and mortality rates.

METHODS

We used publically available data published by CMS from July 1, 2011 through June 30, 2014.[4] These data are provided at the hospital level, without any patient‐level data. We included 4452 acute care facilities based on having hospital‐wide readmission rates, but not all facilities contributed data for each mortality measure. We excluded from analysis on a measure‐by‐measure basis those facilities for which outcomes were absent, without imputing missing outcome measures, because low volume of a given condition was the main reason for not reporting a measure. For each mortality measure, we constructed a logistic regression model to quantify the odds of performing in the lowest (best) mortality tertile as a function of hospital‐wide readmission tertile. To account for patient volumes, we included in each model the number of eligible patients at each hospital with the specified condition. We repeated these analyses using condition‐specific readmission rates (rather than the hospital‐wide readmission rates) as the independent variable. Specifications for CMS models for mortality and readmissions are publically available.[5]

RESULTS

After adjustment for patient volumes, hospitals in the highest hospital‐wide readmission tertile were more likely to perform in the lowest (best) mortality tertile for 3 of the 6 mortality measures: heart failure, COPD, and stroke (P < 0.001 for all). For MI, CABG and pneumonia, there was no significant association between high hospital‐wide readmission rates and low mortality (Table 1). Using condition‐specific readmission rates, there remained an inverse association between readmissions and mortality for heart failure and stroke, but not for COPD. In contrast, hospitals with the highest CABG‐specific readmission rates were significantly less likely to have low CABG‐specific mortality (P < 0.001).

DISCUSSION

We found that higher hospital‐wide readmission rates were associated with lower mortality at the hospital level for 3 of the 6 mortality measures we examined. The findings for heart failure parallel the findings of Krumholz and colleagues who examined 3 of these 6 measures (MI, pneumonia, and heart failure) in relation to readmissions for these specific populations.[3] This prior analysis, however, did not include the 3 more recently reported mortality measures (COPD, stroke, and CABG) and did not use hospital‐wide readmissions.

Causal mechanisms underlying the associations between mortality and readmission at the hospital level deserve further exploration. It is certainly possible that global efforts to keep patients out of the hospital might, in some instances, place patients at risk by delaying necessary acute care.[2] It is also possible that unmeasured variables, particularly access to hospice and palliative care services that might facilitate good deaths, could be associated with both reduced readmissions and higher death rates. Additionally, because deceased patients cannot be readmitted, one might expect that readmissions and mortality might be inversely associated, particularly for conditions with a high postdischarge mortality rate. Similarly, a hospital that does a particularly good job keeping chronically ill patients alive until discharge might exhibit a higher readmission rate than a hospital that is less adept at keeping tenuous patients alive until discharge.

Regardless of the mechanisms of these findings, we present these data to raise the concern that using readmission rates, particularly hospital‐wide readmission rates, as a measure of hospital quality is inherently problematic. It is particularly problematic that CMS has applied equal weight to readmissions and mortality in the Star Report.[1] High readmission rates may result from complications and poor handoffs, but may also stem from the legitimate need to care for chronically ill patients in a high‐intensity setting, particularly fragile patients who have been kept alive against the odds. In conclusion, caution is warranted in viewing readmissions as a quality metric until the associations we describe are better explained using patient‐level data and more robust adjustment than is possible with these publically available data.

Disclosures: Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. There was no financial support for this work. Contributions of the authors are as follows: drafting manuscript (Brotman), revision of manuscript for important intellectual content (brotman, Hoyer, Lepley, Deutschendorf, Leung), acquisition of data (Deutschendorf, Leung, Lepley), interpretation of data (Brotman, Hoyer, Lepley, Deutschendorf, Leung), data analysis (Brotman, Hoyer).

The Centers for Medicare & Medicaid Services (CMS) have sought to reduce readmissions in the 30 days following hospital discharge through penalties applied to hospitals with readmission rates that are higher than expected. Expected readmission rates for Medicare fee‐for‐service beneficiaries are calculated from models that use patient‐level administrative data to account for patient morbidities. Readmitted patients are defined as those who are discharged from the hospital alive and then rehospitalized at any acute care facility within 30 days of discharge. These models explicitly exclude sociodemographic variables that may impact quality of and access to outpatient care. Specific exclusions are also applied based on diagnosis codes so as to avoid penalizing hospitals for rehospitalizations that are likely to have been planned.

More recently, a hospital‐wide readmission measure has been developed, which seeks to provide a comprehensive view of each hospital's readmission rate by including the vast majority of Medicare patients. Like the condition‐specific readmission measures, the hospital‐wide readmission measure also excludes sociodemographic variables and incorporates specific condition‐based exclusions so as to avoid counting planned rehospitalizations (e.g., an admission for cholecystectomy following an admission for biliary sepsis). Although not currently used for pay‐for‐performance, this measure has been included in the CMS Star Report along with other readmission measures.[1] CMS does not currently disseminate a hospital‐wide mortality measure, but does disseminate hospital‐level adjusted 30‐day mortality rates for Medicare beneficiaries with discharge diagnoses of stroke, heart failure, myocardial infarction (MI), chronic obstructive pulmonary disease (COPD) and pneumonia, and principal procedure of coronary artery bypass grafting (CABG).

It is conceivable that aggressive efforts to reduce readmissions might delay life‐saving acute care in some scenarios,[2] and there is prior evidence that heart failure readmissions are inversely (but weakly) related to heart failure mortality.[3] It is also plausible that keeping tenuous patients alive until discharge might result in higher readmission rates. We sought to examine the relationship between hospital‐wide adjusted 30‐day readmissions and death rates across the acute care hospitals in the United States. Lacking a measure of hospital‐wide death rates, we examined the relation between hospital‐wide readmissions and each of the 6 condition‐specific mortality measures. For comparison, we also examined the relationships between condition‐specific readmission rates and mortality rates.

METHODS

We used publically available data published by CMS from July 1, 2011 through June 30, 2014.[4] These data are provided at the hospital level, without any patient‐level data. We included 4452 acute care facilities based on having hospital‐wide readmission rates, but not all facilities contributed data for each mortality measure. We excluded from analysis on a measure‐by‐measure basis those facilities for which outcomes were absent, without imputing missing outcome measures, because low volume of a given condition was the main reason for not reporting a measure. For each mortality measure, we constructed a logistic regression model to quantify the odds of performing in the lowest (best) mortality tertile as a function of hospital‐wide readmission tertile. To account for patient volumes, we included in each model the number of eligible patients at each hospital with the specified condition. We repeated these analyses using condition‐specific readmission rates (rather than the hospital‐wide readmission rates) as the independent variable. Specifications for CMS models for mortality and readmissions are publically available.[5]

RESULTS

After adjustment for patient volumes, hospitals in the highest hospital‐wide readmission tertile were more likely to perform in the lowest (best) mortality tertile for 3 of the 6 mortality measures: heart failure, COPD, and stroke (P < 0.001 for all). For MI, CABG and pneumonia, there was no significant association between high hospital‐wide readmission rates and low mortality (Table 1). Using condition‐specific readmission rates, there remained an inverse association between readmissions and mortality for heart failure and stroke, but not for COPD. In contrast, hospitals with the highest CABG‐specific readmission rates were significantly less likely to have low CABG‐specific mortality (P < 0.001).

DISCUSSION

We found that higher hospital‐wide readmission rates were associated with lower mortality at the hospital level for 3 of the 6 mortality measures we examined. The findings for heart failure parallel the findings of Krumholz and colleagues who examined 3 of these 6 measures (MI, pneumonia, and heart failure) in relation to readmissions for these specific populations.[3] This prior analysis, however, did not include the 3 more recently reported mortality measures (COPD, stroke, and CABG) and did not use hospital‐wide readmissions.

Causal mechanisms underlying the associations between mortality and readmission at the hospital level deserve further exploration. It is certainly possible that global efforts to keep patients out of the hospital might, in some instances, place patients at risk by delaying necessary acute care.[2] It is also possible that unmeasured variables, particularly access to hospice and palliative care services that might facilitate good deaths, could be associated with both reduced readmissions and higher death rates. Additionally, because deceased patients cannot be readmitted, one might expect that readmissions and mortality might be inversely associated, particularly for conditions with a high postdischarge mortality rate. Similarly, a hospital that does a particularly good job keeping chronically ill patients alive until discharge might exhibit a higher readmission rate than a hospital that is less adept at keeping tenuous patients alive until discharge.

Regardless of the mechanisms of these findings, we present these data to raise the concern that using readmission rates, particularly hospital‐wide readmission rates, as a measure of hospital quality is inherently problematic. It is particularly problematic that CMS has applied equal weight to readmissions and mortality in the Star Report.[1] High readmission rates may result from complications and poor handoffs, but may also stem from the legitimate need to care for chronically ill patients in a high‐intensity setting, particularly fragile patients who have been kept alive against the odds. In conclusion, caution is warranted in viewing readmissions as a quality metric until the associations we describe are better explained using patient‐level data and more robust adjustment than is possible with these publically available data.

Disclosures: Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. There was no financial support for this work. Contributions of the authors are as follows: drafting manuscript (Brotman), revision of manuscript for important intellectual content (brotman, Hoyer, Lepley, Deutschendorf, Leung), acquisition of data (Deutschendorf, Leung, Lepley), interpretation of data (Brotman, Hoyer, Lepley, Deutschendorf, Leung), data analysis (Brotman, Hoyer).

The Centers for Medicare & Medicaid Services (CMS) have sought to reduce readmissions in the 30 days following hospital discharge through penalties applied to hospitals with readmission rates that are higher than expected. Expected readmission rates for Medicare fee‐for‐service beneficiaries are calculated from models that use patient‐level administrative data to account for patient morbidities. Readmitted patients are defined as those who are discharged from the hospital alive and then rehospitalized at any acute care facility within 30 days of discharge. These models explicitly exclude sociodemographic variables that may impact quality of and access to outpatient care. Specific exclusions are also applied based on diagnosis codes so as to avoid penalizing hospitals for rehospitalizations that are likely to have been planned.

More recently, a hospital‐wide readmission measure has been developed, which seeks to provide a comprehensive view of each hospital's readmission rate by including the vast majority of Medicare patients. Like the condition‐specific readmission measures, the hospital‐wide readmission measure also excludes sociodemographic variables and incorporates specific condition‐based exclusions so as to avoid counting planned rehospitalizations (e.g., an admission for cholecystectomy following an admission for biliary sepsis). Although not currently used for pay‐for‐performance, this measure has been included in the CMS Star Report along with other readmission measures.[1] CMS does not currently disseminate a hospital‐wide mortality measure, but does disseminate hospital‐level adjusted 30‐day mortality rates for Medicare beneficiaries with discharge diagnoses of stroke, heart failure, myocardial infarction (MI), chronic obstructive pulmonary disease (COPD) and pneumonia, and principal procedure of coronary artery bypass grafting (CABG).

It is conceivable that aggressive efforts to reduce readmissions might delay life‐saving acute care in some scenarios,[2] and there is prior evidence that heart failure readmissions are inversely (but weakly) related to heart failure mortality.[3] It is also plausible that keeping tenuous patients alive until discharge might result in higher readmission rates. We sought to examine the relationship between hospital‐wide adjusted 30‐day readmissions and death rates across the acute care hospitals in the United States. Lacking a measure of hospital‐wide death rates, we examined the relation between hospital‐wide readmissions and each of the 6 condition‐specific mortality measures. For comparison, we also examined the relationships between condition‐specific readmission rates and mortality rates.

METHODS

We used publically available data published by CMS from July 1, 2011 through June 30, 2014.[4] These data are provided at the hospital level, without any patient‐level data. We included 4452 acute care facilities based on having hospital‐wide readmission rates, but not all facilities contributed data for each mortality measure. We excluded from analysis on a measure‐by‐measure basis those facilities for which outcomes were absent, without imputing missing outcome measures, because low volume of a given condition was the main reason for not reporting a measure. For each mortality measure, we constructed a logistic regression model to quantify the odds of performing in the lowest (best) mortality tertile as a function of hospital‐wide readmission tertile. To account for patient volumes, we included in each model the number of eligible patients at each hospital with the specified condition. We repeated these analyses using condition‐specific readmission rates (rather than the hospital‐wide readmission rates) as the independent variable. Specifications for CMS models for mortality and readmissions are publically available.[5]

RESULTS

After adjustment for patient volumes, hospitals in the highest hospital‐wide readmission tertile were more likely to perform in the lowest (best) mortality tertile for 3 of the 6 mortality measures: heart failure, COPD, and stroke (P < 0.001 for all). For MI, CABG and pneumonia, there was no significant association between high hospital‐wide readmission rates and low mortality (Table 1). Using condition‐specific readmission rates, there remained an inverse association between readmissions and mortality for heart failure and stroke, but not for COPD. In contrast, hospitals with the highest CABG‐specific readmission rates were significantly less likely to have low CABG‐specific mortality (P < 0.001).

DISCUSSION

We found that higher hospital‐wide readmission rates were associated with lower mortality at the hospital level for 3 of the 6 mortality measures we examined. The findings for heart failure parallel the findings of Krumholz and colleagues who examined 3 of these 6 measures (MI, pneumonia, and heart failure) in relation to readmissions for these specific populations.[3] This prior analysis, however, did not include the 3 more recently reported mortality measures (COPD, stroke, and CABG) and did not use hospital‐wide readmissions.

Causal mechanisms underlying the associations between mortality and readmission at the hospital level deserve further exploration. It is certainly possible that global efforts to keep patients out of the hospital might, in some instances, place patients at risk by delaying necessary acute care.[2] It is also possible that unmeasured variables, particularly access to hospice and palliative care services that might facilitate good deaths, could be associated with both reduced readmissions and higher death rates. Additionally, because deceased patients cannot be readmitted, one might expect that readmissions and mortality might be inversely associated, particularly for conditions with a high postdischarge mortality rate. Similarly, a hospital that does a particularly good job keeping chronically ill patients alive until discharge might exhibit a higher readmission rate than a hospital that is less adept at keeping tenuous patients alive until discharge.

Regardless of the mechanisms of these findings, we present these data to raise the concern that using readmission rates, particularly hospital‐wide readmission rates, as a measure of hospital quality is inherently problematic. It is particularly problematic that CMS has applied equal weight to readmissions and mortality in the Star Report.[1] High readmission rates may result from complications and poor handoffs, but may also stem from the legitimate need to care for chronically ill patients in a high‐intensity setting, particularly fragile patients who have been kept alive against the odds. In conclusion, caution is warranted in viewing readmissions as a quality metric until the associations we describe are better explained using patient‐level data and more robust adjustment than is possible with these publically available data.

Disclosures: Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. There was no financial support for this work. Contributions of the authors are as follows: drafting manuscript (Brotman), revision of manuscript for important intellectual content (brotman, Hoyer, Lepley, Deutschendorf, Leung), acquisition of data (Deutschendorf, Leung, Lepley), interpretation of data (Brotman, Hoyer, Lepley, Deutschendorf, Leung), data analysis (Brotman, Hoyer).

Up to 10% of the acutely ill patients in hospitals today are not admitted, but cared for under outpatient observation.[1, 2] Hospitals use observation services to replace inpatient services for patients who do not meet inpatient illness standards. Use of this technique for Medicare beneficiaries has grown in recent years, and future policy changes may further increase the percentage of hospital stays classified as observation.[3, 4]

Increased use of observation services has cost‐sharing implications for Medicare beneficiaries. Because hospital observation stays are paid through Medicare's outpatient (Part B) rather than inpatient (Part A) benefit, beneficiaries do not have an out‐of‐pocket maximum per hospital stay.[3] Although prior analyses documented mean out‐of‐pocket costs of $400 to $600 per stay, out‐of‐pocket costs may exceed the Part A deductible and leave beneficiaries responsible for hospital costs that may have been reimbursed by Part A in an inpatient stay.[5, 6, 7] Approximately 6% to 10% of Medicare observation stays result in out‐of‐pocket costs exceeding the Part A deductible nationally, and recent work in this journal documented that 26.6% of beneficiaries with repeat observation stays within a 60‐day period may pay more than the 1‐time deductible‐based payment that beneficiaries are responsible for under Medicare Part A.[5, 6, 7]

However, prior analyses of beneficiary out‐of‐pocket costs did not account for supplemental insurance payments. Approximately 80% to 90% of fee‐for‐service Medicare beneficiaries who use either a private (Medigap or employer based) or state‐based (Medicaid or other plans) supplemental insurance plan.[8, 9, 10] The effect of supplemental insurance on out‐of‐pocket costs has been documented for inpatient stays, yet has not been explored for observation stays.[9] We sought to describe Medicare beneficiaries' out‐of‐pocket costs by accounting for payments from all insurers.

METHODS

We obtained payment data from 2 affiliated hospitals for all Medicare observation hospital stays between April 2013 and March 2014. Stays insured by Medicare Advantage plans (Part C) were excluded, and charges from skilled nursing facility (SNF) stays were not available. Although the exact origin of each stay was not available, the majority of stays at these hospitals came from emergency visits, direct placement from outpatient providers, and postoutpatient procedural monitoring. The dataset included charges, insurance adjustments and payments from all sources, diagnoses, and demographics. Insurance adjustments represented the reductions applied to total stay charges by each insurer in congruence with their contract with the health system. Our dataset included charges for all hospital materials and services usually billed under Part A, including medications. Out‐of‐pocket costs were calculated by subtracting insurance adjustments and payments from the total charges for each stay. To identify potential cost‐shifting from Medicare to beneficiaries, we compared out‐of‐pocket costs to the Part A deductible ($1184 for stays in 2013 and $1216 for 2014). Household income data were estimated using zip code and Internal Revenue Service data for 2013.[11]

The University of California Los Angeles institutional review board approved this study. Statistical significance was calculated using a 2‐tailed unpaired t test for means and ² for percentages.

RESULTS

There were 2029 total observation stays during the study period, representing 5.0% of all discharges from both hospitals. Medicare beneficiaries accounted for 722 of those observation stays. Among the 498 finalized Medicare observation stays, the median patient age was 73 years, and median household income was $50,591. The median length of stay was 25 hours, with 1.8% of stays lasting longer than 2 midnights. Seventy percent of beneficiaries had private supplemental insurance, whereas 6% had state‐based supplemental plans. Table 1 presents detailed costs. Out‐of‐pocket costs ranged from $0 to $16,196. Stays without supplemental insurance had mean and median out of pocket costs of $537 and $286, respectively. The mean out‐of‐pocket costs for stays with private supplemental insurance decreased to $45 (P < 0.01) and $168 (P = 0.21) for stays with state‐based supplemental insurance, with a median below $1 for both. On average, beneficiaries without supplemental insurance were responsible for $654 less than the Part A deductible. Thirteen beneficiaries had multiple finalized stays within 60 days, with a mean out‐of‐pocket cost of $119, median of $20, and 1 stay produced an out‐of‐pocket cost exceeding the Part A deductible. An additional 224 Medicare stays not finalized because of missing supplemental payments had a mean out‐of‐pocket cost of $125 (P < 0.01) and median of $5 after applying supplemental insurer adjustments (not shown in Table 1).

A minority of observation stays produced out‐of‐pocket costs exceeding the Part A deductible. Those percentages were 7.6% for stays without a supplemental insurer, 3.5% (P < 0.01) for stays with a state‐based supplemental insurer, and 0.3% (P < 0.01) for stays with a private supplemental insurer. Of the 224 nonfinalized Medicare stays, 1.3% (P < 0.01) exceeded the Part A deductible after supplemental insurer adjustments.

DISCUSSION

This study demonstrates that supplemental insurance can dramatically reduce Medicare beneficiaries' out‐of‐pocket costs in observation services. Mean out‐of‐pocket costs of $45 and $168 for stays with private and state‐based supplemental insurer plans are significantly lower than prior estimates calculated without supplemental insurance information, as are the percentages of stays with out‐of‐pocket costs exceeding the Part A deductible, at 0.3% and 3.5%, respectively.[5, 6, 7] Because the majority of Medicare beneficiaries use supplemental insurance, excessive out‐of‐pockets in observation services may occur less frequently than previously reported.[9, 10] Clinicians concerned about excessive out‐of‐pocket costs for Medicare beneficiaries can be reassured they are usually modest for beneficiaries with supplemental insurance.

This study's mean out‐of‐pocket cost and percentage of stays with out‐of‐pocket costs exceeding the Part A deductible for beneficiaries without supplemental insurance are similar to results from prior national analyses performed without supplemental insurer information.[5, 6, 7] But this study was limited by a small sample size from 2 affiliated hospitals, with few repeat observation stays within a 60‐day period. In addition, posthospitalization SNF fees were not included, which traditionally have been a significant source of out‐of‐pocket costs in observation services.[3, 7] Populations with supplemental insurance treated elsewhere may incur hospital out‐of‐pocket costs differing from these results due to dissimilarities in the presence and quality of supplemental insurance.

However, most Medigap plans are federally regulated to cover the majority of out‐of‐pockets unpaid by Medicare.[12] Medicaid plans usually place limits on out‐of‐pocket costs, and any other state or employer‐based supplemental plans will also reduce out‐of‐pocket costs.[13] Thus, it is likely accurate to assume mean observation services out‐of‐pocket costs for hospital fees are lower than previously reported by national analyses performed without supplemental insurance information. Attempts at estimating beneficiary out‐of‐pocket costs in the future should account for supplemental insurance adjustments and payments.

Up to 10% of the acutely ill patients in hospitals today are not admitted, but cared for under outpatient observation.[1, 2] Hospitals use observation services to replace inpatient services for patients who do not meet inpatient illness standards. Use of this technique for Medicare beneficiaries has grown in recent years, and future policy changes may further increase the percentage of hospital stays classified as observation.[3, 4]

Increased use of observation services has cost‐sharing implications for Medicare beneficiaries. Because hospital observation stays are paid through Medicare's outpatient (Part B) rather than inpatient (Part A) benefit, beneficiaries do not have an out‐of‐pocket maximum per hospital stay.[3] Although prior analyses documented mean out‐of‐pocket costs of $400 to $600 per stay, out‐of‐pocket costs may exceed the Part A deductible and leave beneficiaries responsible for hospital costs that may have been reimbursed by Part A in an inpatient stay.[5, 6, 7] Approximately 6% to 10% of Medicare observation stays result in out‐of‐pocket costs exceeding the Part A deductible nationally, and recent work in this journal documented that 26.6% of beneficiaries with repeat observation stays within a 60‐day period may pay more than the 1‐time deductible‐based payment that beneficiaries are responsible for under Medicare Part A.[5, 6, 7]

However, prior analyses of beneficiary out‐of‐pocket costs did not account for supplemental insurance payments. Approximately 80% to 90% of fee‐for‐service Medicare beneficiaries who use either a private (Medigap or employer based) or state‐based (Medicaid or other plans) supplemental insurance plan.[8, 9, 10] The effect of supplemental insurance on out‐of‐pocket costs has been documented for inpatient stays, yet has not been explored for observation stays.[9] We sought to describe Medicare beneficiaries' out‐of‐pocket costs by accounting for payments from all insurers.

METHODS

We obtained payment data from 2 affiliated hospitals for all Medicare observation hospital stays between April 2013 and March 2014. Stays insured by Medicare Advantage plans (Part C) were excluded, and charges from skilled nursing facility (SNF) stays were not available. Although the exact origin of each stay was not available, the majority of stays at these hospitals came from emergency visits, direct placement from outpatient providers, and postoutpatient procedural monitoring. The dataset included charges, insurance adjustments and payments from all sources, diagnoses, and demographics. Insurance adjustments represented the reductions applied to total stay charges by each insurer in congruence with their contract with the health system. Our dataset included charges for all hospital materials and services usually billed under Part A, including medications. Out‐of‐pocket costs were calculated by subtracting insurance adjustments and payments from the total charges for each stay. To identify potential cost‐shifting from Medicare to beneficiaries, we compared out‐of‐pocket costs to the Part A deductible ($1184 for stays in 2013 and $1216 for 2014). Household income data were estimated using zip code and Internal Revenue Service data for 2013.[11]

The University of California Los Angeles institutional review board approved this study. Statistical significance was calculated using a 2‐tailed unpaired t test for means and ² for percentages.

RESULTS

There were 2029 total observation stays during the study period, representing 5.0% of all discharges from both hospitals. Medicare beneficiaries accounted for 722 of those observation stays. Among the 498 finalized Medicare observation stays, the median patient age was 73 years, and median household income was $50,591. The median length of stay was 25 hours, with 1.8% of stays lasting longer than 2 midnights. Seventy percent of beneficiaries had private supplemental insurance, whereas 6% had state‐based supplemental plans. Table 1 presents detailed costs. Out‐of‐pocket costs ranged from $0 to $16,196. Stays without supplemental insurance had mean and median out of pocket costs of $537 and $286, respectively. The mean out‐of‐pocket costs for stays with private supplemental insurance decreased to $45 (P < 0.01) and $168 (P = 0.21) for stays with state‐based supplemental insurance, with a median below $1 for both. On average, beneficiaries without supplemental insurance were responsible for $654 less than the Part A deductible. Thirteen beneficiaries had multiple finalized stays within 60 days, with a mean out‐of‐pocket cost of $119, median of $20, and 1 stay produced an out‐of‐pocket cost exceeding the Part A deductible. An additional 224 Medicare stays not finalized because of missing supplemental payments had a mean out‐of‐pocket cost of $125 (P < 0.01) and median of $5 after applying supplemental insurer adjustments (not shown in Table 1).

A minority of observation stays produced out‐of‐pocket costs exceeding the Part A deductible. Those percentages were 7.6% for stays without a supplemental insurer, 3.5% (P < 0.01) for stays with a state‐based supplemental insurer, and 0.3% (P < 0.01) for stays with a private supplemental insurer. Of the 224 nonfinalized Medicare stays, 1.3% (P < 0.01) exceeded the Part A deductible after supplemental insurer adjustments.

DISCUSSION

This study demonstrates that supplemental insurance can dramatically reduce Medicare beneficiaries' out‐of‐pocket costs in observation services. Mean out‐of‐pocket costs of $45 and $168 for stays with private and state‐based supplemental insurer plans are significantly lower than prior estimates calculated without supplemental insurance information, as are the percentages of stays with out‐of‐pocket costs exceeding the Part A deductible, at 0.3% and 3.5%, respectively.[5, 6, 7] Because the majority of Medicare beneficiaries use supplemental insurance, excessive out‐of‐pockets in observation services may occur less frequently than previously reported.[9, 10] Clinicians concerned about excessive out‐of‐pocket costs for Medicare beneficiaries can be reassured they are usually modest for beneficiaries with supplemental insurance.

This study's mean out‐of‐pocket cost and percentage of stays with out‐of‐pocket costs exceeding the Part A deductible for beneficiaries without supplemental insurance are similar to results from prior national analyses performed without supplemental insurer information.[5, 6, 7] But this study was limited by a small sample size from 2 affiliated hospitals, with few repeat observation stays within a 60‐day period. In addition, posthospitalization SNF fees were not included, which traditionally have been a significant source of out‐of‐pocket costs in observation services.[3, 7] Populations with supplemental insurance treated elsewhere may incur hospital out‐of‐pocket costs differing from these results due to dissimilarities in the presence and quality of supplemental insurance.

However, most Medigap plans are federally regulated to cover the majority of out‐of‐pockets unpaid by Medicare.[12] Medicaid plans usually place limits on out‐of‐pocket costs, and any other state or employer‐based supplemental plans will also reduce out‐of‐pocket costs.[13] Thus, it is likely accurate to assume mean observation services out‐of‐pocket costs for hospital fees are lower than previously reported by national analyses performed without supplemental insurance information. Attempts at estimating beneficiary out‐of‐pocket costs in the future should account for supplemental insurance adjustments and payments.

Up to 10% of the acutely ill patients in hospitals today are not admitted, but cared for under outpatient observation.[1, 2] Hospitals use observation services to replace inpatient services for patients who do not meet inpatient illness standards. Use of this technique for Medicare beneficiaries has grown in recent years, and future policy changes may further increase the percentage of hospital stays classified as observation.[3, 4]

Increased use of observation services has cost‐sharing implications for Medicare beneficiaries. Because hospital observation stays are paid through Medicare's outpatient (Part B) rather than inpatient (Part A) benefit, beneficiaries do not have an out‐of‐pocket maximum per hospital stay.[3] Although prior analyses documented mean out‐of‐pocket costs of $400 to $600 per stay, out‐of‐pocket costs may exceed the Part A deductible and leave beneficiaries responsible for hospital costs that may have been reimbursed by Part A in an inpatient stay.[5, 6, 7] Approximately 6% to 10% of Medicare observation stays result in out‐of‐pocket costs exceeding the Part A deductible nationally, and recent work in this journal documented that 26.6% of beneficiaries with repeat observation stays within a 60‐day period may pay more than the 1‐time deductible‐based payment that beneficiaries are responsible for under Medicare Part A.[5, 6, 7]

However, prior analyses of beneficiary out‐of‐pocket costs did not account for supplemental insurance payments. Approximately 80% to 90% of fee‐for‐service Medicare beneficiaries who use either a private (Medigap or employer based) or state‐based (Medicaid or other plans) supplemental insurance plan.[8, 9, 10] The effect of supplemental insurance on out‐of‐pocket costs has been documented for inpatient stays, yet has not been explored for observation stays.[9] We sought to describe Medicare beneficiaries' out‐of‐pocket costs by accounting for payments from all insurers.

METHODS

We obtained payment data from 2 affiliated hospitals for all Medicare observation hospital stays between April 2013 and March 2014. Stays insured by Medicare Advantage plans (Part C) were excluded, and charges from skilled nursing facility (SNF) stays were not available. Although the exact origin of each stay was not available, the majority of stays at these hospitals came from emergency visits, direct placement from outpatient providers, and postoutpatient procedural monitoring. The dataset included charges, insurance adjustments and payments from all sources, diagnoses, and demographics. Insurance adjustments represented the reductions applied to total stay charges by each insurer in congruence with their contract with the health system. Our dataset included charges for all hospital materials and services usually billed under Part A, including medications. Out‐of‐pocket costs were calculated by subtracting insurance adjustments and payments from the total charges for each stay. To identify potential cost‐shifting from Medicare to beneficiaries, we compared out‐of‐pocket costs to the Part A deductible ($1184 for stays in 2013 and $1216 for 2014). Household income data were estimated using zip code and Internal Revenue Service data for 2013.[11]

The University of California Los Angeles institutional review board approved this study. Statistical significance was calculated using a 2‐tailed unpaired t test for means and ² for percentages.

RESULTS

There were 2029 total observation stays during the study period, representing 5.0% of all discharges from both hospitals. Medicare beneficiaries accounted for 722 of those observation stays. Among the 498 finalized Medicare observation stays, the median patient age was 73 years, and median household income was $50,591. The median length of stay was 25 hours, with 1.8% of stays lasting longer than 2 midnights. Seventy percent of beneficiaries had private supplemental insurance, whereas 6% had state‐based supplemental plans. Table 1 presents detailed costs. Out‐of‐pocket costs ranged from $0 to $16,196. Stays without supplemental insurance had mean and median out of pocket costs of $537 and $286, respectively. The mean out‐of‐pocket costs for stays with private supplemental insurance decreased to $45 (P < 0.01) and $168 (P = 0.21) for stays with state‐based supplemental insurance, with a median below $1 for both. On average, beneficiaries without supplemental insurance were responsible for $654 less than the Part A deductible. Thirteen beneficiaries had multiple finalized stays within 60 days, with a mean out‐of‐pocket cost of $119, median of $20, and 1 stay produced an out‐of‐pocket cost exceeding the Part A deductible. An additional 224 Medicare stays not finalized because of missing supplemental payments had a mean out‐of‐pocket cost of $125 (P < 0.01) and median of $5 after applying supplemental insurer adjustments (not shown in Table 1).

A minority of observation stays produced out‐of‐pocket costs exceeding the Part A deductible. Those percentages were 7.6% for stays without a supplemental insurer, 3.5% (P < 0.01) for stays with a state‐based supplemental insurer, and 0.3% (P < 0.01) for stays with a private supplemental insurer. Of the 224 nonfinalized Medicare stays, 1.3% (P < 0.01) exceeded the Part A deductible after supplemental insurer adjustments.

DISCUSSION

This study demonstrates that supplemental insurance can dramatically reduce Medicare beneficiaries' out‐of‐pocket costs in observation services. Mean out‐of‐pocket costs of $45 and $168 for stays with private and state‐based supplemental insurer plans are significantly lower than prior estimates calculated without supplemental insurance information, as are the percentages of stays with out‐of‐pocket costs exceeding the Part A deductible, at 0.3% and 3.5%, respectively.[5, 6, 7] Because the majority of Medicare beneficiaries use supplemental insurance, excessive out‐of‐pockets in observation services may occur less frequently than previously reported.[9, 10] Clinicians concerned about excessive out‐of‐pocket costs for Medicare beneficiaries can be reassured they are usually modest for beneficiaries with supplemental insurance.

This study's mean out‐of‐pocket cost and percentage of stays with out‐of‐pocket costs exceeding the Part A deductible for beneficiaries without supplemental insurance are similar to results from prior national analyses performed without supplemental insurer information.[5, 6, 7] But this study was limited by a small sample size from 2 affiliated hospitals, with few repeat observation stays within a 60‐day period. In addition, posthospitalization SNF fees were not included, which traditionally have been a significant source of out‐of‐pocket costs in observation services.[3, 7] Populations with supplemental insurance treated elsewhere may incur hospital out‐of‐pocket costs differing from these results due to dissimilarities in the presence and quality of supplemental insurance.

However, most Medigap plans are federally regulated to cover the majority of out‐of‐pockets unpaid by Medicare.[12] Medicaid plans usually place limits on out‐of‐pocket costs, and any other state or employer‐based supplemental plans will also reduce out‐of‐pocket costs.[13] Thus, it is likely accurate to assume mean observation services out‐of‐pocket costs for hospital fees are lower than previously reported by national analyses performed without supplemental insurance information. Attempts at estimating beneficiary out‐of‐pocket costs in the future should account for supplemental insurance adjustments and payments.

Injection drug use (IDU) is a major public health problem leading to increased morbidity, mortality, and healthcare expenditures.[1, 2, 3] Persons who inject drugs (PWID) are often hospitalized with severe infections, such as endocarditis,[4, 5] which typically require prolonged courses of intravenous (IV) antibiotics. Outpatient parenteral antibiotic therapy (OPAT) via a peripherally inserted central catheter (PICC) is the standard of care for continuing IV medications once patients are medically stable and ready for discharge.[6] PWID have been excluded from OPAT studies,[6] leaving little evidence to guide care.[7] Furthermore, likely due to fears of ongoing IDU, PWID are often kept in the hospital for the full duration of their antibiotic courses. This practice is costly and may not be optimal, especially considering that hospitalized PWID have high rates of discharges against medical advice.[8, 9]

In 2012, as part of a quality‐improvement effort focused on hospitalized PWID requiring long courses of IV antibiotics, UKHealthCare in Lexington, Kentucky, established a protocol for OPAT in PWID meeting specific criteria. As this protocol was not widely adopted, we sought to formally assess attitudes, practices, and mediating factors impacting the decision making about discharging PWID on OPAT to inform future efforts. This study was approved by the University of Kentucky (UK) Institutional Review Board.

METHODS

A 14‐item survey (see Supporting Information, Appendix, in the online version of this article) with multiple‐choice and open‐ended response items was developed based on the existing protocol, and themes were confirmed through semistructured interviews with 10 attending physicians in hospital medicine (HM) and infectious disease (ID). Questions were designed to elucidate the role that IDU played in the decision to discharge patients on OPAT, identify barriers to discharging PWID on OPAT, as well as elicit recommendations for requisite services or programs. The first question excluded providers not caring for patients requiring long‐term IV antibiotics. Questions that allowed for open‐ended responses were categorized thematically initially by 1 researcher (L.F.), then refined and confirmed by another team member (J.L.). The survey was distributed over email through Qualtrics (Provo, Utah) software to attending physicians in HM, ID, cardiology, and surgery at UK. Qualtrics software was used to generate descriptive statistics.

RESULTS

In January 2015, the survey was emailed to 66 physicians, and the response rate was 83%, with 91% reporting caring for patients requiring long‐term IV antibiotics. Of those, 41 (82%) completed all items; 66% of completers were in HM, 12% ID, 10% surgery, and 2% cardiology. Sixty percent were male and in practice an average of 7.2 years. Thirty‐nine (95%) use OPAT for patients without IDU, but only 12 (29%) would consider OPAT in PWID. If the patient has a remote history of IDU, then 33 (79%) would consider OPAT. There was no agreed‐upon definition of remote history of IDU (range, 2120 months; median, 12 months).

The most common physician‐identified barriers to discharging PWID on OPAT, as well as recommendations for services or processes to be in place to allow PWID to be discharged with OPAT, are listed in Table 1.

DISCUSSION

This survey illustrates the extremely complex barriers present when treating hospitalized PWID requiring long courses of IV antibiotics, and supports the anecdotal evidence that physicians often keep PWID in the hospital for weeks to administer IV antibiotics. The majority of our sample of physicians believe that the largest barriers to OPAT in PWID are socioeconomic factors and the potential risk of the patient misusing the PICC line. Although the overall response rate of our physician survey was robust,[10] our results reflect the opinions of HM and ID physicians at a single site. The low response rate among cardiologists in particular limits the generalizability of this survey. We suspect, however, that our results pertain to HM in other US hospitals, as nearly three‐fourths of 37 HM physicians surveyed at the University of California, Irvine were very concerned about PWIDs potentially misusing the PICC line, and approximately half reported they usually or always kept PWID in the hospital for prolonged treatment due to concern of substance use (personal and email communication: Lloyd Rucker, MD, unpublished data, November 6, 2015).

We were surprised that fewer than half of respondents identified substance use disorder (SUD) treatment as essential to the OPAT decision. The reasons that may explain this observation are likely multifactorial, and may include gaps in knowledge about and resources to provide evidence‐based addiction medicine. Further research is warranted to explore this observation, including the effect of enrollment into medication‐assisted treatment programs (eg, methadone, buprenorphine).

This survey suggests that although there is variability, OPAT may be an option in PWID, if outpatient follow‐up and ancillary services (ie, home health and possibly intensive case management) were well established. We believe the comorbid SUD must be also addressed. Based on the survey results and recommendations, we have begun relationships with community SUD treatment providers willing to monitor IV antibiotics with PICC lines, and dedicated additional case management staff to this population. We are evaluating these programs with the goal of contributing to an evidence base for this high‐risk population.

Injection drug use (IDU) is a major public health problem leading to increased morbidity, mortality, and healthcare expenditures.[1, 2, 3] Persons who inject drugs (PWID) are often hospitalized with severe infections, such as endocarditis,[4, 5] which typically require prolonged courses of intravenous (IV) antibiotics. Outpatient parenteral antibiotic therapy (OPAT) via a peripherally inserted central catheter (PICC) is the standard of care for continuing IV medications once patients are medically stable and ready for discharge.[6] PWID have been excluded from OPAT studies,[6] leaving little evidence to guide care.[7] Furthermore, likely due to fears of ongoing IDU, PWID are often kept in the hospital for the full duration of their antibiotic courses. This practice is costly and may not be optimal, especially considering that hospitalized PWID have high rates of discharges against medical advice.[8, 9]

In 2012, as part of a quality‐improvement effort focused on hospitalized PWID requiring long courses of IV antibiotics, UKHealthCare in Lexington, Kentucky, established a protocol for OPAT in PWID meeting specific criteria. As this protocol was not widely adopted, we sought to formally assess attitudes, practices, and mediating factors impacting the decision making about discharging PWID on OPAT to inform future efforts. This study was approved by the University of Kentucky (UK) Institutional Review Board.

METHODS

A 14‐item survey (see Supporting Information, Appendix, in the online version of this article) with multiple‐choice and open‐ended response items was developed based on the existing protocol, and themes were confirmed through semistructured interviews with 10 attending physicians in hospital medicine (HM) and infectious disease (ID). Questions were designed to elucidate the role that IDU played in the decision to discharge patients on OPAT, identify barriers to discharging PWID on OPAT, as well as elicit recommendations for requisite services or programs. The first question excluded providers not caring for patients requiring long‐term IV antibiotics. Questions that allowed for open‐ended responses were categorized thematically initially by 1 researcher (L.F.), then refined and confirmed by another team member (J.L.). The survey was distributed over email through Qualtrics (Provo, Utah) software to attending physicians in HM, ID, cardiology, and surgery at UK. Qualtrics software was used to generate descriptive statistics.

RESULTS

In January 2015, the survey was emailed to 66 physicians, and the response rate was 83%, with 91% reporting caring for patients requiring long‐term IV antibiotics. Of those, 41 (82%) completed all items; 66% of completers were in HM, 12% ID, 10% surgery, and 2% cardiology. Sixty percent were male and in practice an average of 7.2 years. Thirty‐nine (95%) use OPAT for patients without IDU, but only 12 (29%) would consider OPAT in PWID. If the patient has a remote history of IDU, then 33 (79%) would consider OPAT. There was no agreed‐upon definition of remote history of IDU (range, 2120 months; median, 12 months).

The most common physician‐identified barriers to discharging PWID on OPAT, as well as recommendations for services or processes to be in place to allow PWID to be discharged with OPAT, are listed in Table 1.

DISCUSSION

This survey illustrates the extremely complex barriers present when treating hospitalized PWID requiring long courses of IV antibiotics, and supports the anecdotal evidence that physicians often keep PWID in the hospital for weeks to administer IV antibiotics. The majority of our sample of physicians believe that the largest barriers to OPAT in PWID are socioeconomic factors and the potential risk of the patient misusing the PICC line. Although the overall response rate of our physician survey was robust,[10] our results reflect the opinions of HM and ID physicians at a single site. The low response rate among cardiologists in particular limits the generalizability of this survey. We suspect, however, that our results pertain to HM in other US hospitals, as nearly three‐fourths of 37 HM physicians surveyed at the University of California, Irvine were very concerned about PWIDs potentially misusing the PICC line, and approximately half reported they usually or always kept PWID in the hospital for prolonged treatment due to concern of substance use (personal and email communication: Lloyd Rucker, MD, unpublished data, November 6, 2015).

We were surprised that fewer than half of respondents identified substance use disorder (SUD) treatment as essential to the OPAT decision. The reasons that may explain this observation are likely multifactorial, and may include gaps in knowledge about and resources to provide evidence‐based addiction medicine. Further research is warranted to explore this observation, including the effect of enrollment into medication‐assisted treatment programs (eg, methadone, buprenorphine).

This survey suggests that although there is variability, OPAT may be an option in PWID, if outpatient follow‐up and ancillary services (ie, home health and possibly intensive case management) were well established. We believe the comorbid SUD must be also addressed. Based on the survey results and recommendations, we have begun relationships with community SUD treatment providers willing to monitor IV antibiotics with PICC lines, and dedicated additional case management staff to this population. We are evaluating these programs with the goal of contributing to an evidence base for this high‐risk population.

Injection drug use (IDU) is a major public health problem leading to increased morbidity, mortality, and healthcare expenditures.[1, 2, 3] Persons who inject drugs (PWID) are often hospitalized with severe infections, such as endocarditis,[4, 5] which typically require prolonged courses of intravenous (IV) antibiotics. Outpatient parenteral antibiotic therapy (OPAT) via a peripherally inserted central catheter (PICC) is the standard of care for continuing IV medications once patients are medically stable and ready for discharge.[6] PWID have been excluded from OPAT studies,[6] leaving little evidence to guide care.[7] Furthermore, likely due to fears of ongoing IDU, PWID are often kept in the hospital for the full duration of their antibiotic courses. This practice is costly and may not be optimal, especially considering that hospitalized PWID have high rates of discharges against medical advice.[8, 9]

In 2012, as part of a quality‐improvement effort focused on hospitalized PWID requiring long courses of IV antibiotics, UKHealthCare in Lexington, Kentucky, established a protocol for OPAT in PWID meeting specific criteria. As this protocol was not widely adopted, we sought to formally assess attitudes, practices, and mediating factors impacting the decision making about discharging PWID on OPAT to inform future efforts. This study was approved by the University of Kentucky (UK) Institutional Review Board.

METHODS

A 14‐item survey (see Supporting Information, Appendix, in the online version of this article) with multiple‐choice and open‐ended response items was developed based on the existing protocol, and themes were confirmed through semistructured interviews with 10 attending physicians in hospital medicine (HM) and infectious disease (ID). Questions were designed to elucidate the role that IDU played in the decision to discharge patients on OPAT, identify barriers to discharging PWID on OPAT, as well as elicit recommendations for requisite services or programs. The first question excluded providers not caring for patients requiring long‐term IV antibiotics. Questions that allowed for open‐ended responses were categorized thematically initially by 1 researcher (L.F.), then refined and confirmed by another team member (J.L.). The survey was distributed over email through Qualtrics (Provo, Utah) software to attending physicians in HM, ID, cardiology, and surgery at UK. Qualtrics software was used to generate descriptive statistics.

RESULTS

In January 2015, the survey was emailed to 66 physicians, and the response rate was 83%, with 91% reporting caring for patients requiring long‐term IV antibiotics. Of those, 41 (82%) completed all items; 66% of completers were in HM, 12% ID, 10% surgery, and 2% cardiology. Sixty percent were male and in practice an average of 7.2 years. Thirty‐nine (95%) use OPAT for patients without IDU, but only 12 (29%) would consider OPAT in PWID. If the patient has a remote history of IDU, then 33 (79%) would consider OPAT. There was no agreed‐upon definition of remote history of IDU (range, 2120 months; median, 12 months).

The most common physician‐identified barriers to discharging PWID on OPAT, as well as recommendations for services or processes to be in place to allow PWID to be discharged with OPAT, are listed in Table 1.

DISCUSSION

This survey illustrates the extremely complex barriers present when treating hospitalized PWID requiring long courses of IV antibiotics, and supports the anecdotal evidence that physicians often keep PWID in the hospital for weeks to administer IV antibiotics. The majority of our sample of physicians believe that the largest barriers to OPAT in PWID are socioeconomic factors and the potential risk of the patient misusing the PICC line. Although the overall response rate of our physician survey was robust,[10] our results reflect the opinions of HM and ID physicians at a single site. The low response rate among cardiologists in particular limits the generalizability of this survey. We suspect, however, that our results pertain to HM in other US hospitals, as nearly three‐fourths of 37 HM physicians surveyed at the University of California, Irvine were very concerned about PWIDs potentially misusing the PICC line, and approximately half reported they usually or always kept PWID in the hospital for prolonged treatment due to concern of substance use (personal and email communication: Lloyd Rucker, MD, unpublished data, November 6, 2015).

We were surprised that fewer than half of respondents identified substance use disorder (SUD) treatment as essential to the OPAT decision. The reasons that may explain this observation are likely multifactorial, and may include gaps in knowledge about and resources to provide evidence‐based addiction medicine. Further research is warranted to explore this observation, including the effect of enrollment into medication‐assisted treatment programs (eg, methadone, buprenorphine).

This survey suggests that although there is variability, OPAT may be an option in PWID, if outpatient follow‐up and ancillary services (ie, home health and possibly intensive case management) were well established. We believe the comorbid SUD must be also addressed. Based on the survey results and recommendations, we have begun relationships with community SUD treatment providers willing to monitor IV antibiotics with PICC lines, and dedicated additional case management staff to this population. We are evaluating these programs with the goal of contributing to an evidence base for this high‐risk population.

Payers, providers, and policymakers are testing several major approaches to reducing US healthcare spending without harming quality. One strategy is to bundle payments for a longitudinal episode of care, as in Medicare's popular Bundled Payments for Care Improvement initiative.[1] A second approach is to decrease rates of inappropriate care, through programs such as Choosing Wisely, that discourage use of low‐value services.[2] Finally, a third approach adopted by the Medicare Shared Savings Program strives to reduce both episode costs and rates of inappropriate care, by incorporating annual per capita Medicare spending into performance benchmarks.[3] Given these ongoing efforts, it would be important to compare the potential impact of reducing episode payments versus rates of care on total costs of care.

METHODS

For 3 common surgical procedures, we compared the relative influence of procedure rates versus episode payments (among those with procedures) on total Medicare expenditures.

We used complete Part A and B Medicare claims data for: coronary artery bypass grafting (CABG), prostatectomy, and hip replacement. We used International Classification of Diseases, Ninth Revision codes to identify the procedures (CABG: 361.0, 361.1, 361.2, 361.3, 361.4, 361.5, 361.6, 361.7, 361.9, 36.2; prostatectomy: 60.4, 60.5, 60.62 with a prostate cancer diagnosis code of 185 or 233.4; and hip replacement: 81.51, 81.52 excluding hip fracture codes 820.0, 820.1, 820.2, 820.3, 820.8, 820.9).

For each procedure, we estimated age‐ and sex‐adjusted episode rates for each hospital referral region (HRR). The numerator was the number of admissions to an acute care hospital for CABG (total n = 118,185), prostatectomy (total n = 18,328), or hip replacement (total n = 178,982) from January 2009 to June 2010. The denominator was fee‐for‐service Medicare beneficiaries age 65 years or older. We excluded those without continuous Part A and B enrollment (total denominator n = 23,403,051). Females were also excluded from the prostatectomy cohort.

For each of the 306 HRRs, we next calculated average HRR‐level episode payments. Using CABG as an example, we aggregated up the risk‐adjusted (age, sex, race, admission type, Elixhauser[4] comorbidities), price‐standardized[5] episode payments for all CABG patients residing in an HRR, and divided this by the number of CABG patients living in that HRR.

Finally, we obtained baseline per capita spending by multiplying the age‐ and sex‐adjusted CABG episode rate by the average CABG episode payment in that HRR. All payments were standardized to 2010 dollars using the Consumer Price Index.

We simulated changes in per capita Medicare spending for CABG across all HRRs under 2 scenarios: (1) reducing HRR‐level rates to the median versus (2) reducing HRR‐level episode payments to the median. We repeated this for prostatectomy and hip replacement.

RESULTS

Age‐ and sex‐adjusted rates of CABG varied more than risk‐adjusted, price‐standardized episode payments (90th:10th percentile of 2.0 for rates vs 1.2 for payments) (see Supporting Information, Appendix, in the online version of this article). Reducing rates of CABG to the 50th percentile decreased per capita episode payments by 11.1%. In contrast, reducing CABG episode payments to the 50th percentile decreased per capita episode payments by 3.6%. The absolute difference between the 2 simulations was 7.5% (95% confidence interval [CI]: 5.6%‐9.4%) (Figure 1). Results were similar for prostatectomy and hip replacement. In sensitivity analyses, reducing hospital‐level episode payments (rather than HRR‐level episode payments) produced similar findings. Employing the 90th percentile as a cutoff (instead of the median) also produced qualitatively similar results.

Figure 1

DISCUSSION

For 3 common surgical procedures, reducing procedure rates lowers total Medicare spending substantially more than reducing episode payments. These findings are attributable to a much greater variation in procedure rates compared to episode‐based payments. Prior research has documented wide variation in rates of surgical procedures.[6] This may be due to a number of factors, including physician beliefs about indications for surgery, as well as the degree to which patient preferences are incorporated into decision making.[6]

Our findings suggest that it would be important to incorporate population‐based episode rates into efforts aimed at incentivizing higher value care. Incentives tied to population‐based episode rates are difficult to design well. They may need to be paired with appropriateness criteria to avoid stinting on care. Attribution of a population to a hospital (including those who are not admitted to a hospital) is also complex.[7] Finally, hospitals are not solely responsible for rates of care, because the decision to admit a patient is sometimes made in the emergency department (eg, for chronic medical conditions), but at other times is made in the outpatient arena (eg, for elective surgery). Nevertheless, a narrow focus on per episode spending limits the potential impact of efforts to control Medicare spending.

Payers, providers, and policymakers are testing several major approaches to reducing US healthcare spending without harming quality. One strategy is to bundle payments for a longitudinal episode of care, as in Medicare's popular Bundled Payments for Care Improvement initiative.[1] A second approach is to decrease rates of inappropriate care, through programs such as Choosing Wisely, that discourage use of low‐value services.[2] Finally, a third approach adopted by the Medicare Shared Savings Program strives to reduce both episode costs and rates of inappropriate care, by incorporating annual per capita Medicare spending into performance benchmarks.[3] Given these ongoing efforts, it would be important to compare the potential impact of reducing episode payments versus rates of care on total costs of care.

METHODS

For 3 common surgical procedures, we compared the relative influence of procedure rates versus episode payments (among those with procedures) on total Medicare expenditures.

We used complete Part A and B Medicare claims data for: coronary artery bypass grafting (CABG), prostatectomy, and hip replacement. We used International Classification of Diseases, Ninth Revision codes to identify the procedures (CABG: 361.0, 361.1, 361.2, 361.3, 361.4, 361.5, 361.6, 361.7, 361.9, 36.2; prostatectomy: 60.4, 60.5, 60.62 with a prostate cancer diagnosis code of 185 or 233.4; and hip replacement: 81.51, 81.52 excluding hip fracture codes 820.0, 820.1, 820.2, 820.3, 820.8, 820.9).

For each procedure, we estimated age‐ and sex‐adjusted episode rates for each hospital referral region (HRR). The numerator was the number of admissions to an acute care hospital for CABG (total n = 118,185), prostatectomy (total n = 18,328), or hip replacement (total n = 178,982) from January 2009 to June 2010. The denominator was fee‐for‐service Medicare beneficiaries age 65 years or older. We excluded those without continuous Part A and B enrollment (total denominator n = 23,403,051). Females were also excluded from the prostatectomy cohort.

For each of the 306 HRRs, we next calculated average HRR‐level episode payments. Using CABG as an example, we aggregated up the risk‐adjusted (age, sex, race, admission type, Elixhauser[4] comorbidities), price‐standardized[5] episode payments for all CABG patients residing in an HRR, and divided this by the number of CABG patients living in that HRR.

Finally, we obtained baseline per capita spending by multiplying the age‐ and sex‐adjusted CABG episode rate by the average CABG episode payment in that HRR. All payments were standardized to 2010 dollars using the Consumer Price Index.

We simulated changes in per capita Medicare spending for CABG across all HRRs under 2 scenarios: (1) reducing HRR‐level rates to the median versus (2) reducing HRR‐level episode payments to the median. We repeated this for prostatectomy and hip replacement.

RESULTS

Age‐ and sex‐adjusted rates of CABG varied more than risk‐adjusted, price‐standardized episode payments (90th:10th percentile of 2.0 for rates vs 1.2 for payments) (see Supporting Information, Appendix, in the online version of this article). Reducing rates of CABG to the 50th percentile decreased per capita episode payments by 11.1%. In contrast, reducing CABG episode payments to the 50th percentile decreased per capita episode payments by 3.6%. The absolute difference between the 2 simulations was 7.5% (95% confidence interval [CI]: 5.6%‐9.4%) (Figure 1). Results were similar for prostatectomy and hip replacement. In sensitivity analyses, reducing hospital‐level episode payments (rather than HRR‐level episode payments) produced similar findings. Employing the 90th percentile as a cutoff (instead of the median) also produced qualitatively similar results.

Figure 1

DISCUSSION

For 3 common surgical procedures, reducing procedure rates lowers total Medicare spending substantially more than reducing episode payments. These findings are attributable to a much greater variation in procedure rates compared to episode‐based payments. Prior research has documented wide variation in rates of surgical procedures.[6] This may be due to a number of factors, including physician beliefs about indications for surgery, as well as the degree to which patient preferences are incorporated into decision making.[6]

Our findings suggest that it would be important to incorporate population‐based episode rates into efforts aimed at incentivizing higher value care. Incentives tied to population‐based episode rates are difficult to design well. They may need to be paired with appropriateness criteria to avoid stinting on care. Attribution of a population to a hospital (including those who are not admitted to a hospital) is also complex.[7] Finally, hospitals are not solely responsible for rates of care, because the decision to admit a patient is sometimes made in the emergency department (eg, for chronic medical conditions), but at other times is made in the outpatient arena (eg, for elective surgery). Nevertheless, a narrow focus on per episode spending limits the potential impact of efforts to control Medicare spending.

Payers, providers, and policymakers are testing several major approaches to reducing US healthcare spending without harming quality. One strategy is to bundle payments for a longitudinal episode of care, as in Medicare's popular Bundled Payments for Care Improvement initiative.[1] A second approach is to decrease rates of inappropriate care, through programs such as Choosing Wisely, that discourage use of low‐value services.[2] Finally, a third approach adopted by the Medicare Shared Savings Program strives to reduce both episode costs and rates of inappropriate care, by incorporating annual per capita Medicare spending into performance benchmarks.[3] Given these ongoing efforts, it would be important to compare the potential impact of reducing episode payments versus rates of care on total costs of care.

METHODS

For 3 common surgical procedures, we compared the relative influence of procedure rates versus episode payments (among those with procedures) on total Medicare expenditures.

We used complete Part A and B Medicare claims data for: coronary artery bypass grafting (CABG), prostatectomy, and hip replacement. We used International Classification of Diseases, Ninth Revision codes to identify the procedures (CABG: 361.0, 361.1, 361.2, 361.3, 361.4, 361.5, 361.6, 361.7, 361.9, 36.2; prostatectomy: 60.4, 60.5, 60.62 with a prostate cancer diagnosis code of 185 or 233.4; and hip replacement: 81.51, 81.52 excluding hip fracture codes 820.0, 820.1, 820.2, 820.3, 820.8, 820.9).

For each procedure, we estimated age‐ and sex‐adjusted episode rates for each hospital referral region (HRR). The numerator was the number of admissions to an acute care hospital for CABG (total n = 118,185), prostatectomy (total n = 18,328), or hip replacement (total n = 178,982) from January 2009 to June 2010. The denominator was fee‐for‐service Medicare beneficiaries age 65 years or older. We excluded those without continuous Part A and B enrollment (total denominator n = 23,403,051). Females were also excluded from the prostatectomy cohort.

For each of the 306 HRRs, we next calculated average HRR‐level episode payments. Using CABG as an example, we aggregated up the risk‐adjusted (age, sex, race, admission type, Elixhauser[4] comorbidities), price‐standardized[5] episode payments for all CABG patients residing in an HRR, and divided this by the number of CABG patients living in that HRR.

Finally, we obtained baseline per capita spending by multiplying the age‐ and sex‐adjusted CABG episode rate by the average CABG episode payment in that HRR. All payments were standardized to 2010 dollars using the Consumer Price Index.

We simulated changes in per capita Medicare spending for CABG across all HRRs under 2 scenarios: (1) reducing HRR‐level rates to the median versus (2) reducing HRR‐level episode payments to the median. We repeated this for prostatectomy and hip replacement.

RESULTS

Age‐ and sex‐adjusted rates of CABG varied more than risk‐adjusted, price‐standardized episode payments (90th:10th percentile of 2.0 for rates vs 1.2 for payments) (see Supporting Information, Appendix, in the online version of this article). Reducing rates of CABG to the 50th percentile decreased per capita episode payments by 11.1%. In contrast, reducing CABG episode payments to the 50th percentile decreased per capita episode payments by 3.6%. The absolute difference between the 2 simulations was 7.5% (95% confidence interval [CI]: 5.6%‐9.4%) (Figure 1). Results were similar for prostatectomy and hip replacement. In sensitivity analyses, reducing hospital‐level episode payments (rather than HRR‐level episode payments) produced similar findings. Employing the 90th percentile as a cutoff (instead of the median) also produced qualitatively similar results.

Figure 1

DISCUSSION

For 3 common surgical procedures, reducing procedure rates lowers total Medicare spending substantially more than reducing episode payments. These findings are attributable to a much greater variation in procedure rates compared to episode‐based payments. Prior research has documented wide variation in rates of surgical procedures.[6] This may be due to a number of factors, including physician beliefs about indications for surgery, as well as the degree to which patient preferences are incorporated into decision making.[6]

Our findings suggest that it would be important to incorporate population‐based episode rates into efforts aimed at incentivizing higher value care. Incentives tied to population‐based episode rates are difficult to design well. They may need to be paired with appropriateness criteria to avoid stinting on care. Attribution of a population to a hospital (including those who are not admitted to a hospital) is also complex.[7] Finally, hospitals are not solely responsible for rates of care, because the decision to admit a patient is sometimes made in the emergency department (eg, for chronic medical conditions), but at other times is made in the outpatient arena (eg, for elective surgery). Nevertheless, a narrow focus on per episode spending limits the potential impact of efforts to control Medicare spending.

Over 90% of Americans own mobile phones, and their use for internet access is rising rapidly (31% in 2009, 63% in 2013).[1] This has prompted growth in mobile health (mHealth) programs for outpatient settings,[2] and similar growth is anticipated for inpatient settings.[3] Hospitals and the healthcare systems they operate within are increasingly tied to patient experience scores (eg, Hospital Consumer Assessment of Healthcare Providers and Systems, Press Ganey) for both reputation and reimbursement.[4, 5] As a result, hospitals will need to invest future resources in a consumer‐facing digital experience. Despite these trends, basic information on mobile device ownership and usage by hospitalized patients is limited. This knowledge is needed to guide successful mHealth approaches to engage patients in acute care settings.

METHODS

We administered a 27‐question survey about mobile device use to all adult inpatients at a large urban California teaching hospital over 2 dates (October 27, 2013 and November 11, 2013) to create a cross‐sectional view of mobile device use at a hospital that offers free wireless Internet (WiFi) and personal health records (Internet‐accessible individualized medical records). Average census was 447, and we excluded patients for: age under 18 years (98), admission for neurological problems (75), altered mental status (35), nonEnglish speaking (30), or unavailable if patients were not in their room after 2 attempts spaced 30 to 60 minutes apart (36), leaving 173 eligible. We performed descriptive statistics and unadjusted associations ([2] test) to explore patterns of mobile device use.

RESULTS

We enrolled 152 patients (88% response rate): 77 (51%) male, average age 53 years (1992 years), 84 (56%) white, 115 (75%) with Medicare or commercial insurance. We found 85 (56%) patients brought a smartphone, and 82/85 (95%) used it during their hospital stay. Additionally, 41 (27%) patients brought a tablet, and 29 (19%) brought a laptop; usage was 37/41 (90%) for tablets and 24/29 (83%) for laptops. One hundred three (68%) patients brought at least 1 mobile computing device (smartphone, tablet, laptop) during their hospital stay. Overall device usage was highest among oncology patients (85%) and lowest among medicine patients (54%) (Table 1). Device usage also varied by age (<65 years old: 79% vs 65 years old: 27%), insurance status (private/Medicare: 70% vs Medicaid/other: 59%), and race/ethnicity (white: 73% vs non‐white: 62%), although only age was statistically significant (P<0.01; all others >0.05).

Of the patients with mobile devices (smartphone, tablet, laptop), 97/103 (94%) used them during their hospitalization and for a wide array of activities (Figure 1): 47/97 (48%) accessed their personal health record (PHR), and most of these patients (38/47, 81%) reported this improved their inpatient experience. Additionally, 43/97 (44%) patients used their mobile devices to search for information about doctors, conditions, or treatments; most of these patients (39/43, 91%) used Google to search for this information, and most 29/43 (67%) felt this information made them more confident in their care.

Figure 1

COMMENT

Over two‐thirds of patients in our study brought and used 1 or more mobile devices to the hospital. Despite this level of engagement with mobile devices, relatively few inpatients used their device to access their online PHR, which suggests information technology access is not the leading barrier to PHR access or mHealth engagement during hospitalization. In light of growing patient enthusiasm for PHRs,[6, 7] this represents an untapped opportunity to deliver personalized, patient‐centered care at the hospital bedside.

We also found that among the patients who did access their PHR on their mobile device, the vast majority (38/47, 81%) felt it improved their inpatient experience. Our PHR provides information such as test results and medications, but our survey suggests a number of patients look for health information, such as patient education tools, medication references, and provider information, outside of the PHR. For those patients, 29/43 (67%) felt these health‐related searches improved their experience. Although we did not ask patients why they used Web searches outside their PHR, we believe this suggests that patients desire more information than currently available via the PHR. Although this information might be difficult to incorporate into the PHR, at minimum, hospitals could develop mobile applications to provide patients with basic information about their providers and conditions. Beyond this, hospitals could develop or adopt mobile applications that align with strategic priorities such as improved physician‐provider communication, reduced hospital readmissions, and improved accuracy of medication reconciliation.

Our study has limitations. First, although we used a cross‐sectional, point‐in‐time approach to canvas the entire adult population in our hospital on 2 separate dates, our study was limited to 1 large urban hospital in California; device ownership and usage may vary in other settings. Second, although our hospital provides free WiFi, we did not assess whether patients experienced any connectivity issues that influenced their device usage patterns. Finally, we did not explore questions of access, ownership, and usage of mobile computing devices for family and friends who visited inpatients in our study. These questions are ripe for future research in this emerging area of mHeath.

In summary, our study suggests a role for hospitals to provide universal WiFi access to patients, and a role for both hospitals and healthcare providers to promote digital health programs. Our findings on mobile device use in the hospital are consistent with the growing popularity of mobile device usage nationwide. Patients are increasingly wired for new opportunities to both engage in their care and optimize their hospital experience through use of their mobile computing devices. Hospitals and providers should explore this potential for engagement, but may need to explore local trends in usage to target specific service lines and patient populations given differences in access and use.

Over 90% of Americans own mobile phones, and their use for internet access is rising rapidly (31% in 2009, 63% in 2013).[1] This has prompted growth in mobile health (mHealth) programs for outpatient settings,[2] and similar growth is anticipated for inpatient settings.[3] Hospitals and the healthcare systems they operate within are increasingly tied to patient experience scores (eg, Hospital Consumer Assessment of Healthcare Providers and Systems, Press Ganey) for both reputation and reimbursement.[4, 5] As a result, hospitals will need to invest future resources in a consumer‐facing digital experience. Despite these trends, basic information on mobile device ownership and usage by hospitalized patients is limited. This knowledge is needed to guide successful mHealth approaches to engage patients in acute care settings.

METHODS

We administered a 27‐question survey about mobile device use to all adult inpatients at a large urban California teaching hospital over 2 dates (October 27, 2013 and November 11, 2013) to create a cross‐sectional view of mobile device use at a hospital that offers free wireless Internet (WiFi) and personal health records (Internet‐accessible individualized medical records). Average census was 447, and we excluded patients for: age under 18 years (98), admission for neurological problems (75), altered mental status (35), nonEnglish speaking (30), or unavailable if patients were not in their room after 2 attempts spaced 30 to 60 minutes apart (36), leaving 173 eligible. We performed descriptive statistics and unadjusted associations ([2] test) to explore patterns of mobile device use.

RESULTS

We enrolled 152 patients (88% response rate): 77 (51%) male, average age 53 years (1992 years), 84 (56%) white, 115 (75%) with Medicare or commercial insurance. We found 85 (56%) patients brought a smartphone, and 82/85 (95%) used it during their hospital stay. Additionally, 41 (27%) patients brought a tablet, and 29 (19%) brought a laptop; usage was 37/41 (90%) for tablets and 24/29 (83%) for laptops. One hundred three (68%) patients brought at least 1 mobile computing device (smartphone, tablet, laptop) during their hospital stay. Overall device usage was highest among oncology patients (85%) and lowest among medicine patients (54%) (Table 1). Device usage also varied by age (<65 years old: 79% vs 65 years old: 27%), insurance status (private/Medicare: 70% vs Medicaid/other: 59%), and race/ethnicity (white: 73% vs non‐white: 62%), although only age was statistically significant (P<0.01; all others >0.05).

Of the patients with mobile devices (smartphone, tablet, laptop), 97/103 (94%) used them during their hospitalization and for a wide array of activities (Figure 1): 47/97 (48%) accessed their personal health record (PHR), and most of these patients (38/47, 81%) reported this improved their inpatient experience. Additionally, 43/97 (44%) patients used their mobile devices to search for information about doctors, conditions, or treatments; most of these patients (39/43, 91%) used Google to search for this information, and most 29/43 (67%) felt this information made them more confident in their care.

Figure 1

COMMENT

Over two‐thirds of patients in our study brought and used 1 or more mobile devices to the hospital. Despite this level of engagement with mobile devices, relatively few inpatients used their device to access their online PHR, which suggests information technology access is not the leading barrier to PHR access or mHealth engagement during hospitalization. In light of growing patient enthusiasm for PHRs,[6, 7] this represents an untapped opportunity to deliver personalized, patient‐centered care at the hospital bedside.

We also found that among the patients who did access their PHR on their mobile device, the vast majority (38/47, 81%) felt it improved their inpatient experience. Our PHR provides information such as test results and medications, but our survey suggests a number of patients look for health information, such as patient education tools, medication references, and provider information, outside of the PHR. For those patients, 29/43 (67%) felt these health‐related searches improved their experience. Although we did not ask patients why they used Web searches outside their PHR, we believe this suggests that patients desire more information than currently available via the PHR. Although this information might be difficult to incorporate into the PHR, at minimum, hospitals could develop mobile applications to provide patients with basic information about their providers and conditions. Beyond this, hospitals could develop or adopt mobile applications that align with strategic priorities such as improved physician‐provider communication, reduced hospital readmissions, and improved accuracy of medication reconciliation.

Our study has limitations. First, although we used a cross‐sectional, point‐in‐time approach to canvas the entire adult population in our hospital on 2 separate dates, our study was limited to 1 large urban hospital in California; device ownership and usage may vary in other settings. Second, although our hospital provides free WiFi, we did not assess whether patients experienced any connectivity issues that influenced their device usage patterns. Finally, we did not explore questions of access, ownership, and usage of mobile computing devices for family and friends who visited inpatients in our study. These questions are ripe for future research in this emerging area of mHeath.

In summary, our study suggests a role for hospitals to provide universal WiFi access to patients, and a role for both hospitals and healthcare providers to promote digital health programs. Our findings on mobile device use in the hospital are consistent with the growing popularity of mobile device usage nationwide. Patients are increasingly wired for new opportunities to both engage in their care and optimize their hospital experience through use of their mobile computing devices. Hospitals and providers should explore this potential for engagement, but may need to explore local trends in usage to target specific service lines and patient populations given differences in access and use.

Over 90% of Americans own mobile phones, and their use for internet access is rising rapidly (31% in 2009, 63% in 2013).[1] This has prompted growth in mobile health (mHealth) programs for outpatient settings,[2] and similar growth is anticipated for inpatient settings.[3] Hospitals and the healthcare systems they operate within are increasingly tied to patient experience scores (eg, Hospital Consumer Assessment of Healthcare Providers and Systems, Press Ganey) for both reputation and reimbursement.[4, 5] As a result, hospitals will need to invest future resources in a consumer‐facing digital experience. Despite these trends, basic information on mobile device ownership and usage by hospitalized patients is limited. This knowledge is needed to guide successful mHealth approaches to engage patients in acute care settings.

METHODS

We administered a 27‐question survey about mobile device use to all adult inpatients at a large urban California teaching hospital over 2 dates (October 27, 2013 and November 11, 2013) to create a cross‐sectional view of mobile device use at a hospital that offers free wireless Internet (WiFi) and personal health records (Internet‐accessible individualized medical records). Average census was 447, and we excluded patients for: age under 18 years (98), admission for neurological problems (75), altered mental status (35), nonEnglish speaking (30), or unavailable if patients were not in their room after 2 attempts spaced 30 to 60 minutes apart (36), leaving 173 eligible. We performed descriptive statistics and unadjusted associations ([2] test) to explore patterns of mobile device use.

RESULTS

We enrolled 152 patients (88% response rate): 77 (51%) male, average age 53 years (1992 years), 84 (56%) white, 115 (75%) with Medicare or commercial insurance. We found 85 (56%) patients brought a smartphone, and 82/85 (95%) used it during their hospital stay. Additionally, 41 (27%) patients brought a tablet, and 29 (19%) brought a laptop; usage was 37/41 (90%) for tablets and 24/29 (83%) for laptops. One hundred three (68%) patients brought at least 1 mobile computing device (smartphone, tablet, laptop) during their hospital stay. Overall device usage was highest among oncology patients (85%) and lowest among medicine patients (54%) (Table 1). Device usage also varied by age (<65 years old: 79% vs 65 years old: 27%), insurance status (private/Medicare: 70% vs Medicaid/other: 59%), and race/ethnicity (white: 73% vs non‐white: 62%), although only age was statistically significant (P<0.01; all others >0.05).

Of the patients with mobile devices (smartphone, tablet, laptop), 97/103 (94%) used them during their hospitalization and for a wide array of activities (Figure 1): 47/97 (48%) accessed their personal health record (PHR), and most of these patients (38/47, 81%) reported this improved their inpatient experience. Additionally, 43/97 (44%) patients used their mobile devices to search for information about doctors, conditions, or treatments; most of these patients (39/43, 91%) used Google to search for this information, and most 29/43 (67%) felt this information made them more confident in their care.

Figure 1

COMMENT

Over two‐thirds of patients in our study brought and used 1 or more mobile devices to the hospital. Despite this level of engagement with mobile devices, relatively few inpatients used their device to access their online PHR, which suggests information technology access is not the leading barrier to PHR access or mHealth engagement during hospitalization. In light of growing patient enthusiasm for PHRs,[6, 7] this represents an untapped opportunity to deliver personalized, patient‐centered care at the hospital bedside.

We also found that among the patients who did access their PHR on their mobile device, the vast majority (38/47, 81%) felt it improved their inpatient experience. Our PHR provides information such as test results and medications, but our survey suggests a number of patients look for health information, such as patient education tools, medication references, and provider information, outside of the PHR. For those patients, 29/43 (67%) felt these health‐related searches improved their experience. Although we did not ask patients why they used Web searches outside their PHR, we believe this suggests that patients desire more information than currently available via the PHR. Although this information might be difficult to incorporate into the PHR, at minimum, hospitals could develop mobile applications to provide patients with basic information about their providers and conditions. Beyond this, hospitals could develop or adopt mobile applications that align with strategic priorities such as improved physician‐provider communication, reduced hospital readmissions, and improved accuracy of medication reconciliation.

Our study has limitations. First, although we used a cross‐sectional, point‐in‐time approach to canvas the entire adult population in our hospital on 2 separate dates, our study was limited to 1 large urban hospital in California; device ownership and usage may vary in other settings. Second, although our hospital provides free WiFi, we did not assess whether patients experienced any connectivity issues that influenced their device usage patterns. Finally, we did not explore questions of access, ownership, and usage of mobile computing devices for family and friends who visited inpatients in our study. These questions are ripe for future research in this emerging area of mHeath.

In summary, our study suggests a role for hospitals to provide universal WiFi access to patients, and a role for both hospitals and healthcare providers to promote digital health programs. Our findings on mobile device use in the hospital are consistent with the growing popularity of mobile device usage nationwide. Patients are increasingly wired for new opportunities to both engage in their care and optimize their hospital experience through use of their mobile computing devices. Hospitals and providers should explore this potential for engagement, but may need to explore local trends in usage to target specific service lines and patient populations given differences in access and use.

Hand hygiene is a proven and guideline‐recommended safety practice, although clinicians and particularly physicians are unreliable at performing it.[1] Like hands, stethoscopes can carry pathogens from patient to patient. In 1 study, stethoscopes were as likely to be contaminated after use with methicillin‐resistant Staphylococcus aureuspositive patients as the provider's hands.[2] Furthermore, like hands, stethoscopes can be effectively decolonized with alcohol.[3, 4] However, although hand hygiene rates have been extensively studied,[1] and hand hygiene has been linked to reductions in nosocomial infection,[5] stethoscope hygiene is less well studied and emphasized less by guidelines.[6] Several surveys have documented low self‐reported compliance with stethoscope hygiene.[7, 8, 9, 10] Of 150 healthcare workers, 48% reported stethoscope hygiene between daily and weekly, 37% did stethoscope hygiene monthly, and 7% did stethoscope hygiene annually or never.[8] Of 1401 doctors asked about their stethoscope hygiene beliefs and practices, 76% believed that stethoscopes could transmit infection, but only 24% reported cleaning their scopes regularly.[9] Moreover, of 308 students, 22% had never done stethoscope hygiene, and <4% did it consistently.[10] However, we were unable to find any data on observed rates of stethoscope hygiene. Thus, we observed student and trainee physician stethoscope hygiene performance during hospital medicine rotations as part of the baseline data‐collection phase of a quality‐improvement effort linked to hand hygiene efforts.

METHODS

Attending hospitalists (I.H.J., B.M., and A.A.) and 1 graduate assistant (J.W.) at 3 sites observed stethoscope hygiene opportunities over an 11‐month period. Stethoscope hygiene was counted as performed if a patient‐specific stethoscope was used in an isolation room, or if any type of cleaning (alcohol gel, alcohol wipe, or cleansing cloth) was performed on a stethoscope carried out of the room. Observers also recorded whether stethoscope hygiene opportunities occurred in isolation rooms or nonisolation rooms, and noted if stethoscope hygiene was obviously triggered by an attending's stethoscope hygiene behavior (eg, a trainee asked an attending why he performed stethoscope hygiene, then performed it him or herself). Trainees were not aware that their stethoscope hygiene behaviors were being recorded.

RESULTS

We observed 352 opportunities for stethoscope hygiene, in which doctors or students used stethoscope hygiene in 58 encounters (16%). Twenty of the 58 stethoscope hygiene events occurred only after a trainee observed an attending physician perform stethoscope hygiene. Eliminating stethoscope hygiene events that were triggered by attending physicians, stethoscope hygiene was performed in 38 of 332 opportunities (11%). There was a significant difference between the rate of stethoscope hygiene performed in isolation versus nonisolation rooms: 24/29 (82.7%) versus 14 of 303 (4.6%) (P<0.001 by Pearson ² statistic). In isolation room stethoscope hygiene, in which the type of hygiene was recorded, 18 of 20 (90%) involved use of an isolation stethoscope, and 2 of 20 (10%) involved cleaning of a personal stethoscope.

DISCUSSION

Stethoscope hygiene is rarely performed by trainees. Stethoscope hygiene performance depends on the isolation status of the patient, with more than 80% performance in isolated patients and <5% in nonisolated patients.

Although little is known about the rate of infection related to stethoscopes, colonization of stethoscopes with nosocomial bacteria is well described.[2] Transmission of pathogens from patient to patient by stethoscopes could undermine the benefits of hand hygiene programs, as patients are commonly exposed to unclean stethoscopes.

Our observations are limited by several factors. We used a convenience sample of general medicine trainee behavior at academic medical centers; the behavior of attending physicians, ancillary staff, and nonacademic physicians may be different. Moreover, attending behavior may have prompted more episodes of stethoscope hygiene performance than we recorded, because we only noted when stethoscope hygiene was clearly related to attending behavior. The very low rate of stethoscope hygiene after contact with nonisolation patients represents a current and potentially serious safety threat. Future research might be able to quantify the risk associated with uncleaned stethoscopes or demonstrate the effectiveness of stethoscope hygiene programs. The effect of modeling on hand hygiene and stethoscope hygiene[10, 11] and on stethoscope hygiene in our data suggests a method for improving stethoscope hygiene.

Hand hygiene is a proven and guideline‐recommended safety practice, although clinicians and particularly physicians are unreliable at performing it.[1] Like hands, stethoscopes can carry pathogens from patient to patient. In 1 study, stethoscopes were as likely to be contaminated after use with methicillin‐resistant Staphylococcus aureuspositive patients as the provider's hands.[2] Furthermore, like hands, stethoscopes can be effectively decolonized with alcohol.[3, 4] However, although hand hygiene rates have been extensively studied,[1] and hand hygiene has been linked to reductions in nosocomial infection,[5] stethoscope hygiene is less well studied and emphasized less by guidelines.[6] Several surveys have documented low self‐reported compliance with stethoscope hygiene.[7, 8, 9, 10] Of 150 healthcare workers, 48% reported stethoscope hygiene between daily and weekly, 37% did stethoscope hygiene monthly, and 7% did stethoscope hygiene annually or never.[8] Of 1401 doctors asked about their stethoscope hygiene beliefs and practices, 76% believed that stethoscopes could transmit infection, but only 24% reported cleaning their scopes regularly.[9] Moreover, of 308 students, 22% had never done stethoscope hygiene, and <4% did it consistently.[10] However, we were unable to find any data on observed rates of stethoscope hygiene. Thus, we observed student and trainee physician stethoscope hygiene performance during hospital medicine rotations as part of the baseline data‐collection phase of a quality‐improvement effort linked to hand hygiene efforts.

METHODS

Attending hospitalists (I.H.J., B.M., and A.A.) and 1 graduate assistant (J.W.) at 3 sites observed stethoscope hygiene opportunities over an 11‐month period. Stethoscope hygiene was counted as performed if a patient‐specific stethoscope was used in an isolation room, or if any type of cleaning (alcohol gel, alcohol wipe, or cleansing cloth) was performed on a stethoscope carried out of the room. Observers also recorded whether stethoscope hygiene opportunities occurred in isolation rooms or nonisolation rooms, and noted if stethoscope hygiene was obviously triggered by an attending's stethoscope hygiene behavior (eg, a trainee asked an attending why he performed stethoscope hygiene, then performed it him or herself). Trainees were not aware that their stethoscope hygiene behaviors were being recorded.

RESULTS

We observed 352 opportunities for stethoscope hygiene, in which doctors or students used stethoscope hygiene in 58 encounters (16%). Twenty of the 58 stethoscope hygiene events occurred only after a trainee observed an attending physician perform stethoscope hygiene. Eliminating stethoscope hygiene events that were triggered by attending physicians, stethoscope hygiene was performed in 38 of 332 opportunities (11%). There was a significant difference between the rate of stethoscope hygiene performed in isolation versus nonisolation rooms: 24/29 (82.7%) versus 14 of 303 (4.6%) (P<0.001 by Pearson ² statistic). In isolation room stethoscope hygiene, in which the type of hygiene was recorded, 18 of 20 (90%) involved use of an isolation stethoscope, and 2 of 20 (10%) involved cleaning of a personal stethoscope.

DISCUSSION

Stethoscope hygiene is rarely performed by trainees. Stethoscope hygiene performance depends on the isolation status of the patient, with more than 80% performance in isolated patients and <5% in nonisolated patients.

Although little is known about the rate of infection related to stethoscopes, colonization of stethoscopes with nosocomial bacteria is well described.[2] Transmission of pathogens from patient to patient by stethoscopes could undermine the benefits of hand hygiene programs, as patients are commonly exposed to unclean stethoscopes.

Our observations are limited by several factors. We used a convenience sample of general medicine trainee behavior at academic medical centers; the behavior of attending physicians, ancillary staff, and nonacademic physicians may be different. Moreover, attending behavior may have prompted more episodes of stethoscope hygiene performance than we recorded, because we only noted when stethoscope hygiene was clearly related to attending behavior. The very low rate of stethoscope hygiene after contact with nonisolation patients represents a current and potentially serious safety threat. Future research might be able to quantify the risk associated with uncleaned stethoscopes or demonstrate the effectiveness of stethoscope hygiene programs. The effect of modeling on hand hygiene and stethoscope hygiene[10, 11] and on stethoscope hygiene in our data suggests a method for improving stethoscope hygiene.

Hand hygiene is a proven and guideline‐recommended safety practice, although clinicians and particularly physicians are unreliable at performing it.[1] Like hands, stethoscopes can carry pathogens from patient to patient. In 1 study, stethoscopes were as likely to be contaminated after use with methicillin‐resistant Staphylococcus aureuspositive patients as the provider's hands.[2] Furthermore, like hands, stethoscopes can be effectively decolonized with alcohol.[3, 4] However, although hand hygiene rates have been extensively studied,[1] and hand hygiene has been linked to reductions in nosocomial infection,[5] stethoscope hygiene is less well studied and emphasized less by guidelines.[6] Several surveys have documented low self‐reported compliance with stethoscope hygiene.[7, 8, 9, 10] Of 150 healthcare workers, 48% reported stethoscope hygiene between daily and weekly, 37% did stethoscope hygiene monthly, and 7% did stethoscope hygiene annually or never.[8] Of 1401 doctors asked about their stethoscope hygiene beliefs and practices, 76% believed that stethoscopes could transmit infection, but only 24% reported cleaning their scopes regularly.[9] Moreover, of 308 students, 22% had never done stethoscope hygiene, and <4% did it consistently.[10] However, we were unable to find any data on observed rates of stethoscope hygiene. Thus, we observed student and trainee physician stethoscope hygiene performance during hospital medicine rotations as part of the baseline data‐collection phase of a quality‐improvement effort linked to hand hygiene efforts.

METHODS

Attending hospitalists (I.H.J., B.M., and A.A.) and 1 graduate assistant (J.W.) at 3 sites observed stethoscope hygiene opportunities over an 11‐month period. Stethoscope hygiene was counted as performed if a patient‐specific stethoscope was used in an isolation room, or if any type of cleaning (alcohol gel, alcohol wipe, or cleansing cloth) was performed on a stethoscope carried out of the room. Observers also recorded whether stethoscope hygiene opportunities occurred in isolation rooms or nonisolation rooms, and noted if stethoscope hygiene was obviously triggered by an attending's stethoscope hygiene behavior (eg, a trainee asked an attending why he performed stethoscope hygiene, then performed it him or herself). Trainees were not aware that their stethoscope hygiene behaviors were being recorded.

RESULTS

We observed 352 opportunities for stethoscope hygiene, in which doctors or students used stethoscope hygiene in 58 encounters (16%). Twenty of the 58 stethoscope hygiene events occurred only after a trainee observed an attending physician perform stethoscope hygiene. Eliminating stethoscope hygiene events that were triggered by attending physicians, stethoscope hygiene was performed in 38 of 332 opportunities (11%). There was a significant difference between the rate of stethoscope hygiene performed in isolation versus nonisolation rooms: 24/29 (82.7%) versus 14 of 303 (4.6%) (P<0.001 by Pearson ² statistic). In isolation room stethoscope hygiene, in which the type of hygiene was recorded, 18 of 20 (90%) involved use of an isolation stethoscope, and 2 of 20 (10%) involved cleaning of a personal stethoscope.

DISCUSSION

Stethoscope hygiene is rarely performed by trainees. Stethoscope hygiene performance depends on the isolation status of the patient, with more than 80% performance in isolated patients and <5% in nonisolated patients.

Although little is known about the rate of infection related to stethoscopes, colonization of stethoscopes with nosocomial bacteria is well described.[2] Transmission of pathogens from patient to patient by stethoscopes could undermine the benefits of hand hygiene programs, as patients are commonly exposed to unclean stethoscopes.

Our observations are limited by several factors. We used a convenience sample of general medicine trainee behavior at academic medical centers; the behavior of attending physicians, ancillary staff, and nonacademic physicians may be different. Moreover, attending behavior may have prompted more episodes of stethoscope hygiene performance than we recorded, because we only noted when stethoscope hygiene was clearly related to attending behavior. The very low rate of stethoscope hygiene after contact with nonisolation patients represents a current and potentially serious safety threat. Future research might be able to quantify the risk associated with uncleaned stethoscopes or demonstrate the effectiveness of stethoscope hygiene programs. The effect of modeling on hand hygiene and stethoscope hygiene[10, 11] and on stethoscope hygiene in our data suggests a method for improving stethoscope hygiene.

Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.

METHODS

From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).

Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.

Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).

Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).

Figure 1

One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).

Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)

DISCUSSION

We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.

Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.

Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.

Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]

Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.

METHODS

From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).

Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.

Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).

Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).

Figure 1

One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).

Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)

DISCUSSION

We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.

Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.

Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.

Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]

Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.

METHODS

From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).

Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.

Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).

Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).

Figure 1

One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).

Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)

DISCUSSION

We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.

Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.

Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.

Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]