Hospital‐Wide Readmission Rates

Article Type
Article
Changed
Mon, 01/02/2017 - 19:34
Display Headline
Hospital characteristics and 30‐day all‐cause readmission rates
Author(s)
Mona Al‐Amin, PhD, MPH

The hospital‐wide all‐cause 30‐day readmission rate is a key quality measure associated with patient outcomes, cost of care, and wasted hospital resources.[1] The estimated 20% readmission rate of Medicare patients and the associated $17 billion annual cost of readmissions led the Centers for Medicare and Medicaid Services (CMS) to implement policies that limit reimbursement for 30‐day unplanned readmissions and thus place hospitals with high readmission rates at financial risk.[1, 2]

The variation in readmission rates between hospitals is well documented in the literature.[3, 4] Singh et al. found that 9.3% of the variation in readmissions can be explained by hospital characteristics.[4] Hospital factors associated with lower readmission rates include not‐for‐profit ownership, hospital size, and nursing staffing levels.[5, 6, 7] Other studies found an association between environmental factors such as the percent of patients living under the poverty line and higher readmission rates.[7] The recent publicly available CMS data on readmission rates allows us to further our understanding of hospital characteristics that explain the variation in readmission rates. In this article, we are specifically interested in hospitalist staffing levels and hospital‐physician arrangements such as physician integration level and physician ownership. Moreover, we are interested in novel organizational variables, specifically, the adoption of a medical home model, which has been ignored by previous research. Medical homes are associated with better quality[8]; hospitals that already adopted the medical home model might be better equipped to coordinate care after the patients are discharged.

In recent years, the number of hospitals relying on hospitalists to provide inpatient care has been on the rise. As more hospitals employ hospitalists, it is important to understand how hospitalist staffing levels are associated with quality. Previous studies have linked hospitalists with lower hospital mortality rates,[7] lower cost of care,[9, 10] and lower readmission rates.[10, 11] Goodrich et al., on the other hand, did not find a significant relationship between the presence of hospitalists and mortality or readmission rates.[12] In a recent study, hospitalists indicated that heavy workloads limited the time they had available to communicate with patients, which negatively influenced quality and patient satisfaction, and resulted in delayed admissions and discharges.[13]

The main objective of this article was therefore to study the association between hospitalist staffing levels and hospital‐wide all‐cause readmission rates. Most empirical studies examining the relationship between hospitalist staffing and quality of inpatient care have predominantly focused on whether the presence of hospitalists who provided care at a hospital influenced mortality or readmissions.[11, 12] In this article, we contribute to the literature by examining how staffing levels measured by the ratio of hospitalists to general medical and surgical beds is associated with 30‐day readmission rates. We predict that there is a positive association between readmission rates and hospitalists per bed.

Hospitals have a broad range of contractual arrangements or integration levels with physicians, with employment being the highest level. A hospital can rely on physicians who have admitting privileges but are not salaried employees of the hospital to treat a large portion of its inpatient population. In the past few years, with the passage of the Patient Protection and Affordable Care Act (2010) and the shift in reimbursement towards Value Based Purchasing (VBP), more hospitals are choosing to ensure that physicians are strongly integrated within the hospital by adopting an employment‐based model. Moreover, hospitals view physician employment as a strategic move that will help ensure or expand their market share.[14] For instance, the number of surgeons who identified as self‐employed dropped from 48% in 2001 to 28% in 2011, and this reduction is attributed to the shift toward hospital employment of physicians.[15] Despite the evolving models of hospital‐physician arrangements, little is understood on how the adoption of the integrated salary model, in addition to the equity and foundation models, which are classified by Baker et al. as the highest level of integration, influence quality.[16] Therefore, another objective of this article was to examine the association between hospital‐physician arrangements and all‐cause unplanned readmission rates.

METHODS

Data Source and Sample

Data from the American Hospital Association (AHA) Annual Survey (2013), CMS Hospital Compare (October 2013), and Area Health Resource File (2013) were merged to analyze the association between readmission rates with hospital characteristics and environmental factors. We limited the analysis to private (nonpublic) hospitals with no missing data. Our final sample consisted of 1756 hospitals. Of the hospitals in our sample, 14% were for profit, 70% were nonteaching, 23% were minor teaching, 7% were major teaching hospitals, 73% belonged to a system, and 31% were classified as small hospitals. Table 1 provides descriptive statistics for all the variables included in the analysis.

Summary Statistics
Variable Value Data Source

  • NOTE: Abbreviations: IQR, interquartile range; RNs, registered nurses.

30‐day all‐cause readmissions, median (IQR) 15.8% (15.2%16.5%) Centers for Medicare and Medicaid Services
Hospitalists per general medicine and surgical beds, median (IQR) 0.09 (0.060.15) American Hospital Association
RNs per 100 inpatient days, median (IQR) 0.84 (0.6610.10) American Hospital Association
Medicare admissions, median (IQR) 48.45% (40.84%55.14%) American Hospital Association
Medicaid admissions, median (IQR) 16.45% (11.06%22.76%) American Hospital Association
Competition, median (IQR) 0.56 (0.230.83) American Hospital Association
Unemployment, median (IQR) 2.9% (2.54%3.37%) Area Resource File
Fully integrated American Hospital Association
Yes 51%
No 49%
Physician ownership American Hospital Association
Physician partial or complete ownership 5%
No physician ownership 95%
Established medical home program American Hospital Association
Yes 29%
No 71%
High technology American Hospital Association
Yes 40%
No 60%
Teaching level American Hospital Association
Nonteaching 70%
Minor teaching 23%
Major teaching 7%
Size American Hospital Association
Small 31%
Medium 34%
Large 35%
Ownership American Hospital Association
For profit 14%
Not for profit 86%
Critical access hospital American Hospital Association
Yes 11%
No 89%
System membership American Hospital Association
Yes 73%
No 27%

Variables

Dependent Variable

Risk standardized 30‐day hospital‐wide all‐cause readmission rates (HWR) were obtained from CMS. This measure was publicly reported in October 2013. The HWR is estimated using standardized risk ratios at the hospital level for the following 5 discharge diagnosis groups: surgery/gynecology, neurology, cardiorespiratory, cardiovascular, and general medicine.[17] The measure adjusts, in addition to a hospital's case mix, for patients' ages, principal discharge diagnoses, and comorbidities.[17] HWR is calculated as a predicted‐to‐expected readmissions ratio. Predicted and expected readmissions were calculated for each of the 5 groups for each hospital using each hospital's patient mix and a hospital random effects estimate. A standardized readmission ratio was then derived by dividing predicted readmissions by expected readmissions for each group for each hospital. A single hospital score was obtained by multiplying the volume‐weighted logarithmic average of the 5 diagnostic groups by the average national readmission rate.[18]

Independent Variables

The primary independent variable of interest to this study is hospitalist staffing levels. We calculate the staffing levels of hospitalists by dividing the full‐time equivalent (FTE) of hospitalists by the number of general medical and surgical beds. FTE hospitalists are calculated by the AHA Annual Survey database (2013) as the sum of full‐time hospitalists and 0.5*number of part‐time hospitalists. In addition to hospitalist staffing levels, a main predictors is whether the hospital fully integrates physicians or not. We follow Baker et al. in our classification of full integration. Baker et al. define fully integrated hospitals as those that adopted 1 of the following models with their physicians: integrated salary, foundation or equity model.[16] We predict that fully integrated hospitals are more likely to have better readmission rates. Another key physician variable that is likely to influence outcomes is physician partial or full ownership of the hospital. Ownership aligns physicians' incentives with hospital performance[19] and is therefore likely to be associated with better readmission rates. We also include a dichotomous variable that indicates whether a hospital has an established medical home program or not. Medical homes indicate an organizational culture that is patient centered and committed to continuity and coordination of care; all of which are important for better quality. We predict that the presence of a medical home model will be associated with better readmission rates.

Control Variables

We control for registered nurses per 100 inpatient days ratio, critical access designation, Medicare share of hospital admissions, Medicaid share of hospital admissions, teaching status, size, and technology level. Previous research indicates that these variables are associated with patient outcomes.[20, 21] We follow the Aiken et al. characterization of teaching status: hospitals with no residency programs (nonteaching), hospitals with a resident‐to‐bed ratio of 1 to 4 or less (minor teaching), and hospitals with a resident‐to‐bed ratio of more than 1 to 4 (major teaching).[20] We also classify hospitals as small if they have less than 100 beds, medium if they have 101 to 250 beds, and large if they have more than 250 beds. We modify the Aiken et al. classification of technology level and control for the level of technology adopted at a hospital by classifying hospitals as high technology if they offer any of the following services: any major organ transplant, computer‐assisted orthopedic surgery, or electron beam computed tomography.[21] We also control for 2 market level variables: (1) competition estimated by the county level Herfindahl‐Hirschman Index (HHI) and (2) the percentage of individuals in the county who are unemployed. Unemployment rates are derived from the Area Health Resource File (2013). HHI is calculated by summing the squares of market shares of admissions. For ease of interpretation, competition is coded as 1‐HHI.

Statistical Analysis

We ran a multivariate ordinary least squares (OLS) regression on Stata 12 (StataCorp, College Station, TX) to assess the relationship between 30‐day all‐cause readmissions and hospitalist staffing levels, physician integration, physician ownership, and other organizational characteristics. We checked for multicollinearity by using a variance inflation factor (VIF). The VIF of all independent variables was less than 10, and therefore multicollinearity was not of concern to this analysis.

RESULTS

Among our sample of 1756 hospitals, the median 30‐day all‐cause readmission rate was 16%, with the middle 50% of hospitals with readmission rates between 15.2% and 16.5%. All of the hospitals in this study reported that hospitalists provide care at the hospitals. The median Medicare share of hospital admissions was 48.46%, and the median Medicaid share of hospital admissions was 16.4%. Fifty‐one percent of the hospitals in our sample were fully integrated. Fifty percent of hospitals had 9 or fewer hospitalists per 100 general medical and surgical beds. Only 5% of the hospitals had partial or full physician ownership. Twenty‐nine percent of hospitals had an established medical home program. Table 1 provides summary statistics and the data sources of all the variables included in the study.

To compare readmission rates, we created a dummy variable that divided the sample into 2 categories: hospitals with low hospitalist staffing levels (hospitalists per general medical and surgical beds is less than the median) and high hospitalist staffing (hospitalists per general medical and surgical bed ratio is more than the median). We then used t tests to compare all‐cause readmission rates between hospitals with low and high hospitalist staffing levels, physician owned versus nonphysician owned, and fully integrated versus not fully integrated. We also used single‐factor analysis of variance (ANOVA) to compare readmission rates between nonteaching, minor teaching, and major teaching hospitals. Results are displayed in Table 2. There was a significant difference in the mean readmission rates between hospitals with low hospitalist staffing levels (mean readmission rate = 16.06%) versus high staffing levels (mean readmission rate = 15.72%). The mean readmission rate for physician‐owned hospitals was significantly lower than for nonphysician‐owned hospitals (15.46% vs 15.9%). Also, fully integrated hospitals had a lower readmission rate than hospitals where physicians were not fully integrated (15.93% vs 15.86%). Based on the ANOVA results, there was a significant difference between teaching levels. According to a Tukey honest significant difference post hoc test, there was no significant difference between nonteaching and minor teaching hospitals, but the readmission rate was significantly higher in major teaching hospitals (nonteaching = 15.83%, minor teaching = 15.76%, major teaching = 16.9%).

Comparisons Between Readmission Rates: t Tests and Analysis of Variance
Variable Readmission Rates P Value
Hospitalist staffing levels
Low 16.06% 0.00
High 15.72%
Physician ownership
Fully or partially physician‐owned hospitals 15.46% 0.00
Nonphysician‐owned hospitals 15.9 %
Physician integration
Fully integrated hospitals 15.86% 0.00
Nonintegrated hospitals 15.93%
Teaching status
Nonteaching hospitals 15.83% 0.00
Minor teaching hospitals 15.76%
Major teaching hospitals 16.9%

The OLS regression model was significant and explained 16% of the variability in readmission rates (Table 3). Higher hospitalists staffing levels were associated with lower 30‐day all cause readmission rates (P = 0.00). The addition of 1 hospitalist per general and surgical bed was associated with a 0.77 percentage points decrease in adjusted readmission rates. In terms of hospital‐physician arrangements, fully integrated hospitals had adjusted 30‐day all‐cause readmission rates 0.09 percentage points lower than nonfully integrated hospitals (P = 0.08). Physician partial or full ownership was significantly associated with lower readmission rates (P = 0.00); hospitals partially or fully owned by physicians had adjusted readmission rates 0.36 percentage points lower than nonphysician‐owned hospitals.

Regression Results: Organizational and Environmental Predictors of Hospital Readmissions
Variable Coefficient Standard Error P Value

  • NOTE: Adjusted R2 = 16, F = 20.62, P = 0.00. Abbreviations: RNs, registered nurses.

Hospitalists per general and surgical beds 0.77 0.172 0.00
Full integration 0.086 0.049 0.08
Physician ownership 0.355 0.119 0.00
RNs per 100 inpatient days 0.174 0.050 0.00
Established medical home program 0.132 0.057 0.02
Medicare admissions 0.063 0.002 0.21
Medicaid admissions 0.015 0.003 0.00
Competition 0.115 0.08 0.17
Unemployment 0.244 0.037 0.00
System membership 0.041 0.055 0.45
Teaching level
Minor teaching 0.007 0.066 0.92
Major teaching 1.032 0.106 0.00
Size
Medium 0.032 0.071 0.66
Large 0.066 0.085 0.44
For‐profit ownership 0.206 0.078 0.01
High technology 0.077 0.055 0.17
Critical access hospital 0.202 0.092 0.03

Based on the regression analysis, major teaching hospitals on average had adjusted readmission rates 1.03 percentage point higher than nonteaching hospitals (P = 0.000), whereas there was no significant difference between minor and nonteaching hospitals (P > 0.1). As the number of registered nurses (RNs) per 100 inpatient days increased by 1, readmission rates dropped by 0.17 (P = 0.00). Hospitals with higher Medicaid shares of admission had significantly higher readmission rates (P < 0.05). Hospitals located in counties with higher unemployment rates also had higher readmission rates (P = 0.000), whereas market competition had no significant association with readmissions. For‐profit hospitals had adjusted readmission rates 0.21 percentage points higher than not‐for‐profit hospitals (P = 0.01). Finally, hospitals that have adopted a medical home model had significantly lower readmission rates (P = 0.02); hospitals with an established medical home model had adjusted readmission rates 0.17 percentage points lower than their counterparts.

DISCUSSION

In the era of VBP and mounting pressures on hospitals to improve quality and lower cost, it is important to understand the association between modifiable hospital characteristics, such as hospitalist staffing levels, and unmodifiable characteristics, such as teaching status and size, with quality of care. There are many factors that can contribute to higher readmission rates. Some of these factors are hospital related and others are patient related, such as the environment in which a patient resides. Benbassat and Taragin argue that 9% to 48% of hospital readmissions are avoidable and are related to factors such as inadequate resolution of the problem the patient was admitted for and poor discharge care.[22] In this article, we have focused on hospital and market factors. Our main variables of interest were hospitalist staffing level, physician full integration, physician ownership, and the adoption of the medical home model at the hospital. Moreover, we examined the association between the hospital environment, specifically, market competition, and the patient environment, specifically, unemployment rates, with readmission rates.

Hospitalists' provision of inpatient care has been on the rise. From 1997 to 2006, the likelihood of receiving inpatient care from a hospitalist grew by 29.2% per year.[23] Based on AHA (2013) data, 65% of hospitals reported that hospitalists provided care at the hospital. The main driver behind the adoption of the hospitalists' model is the positive role hospitalists play in improving hospital efficiency and their familiarity and specialization in hospital care.[24] However, concerns exist that hospitalists might negatively influence patient outcomes given the discontinuity of care that occurs once the patient is discharged from the hospital and back to the care of their primary care physician.[25] Based on our analysis though, higher hospitalist staffing levels were associated with lower readmission rates. Therefore, to better understand the relationship between hospitalists and quality, it is important to account for staffing levels, not merely whether hospitalists provide care at the hospital or not. Higher patient load per hospitalist might still improve hospital efficiency by lowering costs, but is it likely to impede the quality of care provided by hospitalists. This is not surprising given similar findings, including in this article, which document a similar positive relationship between nursing staffing levels and quality.

Hospitals utilize various arrangements with physicians that range from employment to more relaxed arrangements such as physicians with privileges who are neither employed by the hospital nor under individual or group contracts. Historically, the main incentive for hospitals to integrate physicians was referrals to hospital services and specialties.[16, 26] The Affordable Care Act, however, provided further incentives, such as ease of care coordination, physicians' involvement, and commitment to quality improvement and cost‐containment efforts. Based on this study, hospitals that were classified as fully integrated had lower readmission rates. Also, hospitals partially or fully owned by physicians had better readmission rates. These findings indicate that hospital‐physician arrangements play a significant role not only in influencing efficiency and market share but also patient outcomes. Physician integration and physician ownership align physicians' financial incentives with those of the hospital. For instance, given the recent changes in reimbursement and the shift toward VBP, physician income in physician‐owned hospitals is at risk if the hospital has poor patient outcomes.

Other significant predictors of readmission rates included the adoption of the medical home model and RN staffing levels. Hospitals that adopted a medical home model and had a higher registered nurse‐to‐inpatient days ratio had significantly better readmission rates. The finding on the adoption of the medical home model is especially important. Previous research indicates that patient‐centered medical homes are associated with lower emergency room visits but not necessarily lower admissions.[27] Our findings indicate that medical homes might play a role in lowering readmission rates, and therefore this outcome needs to be included in studies examining the performance of medical homes. Critical access hospitals and those with higher admissions share of Medicaid patients had worst readmission rates. Finally, hospitals located in counties with higher unemployment rates also had the worst readmission rates. This finding is not surprising and is consistent with previous research, which indicates that the patients' environment and social risk factors play a significant role.

This article contributes to our understanding of readmission rates despite its several limitations, which include the measurement of hospitalist staffing levels based on general medical and surgical beds rather than general medicine admissions. Moreover, some hospitals had missing data on key variables, which warranted their exclusion from this study. In conclusion, many structural, operational and market‐level factors influence all‐cause readmission rates. However, some of these variables are modifiable and can thus be adjusted by a hospital to improve readmission rates. These variables include hospitalists and registered nurse staffing levels; physician integration through the salaried, equity, or foundation model; and the adoption of a medical home model.

Disclosure

Nothing to report.

Files
Supporting Information (1)
Supporting Information (2)
References
  1. Berenson RA, Paulus RA, Kalman NS. Medicare's readmissions‐reduction program—a positive alternative. N Engl J Med. 2012;366:1364–1366.
  2. Gohil SK, Datta R, Cao C, et al. Impact of hospital population case‐mix, including poverty, on hospital all‐cause and infection‐related 30‐day readmission rates. Clin Infect Dis. 2015;31(2):1235–1243.
  3. Krumholz HM, Merrill AR, Schone EM, et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2:407–413.
  4. Singh S, Lin Y‐L, Kuo Y‐F, Nattinger AB, Goodwin JS. Variation in the risk of readmission among hospitals: the relative contribution of patient, hospital and inpatient provider characteristics. J Gen Intern Med. 2014;29:572–578.
  5. Joynt KE, Jha AK. A path forward on Medicare readmissions. N Engl J Med. 2013;368:1175–1177.
  6. McHugh MD, Berez J, Small DS. Hospitals with higher nurse staffing had lower odds of readmissions penalties than hospitals with lower staffing. Health Aff (Millwood). 2013;32:1740–1747.
  7. Tsai TC, Joynt KE, Orav EJ, Gawande AA, Jha AK. Variation in surgical‐readmission rates and quality of hospital care. N Engl J Med. 2013;369:1134–1142.
  8. Gilfillan RJ, Tomcavage J, Rosenthal MB, et al. Value and the medical home: effects of transformed primary care. Am J Manag Care. 2010;16.8:607–614.
  9. Davis KM, Koch KE, Harvey JK, Wilson R, Englert J, Gerard PD. Effects of hospitalists on cost, outcomes, and patient satisfaction in a rural health system. Am J Med. 2000;108:621–626.
  10. Elliott DJ, Young RS, Brice J, Aguiar R, Kolm P. Effect of hospitalist workload on the quality and efficiency of care. JAMA Intern Med. 2014;174:786–793.
  11. Jungerwirth R, Wheeler SB, Paul JE. Association of hospitalist presence and hospital‐level outcome measures among Medicare patients. J Hosp Med. 2014;9:1–6.
  12. Goodrich K, Krumholz HM, Conway PH, Lindenauer P, Auerbach AD. Hospitalist utilization and hospital performance on 6 publicly reported patient outcomes. J Hosp Med. 2012;7:482–488.
  13. Michtalik HJ, Yeh H, Pronovost PJ, Brotman DJ. Impact of attending physician workload on patient care: a survey of hospitalists. JAMA Intern Med. 2013;173:375–377.
  14. O'Malley AS, Bond AM, Berenson RA. Rising hospital employment of physicians: better quality, higher costs. Issue Brief Cent Stud Health Syst Change. 2011;136:1–4.
  15. Charles AG, Ortiz‐Pujols S, Ricketts T, et al. The employed surgeon: a changing professional paradigm. JAMA Surg. 2013;148:323–328.
  16. Baker LC, Bundorf MK, Kessler DP. Vertical integration: hospital ownership of physician practices is associated with higher prices and spending. Health Aff (Millwood). 2014;33:756–763.
  17. Horwitz L, Partovian C, Lin Z, et al. Hospital‐wide (all‐condition) 30‐day risk‐standardized readmission measure: Yale New Haven Health Services Corporation. Center for Outcomes Research 161(10 suppl):S66–S75.
  18. Burns L, Muller R. Hospital‐physician collaboration: landscape of economic integration and impact on clinical integration. Milbank Q. 2008;86(3):375–434.
  19. Aiken LH, Clarke SP, Sloane DM, Lake ET, Cheney T. Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2008;38:223–229.
  20. Aiken LH, Cimiotti JP, Sloane DM, Smith HL, Flynn L, Neff DF. The effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Med Care. 2011;49(12):1047–1053.
  21. Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Arch Intern Med. 2000;160:1074–1081.
  22. Kuo Y‐F, Sharma G, Freeman JL, Goodwin JS. Growth in the care of older patients by hospitalists in the United States. N Engl J Med. 2009;360:1102–1112.
  23. Wachter RM. Reflections: the hospitalist movement a decade later. J Hosp Med. 2006;1:248–252.
  24. Goodwin JS, Lin Y‐L, Singh S, Kuo Y‐F. Variation in length of stay and outcomes among hospitalized patients attributable to hospitals and hospitalists. J Gen Intern Med. 2013;28:370–376.
  25. Bacher GE, Chernew ME, Kessler DP, Weiner SM. Regulatory neutrality is essential to establishing a level playing field for accountable care organizations. Health Aff (Millwood). 2013;32:1426–1432.
  26. Rittenhouse DR, Shortell SM. The patient‐centered medical home: will it stand the test of health reform? JAMA. 2009;301:2038–2040.
  27. Joynt KE, Jha AK. Who has higher readmission rates for heart failure, and why?: implications for efforts to improve care using financial incentives. Circ Cardiovasc Qual Outcomes. 2011;4:53–59.
Article PDF
jhm2606.pdf
Issue
Journal of Hospital Medicine - 11(10)
Page Number
682-687
Sections
Original Research
Author(s)
Mona Al‐Amin, PhD, MPH
Author(s)
Mona Al‐Amin, PhD, MPH
Files
Supporting Information (1)
Supporting Information (2)
Files
Supporting Information (1)
Supporting Information (2)
Article PDF
jhm2606.pdf
Article PDF
jhm2606.pdf

The hospital‐wide all‐cause 30‐day readmission rate is a key quality measure associated with patient outcomes, cost of care, and wasted hospital resources.[1] The estimated 20% readmission rate of Medicare patients and the associated $17 billion annual cost of readmissions led the Centers for Medicare and Medicaid Services (CMS) to implement policies that limit reimbursement for 30‐day unplanned readmissions and thus place hospitals with high readmission rates at financial risk.[1, 2]

The variation in readmission rates between hospitals is well documented in the literature.[3, 4] Singh et al. found that 9.3% of the variation in readmissions can be explained by hospital characteristics.[4] Hospital factors associated with lower readmission rates include not‐for‐profit ownership, hospital size, and nursing staffing levels.[5, 6, 7] Other studies found an association between environmental factors such as the percent of patients living under the poverty line and higher readmission rates.[7] The recent publicly available CMS data on readmission rates allows us to further our understanding of hospital characteristics that explain the variation in readmission rates. In this article, we are specifically interested in hospitalist staffing levels and hospital‐physician arrangements such as physician integration level and physician ownership. Moreover, we are interested in novel organizational variables, specifically, the adoption of a medical home model, which has been ignored by previous research. Medical homes are associated with better quality[8]; hospitals that already adopted the medical home model might be better equipped to coordinate care after the patients are discharged.

In recent years, the number of hospitals relying on hospitalists to provide inpatient care has been on the rise. As more hospitals employ hospitalists, it is important to understand how hospitalist staffing levels are associated with quality. Previous studies have linked hospitalists with lower hospital mortality rates,[7] lower cost of care,[9, 10] and lower readmission rates.[10, 11] Goodrich et al., on the other hand, did not find a significant relationship between the presence of hospitalists and mortality or readmission rates.[12] In a recent study, hospitalists indicated that heavy workloads limited the time they had available to communicate with patients, which negatively influenced quality and patient satisfaction, and resulted in delayed admissions and discharges.[13]

The main objective of this article was therefore to study the association between hospitalist staffing levels and hospital‐wide all‐cause readmission rates. Most empirical studies examining the relationship between hospitalist staffing and quality of inpatient care have predominantly focused on whether the presence of hospitalists who provided care at a hospital influenced mortality or readmissions.[11, 12] In this article, we contribute to the literature by examining how staffing levels measured by the ratio of hospitalists to general medical and surgical beds is associated with 30‐day readmission rates. We predict that there is a positive association between readmission rates and hospitalists per bed.

Hospitals have a broad range of contractual arrangements or integration levels with physicians, with employment being the highest level. A hospital can rely on physicians who have admitting privileges but are not salaried employees of the hospital to treat a large portion of its inpatient population. In the past few years, with the passage of the Patient Protection and Affordable Care Act (2010) and the shift in reimbursement towards Value Based Purchasing (VBP), more hospitals are choosing to ensure that physicians are strongly integrated within the hospital by adopting an employment‐based model. Moreover, hospitals view physician employment as a strategic move that will help ensure or expand their market share.[14] For instance, the number of surgeons who identified as self‐employed dropped from 48% in 2001 to 28% in 2011, and this reduction is attributed to the shift toward hospital employment of physicians.[15] Despite the evolving models of hospital‐physician arrangements, little is understood on how the adoption of the integrated salary model, in addition to the equity and foundation models, which are classified by Baker et al. as the highest level of integration, influence quality.[16] Therefore, another objective of this article was to examine the association between hospital‐physician arrangements and all‐cause unplanned readmission rates.

METHODS

Data Source and Sample

Data from the American Hospital Association (AHA) Annual Survey (2013), CMS Hospital Compare (October 2013), and Area Health Resource File (2013) were merged to analyze the association between readmission rates with hospital characteristics and environmental factors. We limited the analysis to private (nonpublic) hospitals with no missing data. Our final sample consisted of 1756 hospitals. Of the hospitals in our sample, 14% were for profit, 70% were nonteaching, 23% were minor teaching, 7% were major teaching hospitals, 73% belonged to a system, and 31% were classified as small hospitals. Table 1 provides descriptive statistics for all the variables included in the analysis.

Summary Statistics
Variable Value Data Source

  • NOTE: Abbreviations: IQR, interquartile range; RNs, registered nurses.

30‐day all‐cause readmissions, median (IQR) 15.8% (15.2%16.5%) Centers for Medicare and Medicaid Services
Hospitalists per general medicine and surgical beds, median (IQR) 0.09 (0.060.15) American Hospital Association
RNs per 100 inpatient days, median (IQR) 0.84 (0.6610.10) American Hospital Association
Medicare admissions, median (IQR) 48.45% (40.84%55.14%) American Hospital Association
Medicaid admissions, median (IQR) 16.45% (11.06%22.76%) American Hospital Association
Competition, median (IQR) 0.56 (0.230.83) American Hospital Association
Unemployment, median (IQR) 2.9% (2.54%3.37%) Area Resource File
Fully integrated American Hospital Association
Yes 51%
No 49%
Physician ownership American Hospital Association
Physician partial or complete ownership 5%
No physician ownership 95%
Established medical home program American Hospital Association
Yes 29%
No 71%
High technology American Hospital Association
Yes 40%
No 60%
Teaching level American Hospital Association
Nonteaching 70%
Minor teaching 23%
Major teaching 7%
Size American Hospital Association
Small 31%
Medium 34%
Large 35%
Ownership American Hospital Association
For profit 14%
Not for profit 86%
Critical access hospital American Hospital Association
Yes 11%
No 89%
System membership American Hospital Association
Yes 73%
No 27%

Variables

Dependent Variable

Risk standardized 30‐day hospital‐wide all‐cause readmission rates (HWR) were obtained from CMS. This measure was publicly reported in October 2013. The HWR is estimated using standardized risk ratios at the hospital level for the following 5 discharge diagnosis groups: surgery/gynecology, neurology, cardiorespiratory, cardiovascular, and general medicine.[17] The measure adjusts, in addition to a hospital's case mix, for patients' ages, principal discharge diagnoses, and comorbidities.[17] HWR is calculated as a predicted‐to‐expected readmissions ratio. Predicted and expected readmissions were calculated for each of the 5 groups for each hospital using each hospital's patient mix and a hospital random effects estimate. A standardized readmission ratio was then derived by dividing predicted readmissions by expected readmissions for each group for each hospital. A single hospital score was obtained by multiplying the volume‐weighted logarithmic average of the 5 diagnostic groups by the average national readmission rate.[18]

Independent Variables

The primary independent variable of interest to this study is hospitalist staffing levels. We calculate the staffing levels of hospitalists by dividing the full‐time equivalent (FTE) of hospitalists by the number of general medical and surgical beds. FTE hospitalists are calculated by the AHA Annual Survey database (2013) as the sum of full‐time hospitalists and 0.5*number of part‐time hospitalists. In addition to hospitalist staffing levels, a main predictors is whether the hospital fully integrates physicians or not. We follow Baker et al. in our classification of full integration. Baker et al. define fully integrated hospitals as those that adopted 1 of the following models with their physicians: integrated salary, foundation or equity model.[16] We predict that fully integrated hospitals are more likely to have better readmission rates. Another key physician variable that is likely to influence outcomes is physician partial or full ownership of the hospital. Ownership aligns physicians' incentives with hospital performance[19] and is therefore likely to be associated with better readmission rates. We also include a dichotomous variable that indicates whether a hospital has an established medical home program or not. Medical homes indicate an organizational culture that is patient centered and committed to continuity and coordination of care; all of which are important for better quality. We predict that the presence of a medical home model will be associated with better readmission rates.

Control Variables

We control for registered nurses per 100 inpatient days ratio, critical access designation, Medicare share of hospital admissions, Medicaid share of hospital admissions, teaching status, size, and technology level. Previous research indicates that these variables are associated with patient outcomes.[20, 21] We follow the Aiken et al. characterization of teaching status: hospitals with no residency programs (nonteaching), hospitals with a resident‐to‐bed ratio of 1 to 4 or less (minor teaching), and hospitals with a resident‐to‐bed ratio of more than 1 to 4 (major teaching).[20] We also classify hospitals as small if they have less than 100 beds, medium if they have 101 to 250 beds, and large if they have more than 250 beds. We modify the Aiken et al. classification of technology level and control for the level of technology adopted at a hospital by classifying hospitals as high technology if they offer any of the following services: any major organ transplant, computer‐assisted orthopedic surgery, or electron beam computed tomography.[21] We also control for 2 market level variables: (1) competition estimated by the county level Herfindahl‐Hirschman Index (HHI) and (2) the percentage of individuals in the county who are unemployed. Unemployment rates are derived from the Area Health Resource File (2013). HHI is calculated by summing the squares of market shares of admissions. For ease of interpretation, competition is coded as 1‐HHI.

Statistical Analysis

We ran a multivariate ordinary least squares (OLS) regression on Stata 12 (StataCorp, College Station, TX) to assess the relationship between 30‐day all‐cause readmissions and hospitalist staffing levels, physician integration, physician ownership, and other organizational characteristics. We checked for multicollinearity by using a variance inflation factor (VIF). The VIF of all independent variables was less than 10, and therefore multicollinearity was not of concern to this analysis.

RESULTS

Among our sample of 1756 hospitals, the median 30‐day all‐cause readmission rate was 16%, with the middle 50% of hospitals with readmission rates between 15.2% and 16.5%. All of the hospitals in this study reported that hospitalists provide care at the hospitals. The median Medicare share of hospital admissions was 48.46%, and the median Medicaid share of hospital admissions was 16.4%. Fifty‐one percent of the hospitals in our sample were fully integrated. Fifty percent of hospitals had 9 or fewer hospitalists per 100 general medical and surgical beds. Only 5% of the hospitals had partial or full physician ownership. Twenty‐nine percent of hospitals had an established medical home program. Table 1 provides summary statistics and the data sources of all the variables included in the study.

To compare readmission rates, we created a dummy variable that divided the sample into 2 categories: hospitals with low hospitalist staffing levels (hospitalists per general medical and surgical beds is less than the median) and high hospitalist staffing (hospitalists per general medical and surgical bed ratio is more than the median). We then used t tests to compare all‐cause readmission rates between hospitals with low and high hospitalist staffing levels, physician owned versus nonphysician owned, and fully integrated versus not fully integrated. We also used single‐factor analysis of variance (ANOVA) to compare readmission rates between nonteaching, minor teaching, and major teaching hospitals. Results are displayed in Table 2. There was a significant difference in the mean readmission rates between hospitals with low hospitalist staffing levels (mean readmission rate = 16.06%) versus high staffing levels (mean readmission rate = 15.72%). The mean readmission rate for physician‐owned hospitals was significantly lower than for nonphysician‐owned hospitals (15.46% vs 15.9%). Also, fully integrated hospitals had a lower readmission rate than hospitals where physicians were not fully integrated (15.93% vs 15.86%). Based on the ANOVA results, there was a significant difference between teaching levels. According to a Tukey honest significant difference post hoc test, there was no significant difference between nonteaching and minor teaching hospitals, but the readmission rate was significantly higher in major teaching hospitals (nonteaching = 15.83%, minor teaching = 15.76%, major teaching = 16.9%).

Comparisons Between Readmission Rates: t Tests and Analysis of Variance
Variable Readmission Rates P Value
Hospitalist staffing levels
Low 16.06% 0.00
High 15.72%
Physician ownership
Fully or partially physician‐owned hospitals 15.46% 0.00
Nonphysician‐owned hospitals 15.9 %
Physician integration
Fully integrated hospitals 15.86% 0.00
Nonintegrated hospitals 15.93%
Teaching status
Nonteaching hospitals 15.83% 0.00
Minor teaching hospitals 15.76%
Major teaching hospitals 16.9%

The OLS regression model was significant and explained 16% of the variability in readmission rates (Table 3). Higher hospitalists staffing levels were associated with lower 30‐day all cause readmission rates (P = 0.00). The addition of 1 hospitalist per general and surgical bed was associated with a 0.77 percentage points decrease in adjusted readmission rates. In terms of hospital‐physician arrangements, fully integrated hospitals had adjusted 30‐day all‐cause readmission rates 0.09 percentage points lower than nonfully integrated hospitals (P = 0.08). Physician partial or full ownership was significantly associated with lower readmission rates (P = 0.00); hospitals partially or fully owned by physicians had adjusted readmission rates 0.36 percentage points lower than nonphysician‐owned hospitals.

Regression Results: Organizational and Environmental Predictors of Hospital Readmissions
Variable Coefficient Standard Error P Value

  • NOTE: Adjusted R2 = 16, F = 20.62, P = 0.00. Abbreviations: RNs, registered nurses.

Hospitalists per general and surgical beds 0.77 0.172 0.00
Full integration 0.086 0.049 0.08
Physician ownership 0.355 0.119 0.00
RNs per 100 inpatient days 0.174 0.050 0.00
Established medical home program 0.132 0.057 0.02
Medicare admissions 0.063 0.002 0.21
Medicaid admissions 0.015 0.003 0.00
Competition 0.115 0.08 0.17
Unemployment 0.244 0.037 0.00
System membership 0.041 0.055 0.45
Teaching level
Minor teaching 0.007 0.066 0.92
Major teaching 1.032 0.106 0.00
Size
Medium 0.032 0.071 0.66
Large 0.066 0.085 0.44
For‐profit ownership 0.206 0.078 0.01
High technology 0.077 0.055 0.17
Critical access hospital 0.202 0.092 0.03

Based on the regression analysis, major teaching hospitals on average had adjusted readmission rates 1.03 percentage point higher than nonteaching hospitals (P = 0.000), whereas there was no significant difference between minor and nonteaching hospitals (P > 0.1). As the number of registered nurses (RNs) per 100 inpatient days increased by 1, readmission rates dropped by 0.17 (P = 0.00). Hospitals with higher Medicaid shares of admission had significantly higher readmission rates (P < 0.05). Hospitals located in counties with higher unemployment rates also had higher readmission rates (P = 0.000), whereas market competition had no significant association with readmissions. For‐profit hospitals had adjusted readmission rates 0.21 percentage points higher than not‐for‐profit hospitals (P = 0.01). Finally, hospitals that have adopted a medical home model had significantly lower readmission rates (P = 0.02); hospitals with an established medical home model had adjusted readmission rates 0.17 percentage points lower than their counterparts.

DISCUSSION

In the era of VBP and mounting pressures on hospitals to improve quality and lower cost, it is important to understand the association between modifiable hospital characteristics, such as hospitalist staffing levels, and unmodifiable characteristics, such as teaching status and size, with quality of care. There are many factors that can contribute to higher readmission rates. Some of these factors are hospital related and others are patient related, such as the environment in which a patient resides. Benbassat and Taragin argue that 9% to 48% of hospital readmissions are avoidable and are related to factors such as inadequate resolution of the problem the patient was admitted for and poor discharge care.[22] In this article, we have focused on hospital and market factors. Our main variables of interest were hospitalist staffing level, physician full integration, physician ownership, and the adoption of the medical home model at the hospital. Moreover, we examined the association between the hospital environment, specifically, market competition, and the patient environment, specifically, unemployment rates, with readmission rates.

Hospitalists' provision of inpatient care has been on the rise. From 1997 to 2006, the likelihood of receiving inpatient care from a hospitalist grew by 29.2% per year.[23] Based on AHA (2013) data, 65% of hospitals reported that hospitalists provided care at the hospital. The main driver behind the adoption of the hospitalists' model is the positive role hospitalists play in improving hospital efficiency and their familiarity and specialization in hospital care.[24] However, concerns exist that hospitalists might negatively influence patient outcomes given the discontinuity of care that occurs once the patient is discharged from the hospital and back to the care of their primary care physician.[25] Based on our analysis though, higher hospitalist staffing levels were associated with lower readmission rates. Therefore, to better understand the relationship between hospitalists and quality, it is important to account for staffing levels, not merely whether hospitalists provide care at the hospital or not. Higher patient load per hospitalist might still improve hospital efficiency by lowering costs, but is it likely to impede the quality of care provided by hospitalists. This is not surprising given similar findings, including in this article, which document a similar positive relationship between nursing staffing levels and quality.

Hospitals utilize various arrangements with physicians that range from employment to more relaxed arrangements such as physicians with privileges who are neither employed by the hospital nor under individual or group contracts. Historically, the main incentive for hospitals to integrate physicians was referrals to hospital services and specialties.[16, 26] The Affordable Care Act, however, provided further incentives, such as ease of care coordination, physicians' involvement, and commitment to quality improvement and cost‐containment efforts. Based on this study, hospitals that were classified as fully integrated had lower readmission rates. Also, hospitals partially or fully owned by physicians had better readmission rates. These findings indicate that hospital‐physician arrangements play a significant role not only in influencing efficiency and market share but also patient outcomes. Physician integration and physician ownership align physicians' financial incentives with those of the hospital. For instance, given the recent changes in reimbursement and the shift toward VBP, physician income in physician‐owned hospitals is at risk if the hospital has poor patient outcomes.

Other significant predictors of readmission rates included the adoption of the medical home model and RN staffing levels. Hospitals that adopted a medical home model and had a higher registered nurse‐to‐inpatient days ratio had significantly better readmission rates. The finding on the adoption of the medical home model is especially important. Previous research indicates that patient‐centered medical homes are associated with lower emergency room visits but not necessarily lower admissions.[27] Our findings indicate that medical homes might play a role in lowering readmission rates, and therefore this outcome needs to be included in studies examining the performance of medical homes. Critical access hospitals and those with higher admissions share of Medicaid patients had worst readmission rates. Finally, hospitals located in counties with higher unemployment rates also had the worst readmission rates. This finding is not surprising and is consistent with previous research, which indicates that the patients' environment and social risk factors play a significant role.

This article contributes to our understanding of readmission rates despite its several limitations, which include the measurement of hospitalist staffing levels based on general medical and surgical beds rather than general medicine admissions. Moreover, some hospitals had missing data on key variables, which warranted their exclusion from this study. In conclusion, many structural, operational and market‐level factors influence all‐cause readmission rates. However, some of these variables are modifiable and can thus be adjusted by a hospital to improve readmission rates. These variables include hospitalists and registered nurse staffing levels; physician integration through the salaried, equity, or foundation model; and the adoption of a medical home model.

Disclosure

Nothing to report.

The hospital‐wide all‐cause 30‐day readmission rate is a key quality measure associated with patient outcomes, cost of care, and wasted hospital resources.[1] The estimated 20% readmission rate of Medicare patients and the associated $17 billion annual cost of readmissions led the Centers for Medicare and Medicaid Services (CMS) to implement policies that limit reimbursement for 30‐day unplanned readmissions and thus place hospitals with high readmission rates at financial risk.[1, 2]

The variation in readmission rates between hospitals is well documented in the literature.[3, 4] Singh et al. found that 9.3% of the variation in readmissions can be explained by hospital characteristics.[4] Hospital factors associated with lower readmission rates include not‐for‐profit ownership, hospital size, and nursing staffing levels.[5, 6, 7] Other studies found an association between environmental factors such as the percent of patients living under the poverty line and higher readmission rates.[7] The recent publicly available CMS data on readmission rates allows us to further our understanding of hospital characteristics that explain the variation in readmission rates. In this article, we are specifically interested in hospitalist staffing levels and hospital‐physician arrangements such as physician integration level and physician ownership. Moreover, we are interested in novel organizational variables, specifically, the adoption of a medical home model, which has been ignored by previous research. Medical homes are associated with better quality[8]; hospitals that already adopted the medical home model might be better equipped to coordinate care after the patients are discharged.

In recent years, the number of hospitals relying on hospitalists to provide inpatient care has been on the rise. As more hospitals employ hospitalists, it is important to understand how hospitalist staffing levels are associated with quality. Previous studies have linked hospitalists with lower hospital mortality rates,[7] lower cost of care,[9, 10] and lower readmission rates.[10, 11] Goodrich et al., on the other hand, did not find a significant relationship between the presence of hospitalists and mortality or readmission rates.[12] In a recent study, hospitalists indicated that heavy workloads limited the time they had available to communicate with patients, which negatively influenced quality and patient satisfaction, and resulted in delayed admissions and discharges.[13]

The main objective of this article was therefore to study the association between hospitalist staffing levels and hospital‐wide all‐cause readmission rates. Most empirical studies examining the relationship between hospitalist staffing and quality of inpatient care have predominantly focused on whether the presence of hospitalists who provided care at a hospital influenced mortality or readmissions.[11, 12] In this article, we contribute to the literature by examining how staffing levels measured by the ratio of hospitalists to general medical and surgical beds is associated with 30‐day readmission rates. We predict that there is a positive association between readmission rates and hospitalists per bed.

Hospitals have a broad range of contractual arrangements or integration levels with physicians, with employment being the highest level. A hospital can rely on physicians who have admitting privileges but are not salaried employees of the hospital to treat a large portion of its inpatient population. In the past few years, with the passage of the Patient Protection and Affordable Care Act (2010) and the shift in reimbursement towards Value Based Purchasing (VBP), more hospitals are choosing to ensure that physicians are strongly integrated within the hospital by adopting an employment‐based model. Moreover, hospitals view physician employment as a strategic move that will help ensure or expand their market share.[14] For instance, the number of surgeons who identified as self‐employed dropped from 48% in 2001 to 28% in 2011, and this reduction is attributed to the shift toward hospital employment of physicians.[15] Despite the evolving models of hospital‐physician arrangements, little is understood on how the adoption of the integrated salary model, in addition to the equity and foundation models, which are classified by Baker et al. as the highest level of integration, influence quality.[16] Therefore, another objective of this article was to examine the association between hospital‐physician arrangements and all‐cause unplanned readmission rates.

METHODS

Data Source and Sample

Data from the American Hospital Association (AHA) Annual Survey (2013), CMS Hospital Compare (October 2013), and Area Health Resource File (2013) were merged to analyze the association between readmission rates with hospital characteristics and environmental factors. We limited the analysis to private (nonpublic) hospitals with no missing data. Our final sample consisted of 1756 hospitals. Of the hospitals in our sample, 14% were for profit, 70% were nonteaching, 23% were minor teaching, 7% were major teaching hospitals, 73% belonged to a system, and 31% were classified as small hospitals. Table 1 provides descriptive statistics for all the variables included in the analysis.

Summary Statistics
Variable Value Data Source

  • NOTE: Abbreviations: IQR, interquartile range; RNs, registered nurses.

30‐day all‐cause readmissions, median (IQR) 15.8% (15.2%16.5%) Centers for Medicare and Medicaid Services
Hospitalists per general medicine and surgical beds, median (IQR) 0.09 (0.060.15) American Hospital Association
RNs per 100 inpatient days, median (IQR) 0.84 (0.6610.10) American Hospital Association
Medicare admissions, median (IQR) 48.45% (40.84%55.14%) American Hospital Association
Medicaid admissions, median (IQR) 16.45% (11.06%22.76%) American Hospital Association
Competition, median (IQR) 0.56 (0.230.83) American Hospital Association
Unemployment, median (IQR) 2.9% (2.54%3.37%) Area Resource File
Fully integrated American Hospital Association
Yes 51%
No 49%
Physician ownership American Hospital Association
Physician partial or complete ownership 5%
No physician ownership 95%
Established medical home program American Hospital Association
Yes 29%
No 71%
High technology American Hospital Association
Yes 40%
No 60%
Teaching level American Hospital Association
Nonteaching 70%
Minor teaching 23%
Major teaching 7%
Size American Hospital Association
Small 31%
Medium 34%
Large 35%
Ownership American Hospital Association
For profit 14%
Not for profit 86%
Critical access hospital American Hospital Association
Yes 11%
No 89%
System membership American Hospital Association
Yes 73%
No 27%

Variables

Dependent Variable

Risk standardized 30‐day hospital‐wide all‐cause readmission rates (HWR) were obtained from CMS. This measure was publicly reported in October 2013. The HWR is estimated using standardized risk ratios at the hospital level for the following 5 discharge diagnosis groups: surgery/gynecology, neurology, cardiorespiratory, cardiovascular, and general medicine.[17] The measure adjusts, in addition to a hospital's case mix, for patients' ages, principal discharge diagnoses, and comorbidities.[17] HWR is calculated as a predicted‐to‐expected readmissions ratio. Predicted and expected readmissions were calculated for each of the 5 groups for each hospital using each hospital's patient mix and a hospital random effects estimate. A standardized readmission ratio was then derived by dividing predicted readmissions by expected readmissions for each group for each hospital. A single hospital score was obtained by multiplying the volume‐weighted logarithmic average of the 5 diagnostic groups by the average national readmission rate.[18]

Independent Variables

The primary independent variable of interest to this study is hospitalist staffing levels. We calculate the staffing levels of hospitalists by dividing the full‐time equivalent (FTE) of hospitalists by the number of general medical and surgical beds. FTE hospitalists are calculated by the AHA Annual Survey database (2013) as the sum of full‐time hospitalists and 0.5*number of part‐time hospitalists. In addition to hospitalist staffing levels, a main predictors is whether the hospital fully integrates physicians or not. We follow Baker et al. in our classification of full integration. Baker et al. define fully integrated hospitals as those that adopted 1 of the following models with their physicians: integrated salary, foundation or equity model.[16] We predict that fully integrated hospitals are more likely to have better readmission rates. Another key physician variable that is likely to influence outcomes is physician partial or full ownership of the hospital. Ownership aligns physicians' incentives with hospital performance[19] and is therefore likely to be associated with better readmission rates. We also include a dichotomous variable that indicates whether a hospital has an established medical home program or not. Medical homes indicate an organizational culture that is patient centered and committed to continuity and coordination of care; all of which are important for better quality. We predict that the presence of a medical home model will be associated with better readmission rates.

Control Variables

We control for registered nurses per 100 inpatient days ratio, critical access designation, Medicare share of hospital admissions, Medicaid share of hospital admissions, teaching status, size, and technology level. Previous research indicates that these variables are associated with patient outcomes.[20, 21] We follow the Aiken et al. characterization of teaching status: hospitals with no residency programs (nonteaching), hospitals with a resident‐to‐bed ratio of 1 to 4 or less (minor teaching), and hospitals with a resident‐to‐bed ratio of more than 1 to 4 (major teaching).[20] We also classify hospitals as small if they have less than 100 beds, medium if they have 101 to 250 beds, and large if they have more than 250 beds. We modify the Aiken et al. classification of technology level and control for the level of technology adopted at a hospital by classifying hospitals as high technology if they offer any of the following services: any major organ transplant, computer‐assisted orthopedic surgery, or electron beam computed tomography.[21] We also control for 2 market level variables: (1) competition estimated by the county level Herfindahl‐Hirschman Index (HHI) and (2) the percentage of individuals in the county who are unemployed. Unemployment rates are derived from the Area Health Resource File (2013). HHI is calculated by summing the squares of market shares of admissions. For ease of interpretation, competition is coded as 1‐HHI.

Statistical Analysis

We ran a multivariate ordinary least squares (OLS) regression on Stata 12 (StataCorp, College Station, TX) to assess the relationship between 30‐day all‐cause readmissions and hospitalist staffing levels, physician integration, physician ownership, and other organizational characteristics. We checked for multicollinearity by using a variance inflation factor (VIF). The VIF of all independent variables was less than 10, and therefore multicollinearity was not of concern to this analysis.

RESULTS

Among our sample of 1756 hospitals, the median 30‐day all‐cause readmission rate was 16%, with the middle 50% of hospitals with readmission rates between 15.2% and 16.5%. All of the hospitals in this study reported that hospitalists provide care at the hospitals. The median Medicare share of hospital admissions was 48.46%, and the median Medicaid share of hospital admissions was 16.4%. Fifty‐one percent of the hospitals in our sample were fully integrated. Fifty percent of hospitals had 9 or fewer hospitalists per 100 general medical and surgical beds. Only 5% of the hospitals had partial or full physician ownership. Twenty‐nine percent of hospitals had an established medical home program. Table 1 provides summary statistics and the data sources of all the variables included in the study.

To compare readmission rates, we created a dummy variable that divided the sample into 2 categories: hospitals with low hospitalist staffing levels (hospitalists per general medical and surgical beds is less than the median) and high hospitalist staffing (hospitalists per general medical and surgical bed ratio is more than the median). We then used t tests to compare all‐cause readmission rates between hospitals with low and high hospitalist staffing levels, physician owned versus nonphysician owned, and fully integrated versus not fully integrated. We also used single‐factor analysis of variance (ANOVA) to compare readmission rates between nonteaching, minor teaching, and major teaching hospitals. Results are displayed in Table 2. There was a significant difference in the mean readmission rates between hospitals with low hospitalist staffing levels (mean readmission rate = 16.06%) versus high staffing levels (mean readmission rate = 15.72%). The mean readmission rate for physician‐owned hospitals was significantly lower than for nonphysician‐owned hospitals (15.46% vs 15.9%). Also, fully integrated hospitals had a lower readmission rate than hospitals where physicians were not fully integrated (15.93% vs 15.86%). Based on the ANOVA results, there was a significant difference between teaching levels. According to a Tukey honest significant difference post hoc test, there was no significant difference between nonteaching and minor teaching hospitals, but the readmission rate was significantly higher in major teaching hospitals (nonteaching = 15.83%, minor teaching = 15.76%, major teaching = 16.9%).

Comparisons Between Readmission Rates: t Tests and Analysis of Variance
Variable Readmission Rates P Value
Hospitalist staffing levels
Low 16.06% 0.00
High 15.72%
Physician ownership
Fully or partially physician‐owned hospitals 15.46% 0.00
Nonphysician‐owned hospitals 15.9 %
Physician integration
Fully integrated hospitals 15.86% 0.00
Nonintegrated hospitals 15.93%
Teaching status
Nonteaching hospitals 15.83% 0.00
Minor teaching hospitals 15.76%
Major teaching hospitals 16.9%

The OLS regression model was significant and explained 16% of the variability in readmission rates (Table 3). Higher hospitalists staffing levels were associated with lower 30‐day all cause readmission rates (P = 0.00). The addition of 1 hospitalist per general and surgical bed was associated with a 0.77 percentage points decrease in adjusted readmission rates. In terms of hospital‐physician arrangements, fully integrated hospitals had adjusted 30‐day all‐cause readmission rates 0.09 percentage points lower than nonfully integrated hospitals (P = 0.08). Physician partial or full ownership was significantly associated with lower readmission rates (P = 0.00); hospitals partially or fully owned by physicians had adjusted readmission rates 0.36 percentage points lower than nonphysician‐owned hospitals.

Regression Results: Organizational and Environmental Predictors of Hospital Readmissions
Variable Coefficient Standard Error P Value

  • NOTE: Adjusted R2 = 16, F = 20.62, P = 0.00. Abbreviations: RNs, registered nurses.

Hospitalists per general and surgical beds 0.77 0.172 0.00
Full integration 0.086 0.049 0.08
Physician ownership 0.355 0.119 0.00
RNs per 100 inpatient days 0.174 0.050 0.00
Established medical home program 0.132 0.057 0.02
Medicare admissions 0.063 0.002 0.21
Medicaid admissions 0.015 0.003 0.00
Competition 0.115 0.08 0.17
Unemployment 0.244 0.037 0.00
System membership 0.041 0.055 0.45
Teaching level
Minor teaching 0.007 0.066 0.92
Major teaching 1.032 0.106 0.00
Size
Medium 0.032 0.071 0.66
Large 0.066 0.085 0.44
For‐profit ownership 0.206 0.078 0.01
High technology 0.077 0.055 0.17
Critical access hospital 0.202 0.092 0.03

Based on the regression analysis, major teaching hospitals on average had adjusted readmission rates 1.03 percentage point higher than nonteaching hospitals (P = 0.000), whereas there was no significant difference between minor and nonteaching hospitals (P > 0.1). As the number of registered nurses (RNs) per 100 inpatient days increased by 1, readmission rates dropped by 0.17 (P = 0.00). Hospitals with higher Medicaid shares of admission had significantly higher readmission rates (P < 0.05). Hospitals located in counties with higher unemployment rates also had higher readmission rates (P = 0.000), whereas market competition had no significant association with readmissions. For‐profit hospitals had adjusted readmission rates 0.21 percentage points higher than not‐for‐profit hospitals (P = 0.01). Finally, hospitals that have adopted a medical home model had significantly lower readmission rates (P = 0.02); hospitals with an established medical home model had adjusted readmission rates 0.17 percentage points lower than their counterparts.

DISCUSSION

In the era of VBP and mounting pressures on hospitals to improve quality and lower cost, it is important to understand the association between modifiable hospital characteristics, such as hospitalist staffing levels, and unmodifiable characteristics, such as teaching status and size, with quality of care. There are many factors that can contribute to higher readmission rates. Some of these factors are hospital related and others are patient related, such as the environment in which a patient resides. Benbassat and Taragin argue that 9% to 48% of hospital readmissions are avoidable and are related to factors such as inadequate resolution of the problem the patient was admitted for and poor discharge care.[22] In this article, we have focused on hospital and market factors. Our main variables of interest were hospitalist staffing level, physician full integration, physician ownership, and the adoption of the medical home model at the hospital. Moreover, we examined the association between the hospital environment, specifically, market competition, and the patient environment, specifically, unemployment rates, with readmission rates.

Hospitalists' provision of inpatient care has been on the rise. From 1997 to 2006, the likelihood of receiving inpatient care from a hospitalist grew by 29.2% per year.[23] Based on AHA (2013) data, 65% of hospitals reported that hospitalists provided care at the hospital. The main driver behind the adoption of the hospitalists' model is the positive role hospitalists play in improving hospital efficiency and their familiarity and specialization in hospital care.[24] However, concerns exist that hospitalists might negatively influence patient outcomes given the discontinuity of care that occurs once the patient is discharged from the hospital and back to the care of their primary care physician.[25] Based on our analysis though, higher hospitalist staffing levels were associated with lower readmission rates. Therefore, to better understand the relationship between hospitalists and quality, it is important to account for staffing levels, not merely whether hospitalists provide care at the hospital or not. Higher patient load per hospitalist might still improve hospital efficiency by lowering costs, but is it likely to impede the quality of care provided by hospitalists. This is not surprising given similar findings, including in this article, which document a similar positive relationship between nursing staffing levels and quality.

Hospitals utilize various arrangements with physicians that range from employment to more relaxed arrangements such as physicians with privileges who are neither employed by the hospital nor under individual or group contracts. Historically, the main incentive for hospitals to integrate physicians was referrals to hospital services and specialties.[16, 26] The Affordable Care Act, however, provided further incentives, such as ease of care coordination, physicians' involvement, and commitment to quality improvement and cost‐containment efforts. Based on this study, hospitals that were classified as fully integrated had lower readmission rates. Also, hospitals partially or fully owned by physicians had better readmission rates. These findings indicate that hospital‐physician arrangements play a significant role not only in influencing efficiency and market share but also patient outcomes. Physician integration and physician ownership align physicians' financial incentives with those of the hospital. For instance, given the recent changes in reimbursement and the shift toward VBP, physician income in physician‐owned hospitals is at risk if the hospital has poor patient outcomes.

Other significant predictors of readmission rates included the adoption of the medical home model and RN staffing levels. Hospitals that adopted a medical home model and had a higher registered nurse‐to‐inpatient days ratio had significantly better readmission rates. The finding on the adoption of the medical home model is especially important. Previous research indicates that patient‐centered medical homes are associated with lower emergency room visits but not necessarily lower admissions.[27] Our findings indicate that medical homes might play a role in lowering readmission rates, and therefore this outcome needs to be included in studies examining the performance of medical homes. Critical access hospitals and those with higher admissions share of Medicaid patients had worst readmission rates. Finally, hospitals located in counties with higher unemployment rates also had the worst readmission rates. This finding is not surprising and is consistent with previous research, which indicates that the patients' environment and social risk factors play a significant role.

This article contributes to our understanding of readmission rates despite its several limitations, which include the measurement of hospitalist staffing levels based on general medical and surgical beds rather than general medicine admissions. Moreover, some hospitals had missing data on key variables, which warranted their exclusion from this study. In conclusion, many structural, operational and market‐level factors influence all‐cause readmission rates. However, some of these variables are modifiable and can thus be adjusted by a hospital to improve readmission rates. These variables include hospitalists and registered nurse staffing levels; physician integration through the salaried, equity, or foundation model; and the adoption of a medical home model.

Disclosure

Nothing to report.

References
  1. Berenson RA, Paulus RA, Kalman NS. Medicare's readmissions‐reduction program—a positive alternative. N Engl J Med. 2012;366:1364–1366.
  2. Gohil SK, Datta R, Cao C, et al. Impact of hospital population case‐mix, including poverty, on hospital all‐cause and infection‐related 30‐day readmission rates. Clin Infect Dis. 2015;31(2):1235–1243.
  3. Krumholz HM, Merrill AR, Schone EM, et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2:407–413.
  4. Singh S, Lin Y‐L, Kuo Y‐F, Nattinger AB, Goodwin JS. Variation in the risk of readmission among hospitals: the relative contribution of patient, hospital and inpatient provider characteristics. J Gen Intern Med. 2014;29:572–578.
  5. Joynt KE, Jha AK. A path forward on Medicare readmissions. N Engl J Med. 2013;368:1175–1177.
  6. McHugh MD, Berez J, Small DS. Hospitals with higher nurse staffing had lower odds of readmissions penalties than hospitals with lower staffing. Health Aff (Millwood). 2013;32:1740–1747.
  7. Tsai TC, Joynt KE, Orav EJ, Gawande AA, Jha AK. Variation in surgical‐readmission rates and quality of hospital care. N Engl J Med. 2013;369:1134–1142.
  8. Gilfillan RJ, Tomcavage J, Rosenthal MB, et al. Value and the medical home: effects of transformed primary care. Am J Manag Care. 2010;16.8:607–614.
  9. Davis KM, Koch KE, Harvey JK, Wilson R, Englert J, Gerard PD. Effects of hospitalists on cost, outcomes, and patient satisfaction in a rural health system. Am J Med. 2000;108:621–626.
  10. Elliott DJ, Young RS, Brice J, Aguiar R, Kolm P. Effect of hospitalist workload on the quality and efficiency of care. JAMA Intern Med. 2014;174:786–793.
  11. Jungerwirth R, Wheeler SB, Paul JE. Association of hospitalist presence and hospital‐level outcome measures among Medicare patients. J Hosp Med. 2014;9:1–6.
  12. Goodrich K, Krumholz HM, Conway PH, Lindenauer P, Auerbach AD. Hospitalist utilization and hospital performance on 6 publicly reported patient outcomes. J Hosp Med. 2012;7:482–488.
  13. Michtalik HJ, Yeh H, Pronovost PJ, Brotman DJ. Impact of attending physician workload on patient care: a survey of hospitalists. JAMA Intern Med. 2013;173:375–377.
  14. O'Malley AS, Bond AM, Berenson RA. Rising hospital employment of physicians: better quality, higher costs. Issue Brief Cent Stud Health Syst Change. 2011;136:1–4.
  15. Charles AG, Ortiz‐Pujols S, Ricketts T, et al. The employed surgeon: a changing professional paradigm. JAMA Surg. 2013;148:323–328.
  16. Baker LC, Bundorf MK, Kessler DP. Vertical integration: hospital ownership of physician practices is associated with higher prices and spending. Health Aff (Millwood). 2014;33:756–763.
  17. Horwitz L, Partovian C, Lin Z, et al. Hospital‐wide (all‐condition) 30‐day risk‐standardized readmission measure: Yale New Haven Health Services Corporation. Center for Outcomes Research 161(10 suppl):S66–S75.
  18. Burns L, Muller R. Hospital‐physician collaboration: landscape of economic integration and impact on clinical integration. Milbank Q. 2008;86(3):375–434.
  19. Aiken LH, Clarke SP, Sloane DM, Lake ET, Cheney T. Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2008;38:223–229.
  20. Aiken LH, Cimiotti JP, Sloane DM, Smith HL, Flynn L, Neff DF. The effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Med Care. 2011;49(12):1047–1053.
  21. Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Arch Intern Med. 2000;160:1074–1081.
  22. Kuo Y‐F, Sharma G, Freeman JL, Goodwin JS. Growth in the care of older patients by hospitalists in the United States. N Engl J Med. 2009;360:1102–1112.
  23. Wachter RM. Reflections: the hospitalist movement a decade later. J Hosp Med. 2006;1:248–252.
  24. Goodwin JS, Lin Y‐L, Singh S, Kuo Y‐F. Variation in length of stay and outcomes among hospitalized patients attributable to hospitals and hospitalists. J Gen Intern Med. 2013;28:370–376.
  25. Bacher GE, Chernew ME, Kessler DP, Weiner SM. Regulatory neutrality is essential to establishing a level playing field for accountable care organizations. Health Aff (Millwood). 2013;32:1426–1432.
  26. Rittenhouse DR, Shortell SM. The patient‐centered medical home: will it stand the test of health reform? JAMA. 2009;301:2038–2040.
  27. Joynt KE, Jha AK. Who has higher readmission rates for heart failure, and why?: implications for efforts to improve care using financial incentives. Circ Cardiovasc Qual Outcomes. 2011;4:53–59.
References
  1. Berenson RA, Paulus RA, Kalman NS. Medicare's readmissions‐reduction program—a positive alternative. N Engl J Med. 2012;366:1364–1366.
  2. Gohil SK, Datta R, Cao C, et al. Impact of hospital population case‐mix, including poverty, on hospital all‐cause and infection‐related 30‐day readmission rates. Clin Infect Dis. 2015;31(2):1235–1243.
  3. Krumholz HM, Merrill AR, Schone EM, et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2:407–413.
  4. Singh S, Lin Y‐L, Kuo Y‐F, Nattinger AB, Goodwin JS. Variation in the risk of readmission among hospitals: the relative contribution of patient, hospital and inpatient provider characteristics. J Gen Intern Med. 2014;29:572–578.
  5. Joynt KE, Jha AK. A path forward on Medicare readmissions. N Engl J Med. 2013;368:1175–1177.
  6. McHugh MD, Berez J, Small DS. Hospitals with higher nurse staffing had lower odds of readmissions penalties than hospitals with lower staffing. Health Aff (Millwood). 2013;32:1740–1747.
  7. Tsai TC, Joynt KE, Orav EJ, Gawande AA, Jha AK. Variation in surgical‐readmission rates and quality of hospital care. N Engl J Med. 2013;369:1134–1142.
  8. Gilfillan RJ, Tomcavage J, Rosenthal MB, et al. Value and the medical home: effects of transformed primary care. Am J Manag Care. 2010;16.8:607–614.
  9. Davis KM, Koch KE, Harvey JK, Wilson R, Englert J, Gerard PD. Effects of hospitalists on cost, outcomes, and patient satisfaction in a rural health system. Am J Med. 2000;108:621–626.
  10. Elliott DJ, Young RS, Brice J, Aguiar R, Kolm P. Effect of hospitalist workload on the quality and efficiency of care. JAMA Intern Med. 2014;174:786–793.
  11. Jungerwirth R, Wheeler SB, Paul JE. Association of hospitalist presence and hospital‐level outcome measures among Medicare patients. J Hosp Med. 2014;9:1–6.
  12. Goodrich K, Krumholz HM, Conway PH, Lindenauer P, Auerbach AD. Hospitalist utilization and hospital performance on 6 publicly reported patient outcomes. J Hosp Med. 2012;7:482–488.
  13. Michtalik HJ, Yeh H, Pronovost PJ, Brotman DJ. Impact of attending physician workload on patient care: a survey of hospitalists. JAMA Intern Med. 2013;173:375–377.
  14. O'Malley AS, Bond AM, Berenson RA. Rising hospital employment of physicians: better quality, higher costs. Issue Brief Cent Stud Health Syst Change. 2011;136:1–4.
  15. Charles AG, Ortiz‐Pujols S, Ricketts T, et al. The employed surgeon: a changing professional paradigm. JAMA Surg. 2013;148:323–328.
  16. Baker LC, Bundorf MK, Kessler DP. Vertical integration: hospital ownership of physician practices is associated with higher prices and spending. Health Aff (Millwood). 2014;33:756–763.
  17. Horwitz L, Partovian C, Lin Z, et al. Hospital‐wide (all‐condition) 30‐day risk‐standardized readmission measure: Yale New Haven Health Services Corporation. Center for Outcomes Research 161(10 suppl):S66–S75.
  18. Burns L, Muller R. Hospital‐physician collaboration: landscape of economic integration and impact on clinical integration. Milbank Q. 2008;86(3):375–434.
  19. Aiken LH, Clarke SP, Sloane DM, Lake ET, Cheney T. Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2008;38:223–229.
  20. Aiken LH, Cimiotti JP, Sloane DM, Smith HL, Flynn L, Neff DF. The effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Med Care. 2011;49(12):1047–1053.
  21. Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Arch Intern Med. 2000;160:1074–1081.
  22. Kuo Y‐F, Sharma G, Freeman JL, Goodwin JS. Growth in the care of older patients by hospitalists in the United States. N Engl J Med. 2009;360:1102–1112.
  23. Wachter RM. Reflections: the hospitalist movement a decade later. J Hosp Med. 2006;1:248–252.
  24. Goodwin JS, Lin Y‐L, Singh S, Kuo Y‐F. Variation in length of stay and outcomes among hospitalized patients attributable to hospitals and hospitalists. J Gen Intern Med. 2013;28:370–376.
  25. Bacher GE, Chernew ME, Kessler DP, Weiner SM. Regulatory neutrality is essential to establishing a level playing field for accountable care organizations. Health Aff (Millwood). 2013;32:1426–1432.
  26. Rittenhouse DR, Shortell SM. The patient‐centered medical home: will it stand the test of health reform? JAMA. 2009;301:2038–2040.
  27. Joynt KE, Jha AK. Who has higher readmission rates for heart failure, and why?: implications for efforts to improve care using financial incentives. Circ Cardiovasc Qual Outcomes. 2011;4:53–59.
Issue
Journal of Hospital Medicine - 11(10)
Issue
Journal of Hospital Medicine - 11(10)
Page Number
682-687
Page Number
682-687
Article Type
Article
Display Headline
Hospital characteristics and 30‐day all‐cause readmission rates
Display Headline
Hospital characteristics and 30‐day all‐cause readmission rates
Sections
Original Research
Article Source
© 2016 Society of Hospital Medicine
Disallow All Ads
Corresponding Author
Mona Al‐Amin, PhD, MPH
Correspondence Location
Address for correspondence and reprint requests: Mona Al‐Amin, PhD, Associate Professor, Healthcare Administration Department, Sawyer Business School, Suffolk University, 120 Tremont Street, Room 5603, Boston, MA 02108; Telephone: 617‐573‐8794; Fax: 617‐720‐3579; E‐mail: [email protected]
Content Gating
Gated (full article locked unless allowed per User)
Gating Strategy
First Peek Free
Article PDF Media
Media Files

From the Washington Office: Brave new world of acronyms

Article Type
News
Changed
Thu, 03/28/2019 - 15:08
Display Headline
From the Washington Office: Brave new world of acronyms
Author(s)
Patrick V. Bailey, MD, FACS

Just over a year ago, Congress passed and the President signed into law the MACRA legislation, which will serve as the basis for Medicare physician payment beginning in 2019. At the recent Leadership and Advocacy Summit, it became apparent to me that a “refresher” on seven key acronyms would be useful for surgeons as they gear up to understand and effectively participate in this “brave new world” which is rapidly approaching.

Accordingly, let us start at the beginning. MACRA stands for the Medicare Access and CHIP (Children’s Health Insurance Program) Reauthorization Act of 2015. As noted above, this legislation, signed into law by President Obama on April 16, 2015, replaces the flawed sustainable growth rate formula and will be the template utilized to determine Medicare physician payment beginning in 2019. However, it is important to note that it is anticipated that the data to be utilized as the basis for payment in 2019 will likely be collected sometime in 2017.

MACRA provides modest but stable positive updates of 0.5 percent/year for the 5-year period of 2015-2019. Fellows may remember that this provision was included in the legislation as a direct result of objections made by the leadership of the ACS to the original draft legislation, which contained no provision for a positive update. In addition, MACRA provides for the elimination, after 2018, of the current-law penalties associated with the existing Medicare quality programs, including the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier) program and the EHR-MU (Electronic Health Record–Meaningful Use) program. That said, and as outlined below, we will not be saying goodbye to these programs completely. Accordingly, surgeons need to remain, or become, familiar with those acronyms and the programs they represent.

MACRA has two payment pathways. Physicians will choose to participate in one or the other. Those choices are: 1) MIPS (Merit-based Incentive Payment System) and 2) APMs (Alternative Payment Models).

Beginning in 2019, the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier) program and the EHR-MU (Electronic Health Record–Meaningful Use) program will be combined into MIPS (Merit-based Incentive Payment System). In this program, it is possible for all surgeons to receive an annual positive update based on their individual performance in the four categories of Quality, Resource Use, Electronic Health Record–Meaningful Use, and lastly the newly created category of Clinical Practice Improvement Activities (CPIA).

Individual surgeons’ performance in the four categories will be combined into a composite score. Each individual composite score will then be compared with a performance threshold. The threshold will be set as either the mean or median of the composite performance scores for all MIPS-eligible professionals from a prior performance period. The threshold will reset every year. Those with an individual composite performance score above the threshold will receive a positive payment adjustment while those with an individual composite performance score below the threshold will receive a negative payment adjustment.

The Quality component of the MIPS will consist of quality measures currently used in existing quality performance programs namely, the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier program), and EHR-MU (Electronic Health Record–Meaningful Use), as well as measures developed by stakeholders to meet the needs of specialties lacking meaningful measures in the current programs. The RESOURCE USE component of MIPS will include the cost measures used in the current VBM (Value-Based Modifier) program. With regard to the Electronic Health Record–Meaningful Use (EHR-MU) component of MIPS, current EHR-MU requirements will continue to apply but are expected to be modified significantly. ACS continues to advocate for changes to the EHR-MU program to make it easier for surgeons to comply with requirements. Evidence of the effectiveness of our advocacy in this area is found in the success achieved in obtaining a blanket exception for the 2015 reporting period, Stage 2 Meaningful Use rule about which I wrote in the December 2015 and January 2016 editions of this column.

The CPIA (Clinical Practice Improvement Activities) are designed to assess surgeons’ effort toward improving their clinical practice and/or their preparation toward participating in APMs (Alternative Payment Models). The menu of specific, approved activities has yet to be firmly established. ACS provided significant input on the CPIA component of MIPS in our November 2015 response to the request for information issued by the Centers for Medicare & Medicaid Services (CMS) last fall. The MACRA legislation specifies that the CPIA be applicable to all specialties and be attainable for small practices and professionals in rural and underserved areas.

Those Fellows interested in knowing specifically the areas on which CMS requested input in the process of drafting the first proposed rule on MACRA and how ACS responded to same may find the letter sent in response to CMS at https://www.facs.org/~/media/files/advocacy/medicare/cms%20mips%20apm%20rfi%20final.ashx.

 

 

The new law takes concerted steps to incentivize and encourage providers to develop and participate in APMs (Alternative Payment Models). As with the CPIA discussed above, the details of APMs are not yet fully clear and are currently being developed. ACS is actively working on behalf of surgeons to develop APMs as part of the policy efforts of the Division of Advocacy and Health Policy. In general, these programs will require quality measures, the inclusion of elements of upside and downside financial risk for providers and use of certified electronic health record technology. For those surgeons who receive a significant share of their revenue from an APM, an annual 5% bonus will be available for each of the years 2019-2024. To qualify for that bonus, surgeons must receive 25% of their Medicare revenue from an APM in the years 2019 and 2020. That threshold requirement subsequently increases to 50% in 2021 and ultimately to 75% beginning in 2023.

As MACRA specifies that providers participate in either MIPS or APMs, surgeons who meet the aforementioned threshold of payment from a qualified APM will be exempted from many of the MIPS reporting requirements and receive the 5% bonus in lieu of the previously described MIPS payment adjustment. Those who participate in APMs but fail to meet the threshold necessary to receive the 5% bonus will receive credit for their participation in the CPIA component of their MIPS composite score but will not receive the 5% incentive.

While it is completely understandable that acronyms add to surgeons’ collective frustration, I am confident that all Fellows can, with relative ease, master the seven acronyms above and thus be well on their way to both understanding and successfully participating in the new Medicare physician payment system.

Until next month …

Dr. Patrick V. Bailey is an ACS Fellow, a pediatric surgeon, and Medical Director, Advocacy, for the Division of Advocacy and Health Policy, in the ACS offices in Washington, D.C.

References

Author and Disclosure Information

Publications
ACS Surgery News
MDedge Surgery
Topics
Practice Management
Business of Medicine
Sections
From the Washington Office
Commentary
Author(s)
Patrick V. Bailey, MD, FACS
Author(s)
Patrick V. Bailey, MD, FACS
Author and Disclosure Information

Author and Disclosure Information

Just over a year ago, Congress passed and the President signed into law the MACRA legislation, which will serve as the basis for Medicare physician payment beginning in 2019. At the recent Leadership and Advocacy Summit, it became apparent to me that a “refresher” on seven key acronyms would be useful for surgeons as they gear up to understand and effectively participate in this “brave new world” which is rapidly approaching.

Accordingly, let us start at the beginning. MACRA stands for the Medicare Access and CHIP (Children’s Health Insurance Program) Reauthorization Act of 2015. As noted above, this legislation, signed into law by President Obama on April 16, 2015, replaces the flawed sustainable growth rate formula and will be the template utilized to determine Medicare physician payment beginning in 2019. However, it is important to note that it is anticipated that the data to be utilized as the basis for payment in 2019 will likely be collected sometime in 2017.

MACRA provides modest but stable positive updates of 0.5 percent/year for the 5-year period of 2015-2019. Fellows may remember that this provision was included in the legislation as a direct result of objections made by the leadership of the ACS to the original draft legislation, which contained no provision for a positive update. In addition, MACRA provides for the elimination, after 2018, of the current-law penalties associated with the existing Medicare quality programs, including the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier) program and the EHR-MU (Electronic Health Record–Meaningful Use) program. That said, and as outlined below, we will not be saying goodbye to these programs completely. Accordingly, surgeons need to remain, or become, familiar with those acronyms and the programs they represent.

MACRA has two payment pathways. Physicians will choose to participate in one or the other. Those choices are: 1) MIPS (Merit-based Incentive Payment System) and 2) APMs (Alternative Payment Models).

Beginning in 2019, the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier) program and the EHR-MU (Electronic Health Record–Meaningful Use) program will be combined into MIPS (Merit-based Incentive Payment System). In this program, it is possible for all surgeons to receive an annual positive update based on their individual performance in the four categories of Quality, Resource Use, Electronic Health Record–Meaningful Use, and lastly the newly created category of Clinical Practice Improvement Activities (CPIA).

Individual surgeons’ performance in the four categories will be combined into a composite score. Each individual composite score will then be compared with a performance threshold. The threshold will be set as either the mean or median of the composite performance scores for all MIPS-eligible professionals from a prior performance period. The threshold will reset every year. Those with an individual composite performance score above the threshold will receive a positive payment adjustment while those with an individual composite performance score below the threshold will receive a negative payment adjustment.

The Quality component of the MIPS will consist of quality measures currently used in existing quality performance programs namely, the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier program), and EHR-MU (Electronic Health Record–Meaningful Use), as well as measures developed by stakeholders to meet the needs of specialties lacking meaningful measures in the current programs. The RESOURCE USE component of MIPS will include the cost measures used in the current VBM (Value-Based Modifier) program. With regard to the Electronic Health Record–Meaningful Use (EHR-MU) component of MIPS, current EHR-MU requirements will continue to apply but are expected to be modified significantly. ACS continues to advocate for changes to the EHR-MU program to make it easier for surgeons to comply with requirements. Evidence of the effectiveness of our advocacy in this area is found in the success achieved in obtaining a blanket exception for the 2015 reporting period, Stage 2 Meaningful Use rule about which I wrote in the December 2015 and January 2016 editions of this column.

The CPIA (Clinical Practice Improvement Activities) are designed to assess surgeons’ effort toward improving their clinical practice and/or their preparation toward participating in APMs (Alternative Payment Models). The menu of specific, approved activities has yet to be firmly established. ACS provided significant input on the CPIA component of MIPS in our November 2015 response to the request for information issued by the Centers for Medicare & Medicaid Services (CMS) last fall. The MACRA legislation specifies that the CPIA be applicable to all specialties and be attainable for small practices and professionals in rural and underserved areas.

Those Fellows interested in knowing specifically the areas on which CMS requested input in the process of drafting the first proposed rule on MACRA and how ACS responded to same may find the letter sent in response to CMS at https://www.facs.org/~/media/files/advocacy/medicare/cms%20mips%20apm%20rfi%20final.ashx.

 

 

The new law takes concerted steps to incentivize and encourage providers to develop and participate in APMs (Alternative Payment Models). As with the CPIA discussed above, the details of APMs are not yet fully clear and are currently being developed. ACS is actively working on behalf of surgeons to develop APMs as part of the policy efforts of the Division of Advocacy and Health Policy. In general, these programs will require quality measures, the inclusion of elements of upside and downside financial risk for providers and use of certified electronic health record technology. For those surgeons who receive a significant share of their revenue from an APM, an annual 5% bonus will be available for each of the years 2019-2024. To qualify for that bonus, surgeons must receive 25% of their Medicare revenue from an APM in the years 2019 and 2020. That threshold requirement subsequently increases to 50% in 2021 and ultimately to 75% beginning in 2023.

As MACRA specifies that providers participate in either MIPS or APMs, surgeons who meet the aforementioned threshold of payment from a qualified APM will be exempted from many of the MIPS reporting requirements and receive the 5% bonus in lieu of the previously described MIPS payment adjustment. Those who participate in APMs but fail to meet the threshold necessary to receive the 5% bonus will receive credit for their participation in the CPIA component of their MIPS composite score but will not receive the 5% incentive.

While it is completely understandable that acronyms add to surgeons’ collective frustration, I am confident that all Fellows can, with relative ease, master the seven acronyms above and thus be well on their way to both understanding and successfully participating in the new Medicare physician payment system.

Until next month …

Dr. Patrick V. Bailey is an ACS Fellow, a pediatric surgeon, and Medical Director, Advocacy, for the Division of Advocacy and Health Policy, in the ACS offices in Washington, D.C.

Just over a year ago, Congress passed and the President signed into law the MACRA legislation, which will serve as the basis for Medicare physician payment beginning in 2019. At the recent Leadership and Advocacy Summit, it became apparent to me that a “refresher” on seven key acronyms would be useful for surgeons as they gear up to understand and effectively participate in this “brave new world” which is rapidly approaching.

Accordingly, let us start at the beginning. MACRA stands for the Medicare Access and CHIP (Children’s Health Insurance Program) Reauthorization Act of 2015. As noted above, this legislation, signed into law by President Obama on April 16, 2015, replaces the flawed sustainable growth rate formula and will be the template utilized to determine Medicare physician payment beginning in 2019. However, it is important to note that it is anticipated that the data to be utilized as the basis for payment in 2019 will likely be collected sometime in 2017.

MACRA provides modest but stable positive updates of 0.5 percent/year for the 5-year period of 2015-2019. Fellows may remember that this provision was included in the legislation as a direct result of objections made by the leadership of the ACS to the original draft legislation, which contained no provision for a positive update. In addition, MACRA provides for the elimination, after 2018, of the current-law penalties associated with the existing Medicare quality programs, including the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier) program and the EHR-MU (Electronic Health Record–Meaningful Use) program. That said, and as outlined below, we will not be saying goodbye to these programs completely. Accordingly, surgeons need to remain, or become, familiar with those acronyms and the programs they represent.

MACRA has two payment pathways. Physicians will choose to participate in one or the other. Those choices are: 1) MIPS (Merit-based Incentive Payment System) and 2) APMs (Alternative Payment Models).

Beginning in 2019, the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier) program and the EHR-MU (Electronic Health Record–Meaningful Use) program will be combined into MIPS (Merit-based Incentive Payment System). In this program, it is possible for all surgeons to receive an annual positive update based on their individual performance in the four categories of Quality, Resource Use, Electronic Health Record–Meaningful Use, and lastly the newly created category of Clinical Practice Improvement Activities (CPIA).

Individual surgeons’ performance in the four categories will be combined into a composite score. Each individual composite score will then be compared with a performance threshold. The threshold will be set as either the mean or median of the composite performance scores for all MIPS-eligible professionals from a prior performance period. The threshold will reset every year. Those with an individual composite performance score above the threshold will receive a positive payment adjustment while those with an individual composite performance score below the threshold will receive a negative payment adjustment.

The Quality component of the MIPS will consist of quality measures currently used in existing quality performance programs namely, the PQRS (Physician Quality Reporting System), the VBM (Value-Based Modifier program), and EHR-MU (Electronic Health Record–Meaningful Use), as well as measures developed by stakeholders to meet the needs of specialties lacking meaningful measures in the current programs. The RESOURCE USE component of MIPS will include the cost measures used in the current VBM (Value-Based Modifier) program. With regard to the Electronic Health Record–Meaningful Use (EHR-MU) component of MIPS, current EHR-MU requirements will continue to apply but are expected to be modified significantly. ACS continues to advocate for changes to the EHR-MU program to make it easier for surgeons to comply with requirements. Evidence of the effectiveness of our advocacy in this area is found in the success achieved in obtaining a blanket exception for the 2015 reporting period, Stage 2 Meaningful Use rule about which I wrote in the December 2015 and January 2016 editions of this column.

The CPIA (Clinical Practice Improvement Activities) are designed to assess surgeons’ effort toward improving their clinical practice and/or their preparation toward participating in APMs (Alternative Payment Models). The menu of specific, approved activities has yet to be firmly established. ACS provided significant input on the CPIA component of MIPS in our November 2015 response to the request for information issued by the Centers for Medicare & Medicaid Services (CMS) last fall. The MACRA legislation specifies that the CPIA be applicable to all specialties and be attainable for small practices and professionals in rural and underserved areas.

Those Fellows interested in knowing specifically the areas on which CMS requested input in the process of drafting the first proposed rule on MACRA and how ACS responded to same may find the letter sent in response to CMS at https://www.facs.org/~/media/files/advocacy/medicare/cms%20mips%20apm%20rfi%20final.ashx.

 

 

The new law takes concerted steps to incentivize and encourage providers to develop and participate in APMs (Alternative Payment Models). As with the CPIA discussed above, the details of APMs are not yet fully clear and are currently being developed. ACS is actively working on behalf of surgeons to develop APMs as part of the policy efforts of the Division of Advocacy and Health Policy. In general, these programs will require quality measures, the inclusion of elements of upside and downside financial risk for providers and use of certified electronic health record technology. For those surgeons who receive a significant share of their revenue from an APM, an annual 5% bonus will be available for each of the years 2019-2024. To qualify for that bonus, surgeons must receive 25% of their Medicare revenue from an APM in the years 2019 and 2020. That threshold requirement subsequently increases to 50% in 2021 and ultimately to 75% beginning in 2023.

As MACRA specifies that providers participate in either MIPS or APMs, surgeons who meet the aforementioned threshold of payment from a qualified APM will be exempted from many of the MIPS reporting requirements and receive the 5% bonus in lieu of the previously described MIPS payment adjustment. Those who participate in APMs but fail to meet the threshold necessary to receive the 5% bonus will receive credit for their participation in the CPIA component of their MIPS composite score but will not receive the 5% incentive.

While it is completely understandable that acronyms add to surgeons’ collective frustration, I am confident that all Fellows can, with relative ease, master the seven acronyms above and thus be well on their way to both understanding and successfully participating in the new Medicare physician payment system.

Until next month …

Dr. Patrick V. Bailey is an ACS Fellow, a pediatric surgeon, and Medical Director, Advocacy, for the Division of Advocacy and Health Policy, in the ACS offices in Washington, D.C.

References

References

Publications
ACS Surgery News
MDedge Surgery
Publications
ACS Surgery News
MDedge Surgery
Topics
Practice Management
Business of Medicine
Article Type
News
Display Headline
From the Washington Office: Brave new world of acronyms
Display Headline
From the Washington Office: Brave new world of acronyms
Sections
From the Washington Office
Commentary
Article Source

PURLs Copyright

Inside the Article

Teaser Media

2016 Leadership Summit Focuses on Communication and Team Building

Article Type
News
Changed
Wed, 01/02/2019 - 09:33
Display Headline
2016 Leadership Summit Focuses on Communication and Team Building

The American College of Surgeons (ACS) hosted the fifth annual Leadership & Advocacy Summit, April 9–12, at the JW Marriott in Washington, DC. More than 445 College leaders, residents, and medical students participated in the Leadership portion of the Summit, which featured a full day of sessions on effective leadership building communication and strategic thinking skills for effective leadership in and out of the operating room. The Leadership Summit also provided attendees with ample networking opportunities.

The Leadership Summit began with a well-attended Welcome Reception on Saturday evening. Sunday’s program featured nine presentations on such topics as preparing for difficult conversations, best practices for social networking, improving team emotional intelligence, leading teams through conflict situations, and sharpening strategic thinking skills. Leaders from the Georgia, North Texas, and West Virginia Chapters of the ACS presented their chapter’s success stories from the past year. Leadership Summit attendees also convened over lunch by state/region to identify new areas for collaboration in the coming year.

Details regarding the Leadership Summit will be published in the July Bulletin of the American College of Surgeons at http://bulletin.facs.org/. The sixth annual Leadership & Advocacy Summit will take place May 6−9, 2017 at the Renaissance Washington, DC Downtown Hotel. For more information on the Leadership Summit, contact Donna Tieberg, ACS International Chapter Services Manager, at [email protected].

References

Author and Disclosure Information

Publications
ACS Surgery News
MDedge Surgery
Sections
News From the College
Author and Disclosure Information

Author and Disclosure Information

The American College of Surgeons (ACS) hosted the fifth annual Leadership & Advocacy Summit, April 9–12, at the JW Marriott in Washington, DC. More than 445 College leaders, residents, and medical students participated in the Leadership portion of the Summit, which featured a full day of sessions on effective leadership building communication and strategic thinking skills for effective leadership in and out of the operating room. The Leadership Summit also provided attendees with ample networking opportunities.

The Leadership Summit began with a well-attended Welcome Reception on Saturday evening. Sunday’s program featured nine presentations on such topics as preparing for difficult conversations, best practices for social networking, improving team emotional intelligence, leading teams through conflict situations, and sharpening strategic thinking skills. Leaders from the Georgia, North Texas, and West Virginia Chapters of the ACS presented their chapter’s success stories from the past year. Leadership Summit attendees also convened over lunch by state/region to identify new areas for collaboration in the coming year.

Details regarding the Leadership Summit will be published in the July Bulletin of the American College of Surgeons at http://bulletin.facs.org/. The sixth annual Leadership & Advocacy Summit will take place May 6−9, 2017 at the Renaissance Washington, DC Downtown Hotel. For more information on the Leadership Summit, contact Donna Tieberg, ACS International Chapter Services Manager, at [email protected].

The American College of Surgeons (ACS) hosted the fifth annual Leadership & Advocacy Summit, April 9–12, at the JW Marriott in Washington, DC. More than 445 College leaders, residents, and medical students participated in the Leadership portion of the Summit, which featured a full day of sessions on effective leadership building communication and strategic thinking skills for effective leadership in and out of the operating room. The Leadership Summit also provided attendees with ample networking opportunities.

The Leadership Summit began with a well-attended Welcome Reception on Saturday evening. Sunday’s program featured nine presentations on such topics as preparing for difficult conversations, best practices for social networking, improving team emotional intelligence, leading teams through conflict situations, and sharpening strategic thinking skills. Leaders from the Georgia, North Texas, and West Virginia Chapters of the ACS presented their chapter’s success stories from the past year. Leadership Summit attendees also convened over lunch by state/region to identify new areas for collaboration in the coming year.

Details regarding the Leadership Summit will be published in the July Bulletin of the American College of Surgeons at http://bulletin.facs.org/. The sixth annual Leadership & Advocacy Summit will take place May 6−9, 2017 at the Renaissance Washington, DC Downtown Hotel. For more information on the Leadership Summit, contact Donna Tieberg, ACS International Chapter Services Manager, at [email protected].

References

References

Publications
ACS Surgery News
MDedge Surgery
Publications
ACS Surgery News
MDedge Surgery
Article Type
News
Display Headline
2016 Leadership Summit Focuses on Communication and Team Building
Display Headline
2016 Leadership Summit Focuses on Communication and Team Building
Sections
News From the College
Article Source

PURLs Copyright

Inside the Article

Survey: Civilians support wider access to education on how to help victims of mass casualty events

Article Type
News
Changed
Wed, 01/02/2019 - 09:33
Display Headline
Survey: Civilians support wider access to education on how to help victims of mass casualty events

Many civilians have expressed interest in taking a bleeding control training course that would empower them to immediately assist victims of active shooter and other intentional mass casualty events at the point of wounding, according to the results of a national poll published in the Journal of the American College of Surgeons (JACS). Furthermore, most civilians support training and equipping police officers to perform severe bleeding control on victims as soon as possible, rather than wait for emergency medical services (EMS) personnel to arrive on the scene. Survey respondents also supported the placement of bleeding control kits in public places where large crowds gather, similar to the way that automatic external defibrillators are now found in airports and shopping malls.

Working to save lives

The Joint Committee to Create a National Policy to Enhance Survivability from Intentional Mass Casualty and Active Shooter Events, convened by the American College of Surgeons, recommends careful consideration of these study results. The committee’s deliberations are known as the Hartford Consensus™. The Hartford Consensus reports have been published in the Bulletin and JACS since the group’s formation in 2013 and promote the group’s core principle that “no one should die from uncontrolled bleeding.”

To that end, the Hartford Consensus calls for providing law enforcement officers with the training and equipment needed to act before EMS personnel arrive, providing EMS professionals with quicker access to the wounded, and training civilian bystanders to act as immediate responders. This element from the Hartford Consensus is at the heart of the “Stop the Bleed” campaign launched by the U.S. Department of Homeland Security through the National Security Council.

“We know that to save life and limb, you need to stop the bleeding very early—within five to 10 minutes—or victims can lose their lives,” said ACS Regent Lenworth M. Jacobs, Jr., MD, MPH, FACS, Chair of the Hartford Consensus and director of the Trauma Institute at Hartford Hospital, CT. “However, until now, there has been no clear indication of how well trained the general public is in bleeding control and how willing they might be to participate as immediate responders until professionals arrive on the scene.”

Public ready and willing to act

Langer Research Associates, New York, NY, conducted a national telephone survey of the general public, November 6−11, 2015, concluding just two days before the terrorist attacks in Paris. A total of 1,051 telephone interviews were conducted—528 via cellphone and 523 via landline. Respondents were asked whether they had ever participated in first aid training, and, if so, when and whether it included bleeding control instruction. Nearly half of all respondents (47 percent) said that they had received first aid training at some point. Of that number, 13 percent had trained in first aid in the last two years and 52 percent had first aid training in the last five years.

Respondents also were asked about their willingness to provide aid to bleeding victims in two different scenarios: a car crash and a mass shooting.

Within the context of the two scenarios, the study authors reported that:

Of the 941 respondents able to provide first aid, 98 percent indicated they would be “very likely” or “somewhat likely” to attempt bleeding control on a family member with a leg wound. Within this subgroup, 62 percent indicated they would apply pressure or compression to the wound, 36 percent would apply a tourniquet, 6 percent would cover or wrap the wound in a bandage, and 2 percent would elevate the injured leg.

When presented with a scenario of trying to stop severe bleeding in a car crash victim who is unknown to them, 92 percent of a random half sample of respondents indicated they would be very likely (61 percent) or somewhat likely (31 percent) to act.

In a mass shooting scenario, 75 percent of the other random half sample responded that they would attempt to give first aid if it seemed safe to act, 16 percent responded that they would wait to see what happens, and 8 percent said they would leave the area. In terms of assisting if the situation seemed safe, 94 percent responded that they would be very likely (62 percent) or somewhat likely (32 percent) to try to help a stranger.

Many respondents reported having major or some concern about several issues related to trying to stop severe bleeding in someone whom they did not know. Specifically, respondents expressed concern about seeing someone bleeding heavily (30 percent), becoming contaminated with a disease (61 percent), endangering personal safety (43 percent), causing a victim additional pain or injury (65 percent), and being responsible for a bad outcome (61 percent). Within the context of rendering assistance in the shooting scenario, 71 percent expressed concern about “putting themselves in physical danger from additional violence.”

 

 

Respondents also were asked about their interest in taking a bleeding control class and their support for requiring bleeding control kits in public places. Among the respondents who were physically able to provide first aid, 82 percent said they would be “very interested” or “somewhat interested” in attending a two-hour bleeding control course.

In addition, 93 percent supported the public placement of bleeding control kits (containing gloves, tourniquets, and compression dressings).

The authors also noted strong public approval (91 percent of all surveyed) for training and equipping police officers for severe bleeding control to act as soon as possible before the arrival of EMS personnel, with 65 percent also supporting “faster access of EMS to victims in areas that may not be totally secure.”

“It takes internal fortitude to want to get involved as an immediate responder. We were overwhelmed to learn that the public is prepared to accept this responsibility,” Dr. Jacobs said. “Moving forward, we plan to use these new insights to develop a training program for the public, not just health care professionals, so civilians can learn how to act as immediate responders. We want to steer interested people toward getting the right training and to understand when victims are experiencing the signs of massive bleeding so they can ‘stop the bleed’ and save lives.”

References

Author and Disclosure Information

Publications
ACS Surgery News
MDedge Surgery
Sections
News From the College
Author and Disclosure Information

Author and Disclosure Information

Many civilians have expressed interest in taking a bleeding control training course that would empower them to immediately assist victims of active shooter and other intentional mass casualty events at the point of wounding, according to the results of a national poll published in the Journal of the American College of Surgeons (JACS). Furthermore, most civilians support training and equipping police officers to perform severe bleeding control on victims as soon as possible, rather than wait for emergency medical services (EMS) personnel to arrive on the scene. Survey respondents also supported the placement of bleeding control kits in public places where large crowds gather, similar to the way that automatic external defibrillators are now found in airports and shopping malls.

Working to save lives

The Joint Committee to Create a National Policy to Enhance Survivability from Intentional Mass Casualty and Active Shooter Events, convened by the American College of Surgeons, recommends careful consideration of these study results. The committee’s deliberations are known as the Hartford Consensus™. The Hartford Consensus reports have been published in the Bulletin and JACS since the group’s formation in 2013 and promote the group’s core principle that “no one should die from uncontrolled bleeding.”

To that end, the Hartford Consensus calls for providing law enforcement officers with the training and equipment needed to act before EMS personnel arrive, providing EMS professionals with quicker access to the wounded, and training civilian bystanders to act as immediate responders. This element from the Hartford Consensus is at the heart of the “Stop the Bleed” campaign launched by the U.S. Department of Homeland Security through the National Security Council.

“We know that to save life and limb, you need to stop the bleeding very early—within five to 10 minutes—or victims can lose their lives,” said ACS Regent Lenworth M. Jacobs, Jr., MD, MPH, FACS, Chair of the Hartford Consensus and director of the Trauma Institute at Hartford Hospital, CT. “However, until now, there has been no clear indication of how well trained the general public is in bleeding control and how willing they might be to participate as immediate responders until professionals arrive on the scene.”

Public ready and willing to act

Langer Research Associates, New York, NY, conducted a national telephone survey of the general public, November 6−11, 2015, concluding just two days before the terrorist attacks in Paris. A total of 1,051 telephone interviews were conducted—528 via cellphone and 523 via landline. Respondents were asked whether they had ever participated in first aid training, and, if so, when and whether it included bleeding control instruction. Nearly half of all respondents (47 percent) said that they had received first aid training at some point. Of that number, 13 percent had trained in first aid in the last two years and 52 percent had first aid training in the last five years.

Respondents also were asked about their willingness to provide aid to bleeding victims in two different scenarios: a car crash and a mass shooting.

Within the context of the two scenarios, the study authors reported that:

Of the 941 respondents able to provide first aid, 98 percent indicated they would be “very likely” or “somewhat likely” to attempt bleeding control on a family member with a leg wound. Within this subgroup, 62 percent indicated they would apply pressure or compression to the wound, 36 percent would apply a tourniquet, 6 percent would cover or wrap the wound in a bandage, and 2 percent would elevate the injured leg.

When presented with a scenario of trying to stop severe bleeding in a car crash victim who is unknown to them, 92 percent of a random half sample of respondents indicated they would be very likely (61 percent) or somewhat likely (31 percent) to act.

In a mass shooting scenario, 75 percent of the other random half sample responded that they would attempt to give first aid if it seemed safe to act, 16 percent responded that they would wait to see what happens, and 8 percent said they would leave the area. In terms of assisting if the situation seemed safe, 94 percent responded that they would be very likely (62 percent) or somewhat likely (32 percent) to try to help a stranger.

Many respondents reported having major or some concern about several issues related to trying to stop severe bleeding in someone whom they did not know. Specifically, respondents expressed concern about seeing someone bleeding heavily (30 percent), becoming contaminated with a disease (61 percent), endangering personal safety (43 percent), causing a victim additional pain or injury (65 percent), and being responsible for a bad outcome (61 percent). Within the context of rendering assistance in the shooting scenario, 71 percent expressed concern about “putting themselves in physical danger from additional violence.”

 

 

Respondents also were asked about their interest in taking a bleeding control class and their support for requiring bleeding control kits in public places. Among the respondents who were physically able to provide first aid, 82 percent said they would be “very interested” or “somewhat interested” in attending a two-hour bleeding control course.

In addition, 93 percent supported the public placement of bleeding control kits (containing gloves, tourniquets, and compression dressings).

The authors also noted strong public approval (91 percent of all surveyed) for training and equipping police officers for severe bleeding control to act as soon as possible before the arrival of EMS personnel, with 65 percent also supporting “faster access of EMS to victims in areas that may not be totally secure.”

“It takes internal fortitude to want to get involved as an immediate responder. We were overwhelmed to learn that the public is prepared to accept this responsibility,” Dr. Jacobs said. “Moving forward, we plan to use these new insights to develop a training program for the public, not just health care professionals, so civilians can learn how to act as immediate responders. We want to steer interested people toward getting the right training and to understand when victims are experiencing the signs of massive bleeding so they can ‘stop the bleed’ and save lives.”

Many civilians have expressed interest in taking a bleeding control training course that would empower them to immediately assist victims of active shooter and other intentional mass casualty events at the point of wounding, according to the results of a national poll published in the Journal of the American College of Surgeons (JACS). Furthermore, most civilians support training and equipping police officers to perform severe bleeding control on victims as soon as possible, rather than wait for emergency medical services (EMS) personnel to arrive on the scene. Survey respondents also supported the placement of bleeding control kits in public places where large crowds gather, similar to the way that automatic external defibrillators are now found in airports and shopping malls.

Working to save lives

The Joint Committee to Create a National Policy to Enhance Survivability from Intentional Mass Casualty and Active Shooter Events, convened by the American College of Surgeons, recommends careful consideration of these study results. The committee’s deliberations are known as the Hartford Consensus™. The Hartford Consensus reports have been published in the Bulletin and JACS since the group’s formation in 2013 and promote the group’s core principle that “no one should die from uncontrolled bleeding.”

To that end, the Hartford Consensus calls for providing law enforcement officers with the training and equipment needed to act before EMS personnel arrive, providing EMS professionals with quicker access to the wounded, and training civilian bystanders to act as immediate responders. This element from the Hartford Consensus is at the heart of the “Stop the Bleed” campaign launched by the U.S. Department of Homeland Security through the National Security Council.

“We know that to save life and limb, you need to stop the bleeding very early—within five to 10 minutes—or victims can lose their lives,” said ACS Regent Lenworth M. Jacobs, Jr., MD, MPH, FACS, Chair of the Hartford Consensus and director of the Trauma Institute at Hartford Hospital, CT. “However, until now, there has been no clear indication of how well trained the general public is in bleeding control and how willing they might be to participate as immediate responders until professionals arrive on the scene.”

Public ready and willing to act

Langer Research Associates, New York, NY, conducted a national telephone survey of the general public, November 6−11, 2015, concluding just two days before the terrorist attacks in Paris. A total of 1,051 telephone interviews were conducted—528 via cellphone and 523 via landline. Respondents were asked whether they had ever participated in first aid training, and, if so, when and whether it included bleeding control instruction. Nearly half of all respondents (47 percent) said that they had received first aid training at some point. Of that number, 13 percent had trained in first aid in the last two years and 52 percent had first aid training in the last five years.

Respondents also were asked about their willingness to provide aid to bleeding victims in two different scenarios: a car crash and a mass shooting.

Within the context of the two scenarios, the study authors reported that:

Of the 941 respondents able to provide first aid, 98 percent indicated they would be “very likely” or “somewhat likely” to attempt bleeding control on a family member with a leg wound. Within this subgroup, 62 percent indicated they would apply pressure or compression to the wound, 36 percent would apply a tourniquet, 6 percent would cover or wrap the wound in a bandage, and 2 percent would elevate the injured leg.

When presented with a scenario of trying to stop severe bleeding in a car crash victim who is unknown to them, 92 percent of a random half sample of respondents indicated they would be very likely (61 percent) or somewhat likely (31 percent) to act.

In a mass shooting scenario, 75 percent of the other random half sample responded that they would attempt to give first aid if it seemed safe to act, 16 percent responded that they would wait to see what happens, and 8 percent said they would leave the area. In terms of assisting if the situation seemed safe, 94 percent responded that they would be very likely (62 percent) or somewhat likely (32 percent) to try to help a stranger.

Many respondents reported having major or some concern about several issues related to trying to stop severe bleeding in someone whom they did not know. Specifically, respondents expressed concern about seeing someone bleeding heavily (30 percent), becoming contaminated with a disease (61 percent), endangering personal safety (43 percent), causing a victim additional pain or injury (65 percent), and being responsible for a bad outcome (61 percent). Within the context of rendering assistance in the shooting scenario, 71 percent expressed concern about “putting themselves in physical danger from additional violence.”

 

 

Respondents also were asked about their interest in taking a bleeding control class and their support for requiring bleeding control kits in public places. Among the respondents who were physically able to provide first aid, 82 percent said they would be “very interested” or “somewhat interested” in attending a two-hour bleeding control course.

In addition, 93 percent supported the public placement of bleeding control kits (containing gloves, tourniquets, and compression dressings).

The authors also noted strong public approval (91 percent of all surveyed) for training and equipping police officers for severe bleeding control to act as soon as possible before the arrival of EMS personnel, with 65 percent also supporting “faster access of EMS to victims in areas that may not be totally secure.”

“It takes internal fortitude to want to get involved as an immediate responder. We were overwhelmed to learn that the public is prepared to accept this responsibility,” Dr. Jacobs said. “Moving forward, we plan to use these new insights to develop a training program for the public, not just health care professionals, so civilians can learn how to act as immediate responders. We want to steer interested people toward getting the right training and to understand when victims are experiencing the signs of massive bleeding so they can ‘stop the bleed’ and save lives.”

References

References

Publications
ACS Surgery News
MDedge Surgery
Publications
ACS Surgery News
MDedge Surgery
Article Type
News
Display Headline
Survey: Civilians support wider access to education on how to help victims of mass casualty events
Display Headline
Survey: Civilians support wider access to education on how to help victims of mass casualty events
Sections
News From the College
Article Source

PURLs Copyright

Inside the Article

Surgeons Voice Legislative Priorities at Advocacy Summit 2016

Article Type
News
Changed
Wed, 01/02/2019 - 09:33
Display Headline
Surgeons Voice Legislative Priorities at Advocacy Summit 2016

Approximately 300 surgeons and surgical residents participated in the advocacy portion of the 2016 American College of Surgeons (ACS) Leadership & Advocacy Summit. The event provided participants with an opportunity to develop their advocacy skills, learn about legislative and health policy priorities, and advocate in meetings with members of Congress and their staffs.

Surgeons asked lawmakers to use their oversight authority to encourage the Centers for Medicare & Medicaid Services to adopt meaningful quality measures, and physician-developed Alternative Payment Models. ACS members also asked their elected officials to support the Responsible Data Transparency Act, legislation that is being developed by Rep. Bill Flores (R-Tex.). The College is committed to maintaining transparency in the Medicare system to promote high-quality patient care. At issue, however, are third-party groups that are evading established, accurate, valid, and transparent pathways to sensitive Medicare data by using Freedom of Information Act requests to obtain raw physician claims data. This legislation would prevent groups from using questionable, non–risk-adjusted methodologies to conduct performance analyses and publish potentially misleading physician performance ratings on public websites.

Other issues discussed at the Capitol Hill meetings include promotion of the Ensuring Access to General Surgery Act of 2016, legislation being developed that would require that a study be conducted to designate general surgery Health Professional Shortage Areas (HPSAs); cancer-related concerns, including education on the importance of Commission on Cancer accreditation; and improved access to trauma care. Details about the ACS Leadership & Advocacy Summit will be published in the May SurgeonsVoice Monthly and the July issue of the Bulletin at http://bulletin.facs.org/.

References

Author and Disclosure Information

Publications
ACS Surgery News
Thoracic Surgery News (Archived)
Vascular Specialist
MDedge Surgery
Sections
News From the College
Author and Disclosure Information

Author and Disclosure Information

Approximately 300 surgeons and surgical residents participated in the advocacy portion of the 2016 American College of Surgeons (ACS) Leadership & Advocacy Summit. The event provided participants with an opportunity to develop their advocacy skills, learn about legislative and health policy priorities, and advocate in meetings with members of Congress and their staffs.

Surgeons asked lawmakers to use their oversight authority to encourage the Centers for Medicare & Medicaid Services to adopt meaningful quality measures, and physician-developed Alternative Payment Models. ACS members also asked their elected officials to support the Responsible Data Transparency Act, legislation that is being developed by Rep. Bill Flores (R-Tex.). The College is committed to maintaining transparency in the Medicare system to promote high-quality patient care. At issue, however, are third-party groups that are evading established, accurate, valid, and transparent pathways to sensitive Medicare data by using Freedom of Information Act requests to obtain raw physician claims data. This legislation would prevent groups from using questionable, non–risk-adjusted methodologies to conduct performance analyses and publish potentially misleading physician performance ratings on public websites.

Other issues discussed at the Capitol Hill meetings include promotion of the Ensuring Access to General Surgery Act of 2016, legislation being developed that would require that a study be conducted to designate general surgery Health Professional Shortage Areas (HPSAs); cancer-related concerns, including education on the importance of Commission on Cancer accreditation; and improved access to trauma care. Details about the ACS Leadership & Advocacy Summit will be published in the May SurgeonsVoice Monthly and the July issue of the Bulletin at http://bulletin.facs.org/.

Approximately 300 surgeons and surgical residents participated in the advocacy portion of the 2016 American College of Surgeons (ACS) Leadership & Advocacy Summit. The event provided participants with an opportunity to develop their advocacy skills, learn about legislative and health policy priorities, and advocate in meetings with members of Congress and their staffs.

Surgeons asked lawmakers to use their oversight authority to encourage the Centers for Medicare & Medicaid Services to adopt meaningful quality measures, and physician-developed Alternative Payment Models. ACS members also asked their elected officials to support the Responsible Data Transparency Act, legislation that is being developed by Rep. Bill Flores (R-Tex.). The College is committed to maintaining transparency in the Medicare system to promote high-quality patient care. At issue, however, are third-party groups that are evading established, accurate, valid, and transparent pathways to sensitive Medicare data by using Freedom of Information Act requests to obtain raw physician claims data. This legislation would prevent groups from using questionable, non–risk-adjusted methodologies to conduct performance analyses and publish potentially misleading physician performance ratings on public websites.

Other issues discussed at the Capitol Hill meetings include promotion of the Ensuring Access to General Surgery Act of 2016, legislation being developed that would require that a study be conducted to designate general surgery Health Professional Shortage Areas (HPSAs); cancer-related concerns, including education on the importance of Commission on Cancer accreditation; and improved access to trauma care. Details about the ACS Leadership & Advocacy Summit will be published in the May SurgeonsVoice Monthly and the July issue of the Bulletin at http://bulletin.facs.org/.

References

References

Publications
ACS Surgery News
Thoracic Surgery News (Archived)
Vascular Specialist
MDedge Surgery
Publications
ACS Surgery News
Thoracic Surgery News (Archived)
Vascular Specialist
MDedge Surgery
Article Type
News
Display Headline
Surgeons Voice Legislative Priorities at Advocacy Summit 2016
Display Headline
Surgeons Voice Legislative Priorities at Advocacy Summit 2016
Sections
News From the College
Article Source

PURLs Copyright

Inside the Article

No benefit from added trabectedin for STS patients

Article Type
News
Changed
Fri, 01/04/2019 - 13:15
Display Headline
No benefit from added trabectedin for STS patients
Author(s)
Jessica Craig

When trabectedin was administered to soft tissue sarcoma (STS) patients receiving doxorubicin, neither progression-free nor overall survival significantly improved, investigators found.

In addition, patients who received both drugs were significantly more likely to experience adverse events.

“The combination of trabectedin plus doxorubicin did not show superiority over doxorubicin alone as first-line treatment of advanced STS patients, at least under this schedule. Moreover, the experimental arm was significantly more toxic than the control arm, especially regarding thrombocytopenia, vomiting, liver toxicity, and asthenia,” wrote Dr. Javier Martin-Broto of the Virgen del Rocio Hospital and Biomedicine Institute, Seville (Spain) and his associates (J Clin Oncol. 2016. doi: 10.1200/JCO.2015.65.3329).

Of 115 adult patients with advanced nonresectable or metastatic STS, 59 received doxorubicin only, 55 received both doxorubicin and trabectedin, and one patient was not treated. In the experimental group, doxorubicin was administered before trabectedin. Both the experimental and control groups underwent six cycles of their respective drug regime unless disease progression or unacceptable toxicity was observed.

A Cox proportional hazard regression model revealed that the progression-free survival was not significantly higher among patients receiving trabectedin and doxorubicin compared to patients only receiving doxorubicin (5.7 months vs. 5.5 months; hazard ratio, 1.16; 95% confidence interval, 0.79-1.71; P = .45). Overall survival was also not significantly different between the groups (13.3 months vs. 13.7 months; HR, 1.21; 95% CI, .77-1.92, P = .41).

However, compared with patients who only received doxorubicin, patients who received both trabectedin and doxorubicin experienced significantly more adverse events such as grade 3 or 4 thrombocytopenia (2% vs. 18%, P = .016), grade 3 or 4 liver toxicity (12% vs. 29%, P = .002), AST (0% vs. 8%, P = .007), ALT (0% vs. 19%, P less than .001), and grade 3 or 4 asthenia (4% vs. 25%, P = .002).

[email protected]

On Twitter @jess_craig94

References

Author and Disclosure Information

Publications
Oncology Practice
MDedge Hematology and Oncology
Topics
Sarcoma & GIST
Author(s)
Jessica Craig
Author(s)
Jessica Craig
Author and Disclosure Information

Author and Disclosure Information

When trabectedin was administered to soft tissue sarcoma (STS) patients receiving doxorubicin, neither progression-free nor overall survival significantly improved, investigators found.

In addition, patients who received both drugs were significantly more likely to experience adverse events.

“The combination of trabectedin plus doxorubicin did not show superiority over doxorubicin alone as first-line treatment of advanced STS patients, at least under this schedule. Moreover, the experimental arm was significantly more toxic than the control arm, especially regarding thrombocytopenia, vomiting, liver toxicity, and asthenia,” wrote Dr. Javier Martin-Broto of the Virgen del Rocio Hospital and Biomedicine Institute, Seville (Spain) and his associates (J Clin Oncol. 2016. doi: 10.1200/JCO.2015.65.3329).

Of 115 adult patients with advanced nonresectable or metastatic STS, 59 received doxorubicin only, 55 received both doxorubicin and trabectedin, and one patient was not treated. In the experimental group, doxorubicin was administered before trabectedin. Both the experimental and control groups underwent six cycles of their respective drug regime unless disease progression or unacceptable toxicity was observed.

A Cox proportional hazard regression model revealed that the progression-free survival was not significantly higher among patients receiving trabectedin and doxorubicin compared to patients only receiving doxorubicin (5.7 months vs. 5.5 months; hazard ratio, 1.16; 95% confidence interval, 0.79-1.71; P = .45). Overall survival was also not significantly different between the groups (13.3 months vs. 13.7 months; HR, 1.21; 95% CI, .77-1.92, P = .41).

However, compared with patients who only received doxorubicin, patients who received both trabectedin and doxorubicin experienced significantly more adverse events such as grade 3 or 4 thrombocytopenia (2% vs. 18%, P = .016), grade 3 or 4 liver toxicity (12% vs. 29%, P = .002), AST (0% vs. 8%, P = .007), ALT (0% vs. 19%, P less than .001), and grade 3 or 4 asthenia (4% vs. 25%, P = .002).

[email protected]

On Twitter @jess_craig94

When trabectedin was administered to soft tissue sarcoma (STS) patients receiving doxorubicin, neither progression-free nor overall survival significantly improved, investigators found.

In addition, patients who received both drugs were significantly more likely to experience adverse events.

“The combination of trabectedin plus doxorubicin did not show superiority over doxorubicin alone as first-line treatment of advanced STS patients, at least under this schedule. Moreover, the experimental arm was significantly more toxic than the control arm, especially regarding thrombocytopenia, vomiting, liver toxicity, and asthenia,” wrote Dr. Javier Martin-Broto of the Virgen del Rocio Hospital and Biomedicine Institute, Seville (Spain) and his associates (J Clin Oncol. 2016. doi: 10.1200/JCO.2015.65.3329).

Of 115 adult patients with advanced nonresectable or metastatic STS, 59 received doxorubicin only, 55 received both doxorubicin and trabectedin, and one patient was not treated. In the experimental group, doxorubicin was administered before trabectedin. Both the experimental and control groups underwent six cycles of their respective drug regime unless disease progression or unacceptable toxicity was observed.

A Cox proportional hazard regression model revealed that the progression-free survival was not significantly higher among patients receiving trabectedin and doxorubicin compared to patients only receiving doxorubicin (5.7 months vs. 5.5 months; hazard ratio, 1.16; 95% confidence interval, 0.79-1.71; P = .45). Overall survival was also not significantly different between the groups (13.3 months vs. 13.7 months; HR, 1.21; 95% CI, .77-1.92, P = .41).

However, compared with patients who only received doxorubicin, patients who received both trabectedin and doxorubicin experienced significantly more adverse events such as grade 3 or 4 thrombocytopenia (2% vs. 18%, P = .016), grade 3 or 4 liver toxicity (12% vs. 29%, P = .002), AST (0% vs. 8%, P = .007), ALT (0% vs. 19%, P less than .001), and grade 3 or 4 asthenia (4% vs. 25%, P = .002).

[email protected]

On Twitter @jess_craig94

References

References

Publications
Oncology Practice
MDedge Hematology and Oncology
Publications
Oncology Practice
MDedge Hematology and Oncology
Topics
Sarcoma & GIST
Article Type
News
Display Headline
No benefit from added trabectedin for STS patients
Display Headline
No benefit from added trabectedin for STS patients
Article Source

FROM JOURNAL OF CLINICAL ONCOLOGY

PURLs Copyright

Inside the Article

Vitals

Key clinical point: When trabectedin was administered to soft tissue sarcoma (STS) patients receiving doxorubicin, neither progression-free nor overall survival significantly improved. However, patients who received both drugs were significantly more likely to experience adverse events.

Major finding: Progression-free survival was not significantly higher among patients receiving trabectedin and doxorubicin, compared with patients only receiving doxorubicin (5.7 months vs. 5.5 months; HR, 1.16; 95% CI, 0.79-1.71; P = .45). Compared with patients who only received doxorubicin, patients who received both drugs experienced significantly more adverse events such as thrombocytopenia, liver toxicity, AST, ALT, and asthenia (all P values less than .05).

Data source: Randomized phase II trial of 115 adult patients with advanced nonresectable soft tissue sarcoma.

Disclosures: This study was supported by the Spanish Group for Research on Sarcoma. Ten investigators reported serving in advisory roles or receiving financial compensation or honoraria from several companies. The other 14 investigators reported having no disclosures.

MRI Results May Help Pinpoint PNEEs

Article Type
News
Changed
Thu, 12/15/2022 - 16:02
Display Headline
MRI Results May Help Pinpoint PNEEs
Psychogenic nonepileptic events generate more brain MRI abnormalities, and their location may differentiate PNEEs from epilepsy.

A recent study suggests that brain MRI abnormalities are more common in patients with psychogenic nonepileptic events, when compared to the findings in normal persons. When investigators analyzed MRI data from 339 patients discharged from their epilepsy monitoring units, they found brain MRI abnormalities in 33.8% of patients with PNEEs and 57.7% in patients with epilepsy, much higher than would be found in a normal population.  The researchers also discovered that the brain MRI anomalies during epileptic seizures were more likely to occur in the temporal region of the brain, while PNEE anomalies were more frequently multifocal. 

Bolen RD, Koontz EH, Pritchard PB. Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events. Epilepsy Behav. 2016;59:73-76. 

Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Topics
Epilepsy & Seizures
Sections
Literature Review
Literature Monitor
Psychogenic nonepileptic events generate more brain MRI abnormalities, and their location may differentiate PNEEs from epilepsy.
Psychogenic nonepileptic events generate more brain MRI abnormalities, and their location may differentiate PNEEs from epilepsy.

A recent study suggests that brain MRI abnormalities are more common in patients with psychogenic nonepileptic events, when compared to the findings in normal persons. When investigators analyzed MRI data from 339 patients discharged from their epilepsy monitoring units, they found brain MRI abnormalities in 33.8% of patients with PNEEs and 57.7% in patients with epilepsy, much higher than would be found in a normal population.  The researchers also discovered that the brain MRI anomalies during epileptic seizures were more likely to occur in the temporal region of the brain, while PNEE anomalies were more frequently multifocal. 

Bolen RD, Koontz EH, Pritchard PB. Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events. Epilepsy Behav. 2016;59:73-76. 

A recent study suggests that brain MRI abnormalities are more common in patients with psychogenic nonepileptic events, when compared to the findings in normal persons. When investigators analyzed MRI data from 339 patients discharged from their epilepsy monitoring units, they found brain MRI abnormalities in 33.8% of patients with PNEEs and 57.7% in patients with epilepsy, much higher than would be found in a normal population.  The researchers also discovered that the brain MRI anomalies during epileptic seizures were more likely to occur in the temporal region of the brain, while PNEE anomalies were more frequently multifocal. 

Bolen RD, Koontz EH, Pritchard PB. Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events. Epilepsy Behav. 2016;59:73-76. 

Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Topics
Epilepsy & Seizures
Article Type
News
Display Headline
MRI Results May Help Pinpoint PNEEs
Display Headline
MRI Results May Help Pinpoint PNEEs
Sections
Literature Review
Literature Monitor
Disallow All Ads
Alternative CME

Texting on a Smartphone Generates Unique EEG Readings

Article Type
News
Changed
Thu, 12/15/2022 - 16:02
Display Headline
Texting on a Smartphone Generates Unique EEG Readings
One in five patients displayed a “reproducible texting rhythm” on scalp EEGs.

Using a smartphone or other personal electronic device (PED) to send text messages produces a “reproducible texting rhythm” that can be detected during video-EEG monitoring, according to Mayo Clinic researchers. In a cohort of 129 patients, this texting rhythm was detected in 27 (20.9%) patients. The rhythm existed in 28% of patients with epilepsy and 16% of those with non-epileptic seizures. The unique pattern was not present in patients when they performed independent tasks or when using a cellphone to make audio calls. The investigators concluded that the reproducible text rhythm “represents a novel technology-specific neurophysiological alteration of brain networks” and proposed that “cortical processing in the contemporary brain is uniquely activated by the use of PEDs.”  

Tatum WO, DiCiaccio B, Yelvington KH. Cortical processing during smartphone text messaging. Epilepsy Behav. 2016;59:117-121. 

Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Topics
Epilepsy & Seizures
Sections
Literature Review
Literature Monitor
One in five patients displayed a “reproducible texting rhythm” on scalp EEGs.
One in five patients displayed a “reproducible texting rhythm” on scalp EEGs.

Using a smartphone or other personal electronic device (PED) to send text messages produces a “reproducible texting rhythm” that can be detected during video-EEG monitoring, according to Mayo Clinic researchers. In a cohort of 129 patients, this texting rhythm was detected in 27 (20.9%) patients. The rhythm existed in 28% of patients with epilepsy and 16% of those with non-epileptic seizures. The unique pattern was not present in patients when they performed independent tasks or when using a cellphone to make audio calls. The investigators concluded that the reproducible text rhythm “represents a novel technology-specific neurophysiological alteration of brain networks” and proposed that “cortical processing in the contemporary brain is uniquely activated by the use of PEDs.”  

Tatum WO, DiCiaccio B, Yelvington KH. Cortical processing during smartphone text messaging. Epilepsy Behav. 2016;59:117-121. 

Using a smartphone or other personal electronic device (PED) to send text messages produces a “reproducible texting rhythm” that can be detected during video-EEG monitoring, according to Mayo Clinic researchers. In a cohort of 129 patients, this texting rhythm was detected in 27 (20.9%) patients. The rhythm existed in 28% of patients with epilepsy and 16% of those with non-epileptic seizures. The unique pattern was not present in patients when they performed independent tasks or when using a cellphone to make audio calls. The investigators concluded that the reproducible text rhythm “represents a novel technology-specific neurophysiological alteration of brain networks” and proposed that “cortical processing in the contemporary brain is uniquely activated by the use of PEDs.”  

Tatum WO, DiCiaccio B, Yelvington KH. Cortical processing during smartphone text messaging. Epilepsy Behav. 2016;59:117-121. 

Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Topics
Epilepsy & Seizures
Article Type
News
Display Headline
Texting on a Smartphone Generates Unique EEG Readings
Display Headline
Texting on a Smartphone Generates Unique EEG Readings
Sections
Literature Review
Literature Monitor
Disallow All Ads
Alternative CME

Most Women With Epilepsy Seem to Favor Effective Contraceptive Methods

Article Type
News
Changed
Thu, 12/15/2022 - 16:02
Display Headline
Most Women With Epilepsy Seem to Favor Effective Contraceptive Methods
More than two thirds of patients use a method generally considered safe and effective but there’s no firm evidence to establish efficacy in this special population.

A cross-sectional data analysis derived from the Epilepsy Birth Control Registry recently found that among nearly 800 patients who were at risk for unintended pregnancy, 69.7% were using effective contraceptive methods, which included hormonal contraceptives, intrauterine devices, tubal ligation, and vasectomy. Despite the high number of patients with epilepsy using what are generally considered highly effective forms of birth control, the efficacy of these methods in this population "remains to be proven" according to researchers from Columbia University and Beth Israel Deaconess Medical Center. The analysis suggests that there is a need for evidence-based guidelines that demonstrate the efficacy and safety of various contraceptive methods in this special population.

Herzog AG, Mandle HB, Cahill KE, Fowler KM, Hauser WA, Davis AR. Contraceptive practices of women with epilepsy: Findings of the epilepsy birth control registry. Epilepsia. 2016;57(4):630-637. 

Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Topics
Epilepsy & Seizures
Sections
Literature Review
Literature Monitor
More than two thirds of patients use a method generally considered safe and effective but there’s no firm evidence to establish efficacy in this special population.
More than two thirds of patients use a method generally considered safe and effective but there’s no firm evidence to establish efficacy in this special population.

A cross-sectional data analysis derived from the Epilepsy Birth Control Registry recently found that among nearly 800 patients who were at risk for unintended pregnancy, 69.7% were using effective contraceptive methods, which included hormonal contraceptives, intrauterine devices, tubal ligation, and vasectomy. Despite the high number of patients with epilepsy using what are generally considered highly effective forms of birth control, the efficacy of these methods in this population "remains to be proven" according to researchers from Columbia University and Beth Israel Deaconess Medical Center. The analysis suggests that there is a need for evidence-based guidelines that demonstrate the efficacy and safety of various contraceptive methods in this special population.

Herzog AG, Mandle HB, Cahill KE, Fowler KM, Hauser WA, Davis AR. Contraceptive practices of women with epilepsy: Findings of the epilepsy birth control registry. Epilepsia. 2016;57(4):630-637. 

A cross-sectional data analysis derived from the Epilepsy Birth Control Registry recently found that among nearly 800 patients who were at risk for unintended pregnancy, 69.7% were using effective contraceptive methods, which included hormonal contraceptives, intrauterine devices, tubal ligation, and vasectomy. Despite the high number of patients with epilepsy using what are generally considered highly effective forms of birth control, the efficacy of these methods in this population "remains to be proven" according to researchers from Columbia University and Beth Israel Deaconess Medical Center. The analysis suggests that there is a need for evidence-based guidelines that demonstrate the efficacy and safety of various contraceptive methods in this special population.

Herzog AG, Mandle HB, Cahill KE, Fowler KM, Hauser WA, Davis AR. Contraceptive practices of women with epilepsy: Findings of the epilepsy birth control registry. Epilepsia. 2016;57(4):630-637. 

Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Publications
Epilepsy Resource Center
B-Cell Lymphoma ICYMI
Breast Cancer ICYMI
Topics
Epilepsy & Seizures
Article Type
News
Display Headline
Most Women With Epilepsy Seem to Favor Effective Contraceptive Methods
Display Headline
Most Women With Epilepsy Seem to Favor Effective Contraceptive Methods
Sections
Literature Review
Literature Monitor
Disallow All Ads
Alternative CME

FDA approves lenvatinib for advanced renal cell carcinoma

Article Type
News
Changed
Fri, 01/04/2019 - 13:15
Display Headline
FDA approves lenvatinib for advanced renal cell carcinoma
Author(s)
Jessica Craig

The Food and Drug Administration has approved lenvatinib capsules, in combination with everolimus, for the treatment of patients with advanced renal cell carcinoma following one prior antiangiogenic therapy.

Approval was based on prolonged progression-free survival (PFS) in a randomized, phase II, open-label multicenter clinical trial of 153 patients, the FDA said in a written statement.

 

Patients who received lenvatinib plus everolimus (n = 51) had significantly prolonged PFS, compared with patients who received only everolimus (n = 50) (14.6 months vs. 5.5 months; HR, 0.40; 95% CI, 0.24-0.68; P = .0005) but not compared with patients who received lenvatinib alone (n = 52) (7.4 months; HR, 0.66; 95% CI, 0.30-1.10; P = .12). Of patients receiving the combination of drugs, 71% experienced adverse events, compared with 79% of patients receiving lenvatinib alone and 50% of patients receiving everolimus alone. The most common treatment-related adverse events reported included diarrhea, decreased appetite, and severe fatigue.

The FDA previously granted lenvatinib, marketed as Lenvima by Eisai, a breakthrough therapy designation and priority review.

“This is the only combination regimen to significantly prolong progression-free survival … when compared with a standard of care in patients with advanced renal cell carcinoma,” representatives from Eisai said in a written statement.

Lenvatinib was previously approved for the treatment of recurrent, progressive, radioactive iodine-refractory differentiated thyroid cancer in early 2015. However, in April 2016, the FDA released a safety warning about lenvatinib capsules for oral use.

“Serious tumor-related bleeds, including fatal hemorrhagic events in Lenvima-treated patients, have occurred in clinical trials and been reported in postmarketing experience,” reported the FDA in a written statement.

The recommended lenvatinib dosage for renal cell carcinoma patients is 18 mg/day, lower than the 24 mg/day that was recommended to thyroid cancer patients prior to the FDA’s warning.

[email protected]

On Twitter @JessCraig_OP

Publications
Oncology Practice
MDedge Hematology and Oncology
Topics
Genitourinary Cancer
Renal Cell Carcinoma
Author(s)
Jessica Craig
Author(s)
Jessica Craig

The Food and Drug Administration has approved lenvatinib capsules, in combination with everolimus, for the treatment of patients with advanced renal cell carcinoma following one prior antiangiogenic therapy.

Approval was based on prolonged progression-free survival (PFS) in a randomized, phase II, open-label multicenter clinical trial of 153 patients, the FDA said in a written statement.

 

Patients who received lenvatinib plus everolimus (n = 51) had significantly prolonged PFS, compared with patients who received only everolimus (n = 50) (14.6 months vs. 5.5 months; HR, 0.40; 95% CI, 0.24-0.68; P = .0005) but not compared with patients who received lenvatinib alone (n = 52) (7.4 months; HR, 0.66; 95% CI, 0.30-1.10; P = .12). Of patients receiving the combination of drugs, 71% experienced adverse events, compared with 79% of patients receiving lenvatinib alone and 50% of patients receiving everolimus alone. The most common treatment-related adverse events reported included diarrhea, decreased appetite, and severe fatigue.

The FDA previously granted lenvatinib, marketed as Lenvima by Eisai, a breakthrough therapy designation and priority review.

“This is the only combination regimen to significantly prolong progression-free survival … when compared with a standard of care in patients with advanced renal cell carcinoma,” representatives from Eisai said in a written statement.

Lenvatinib was previously approved for the treatment of recurrent, progressive, radioactive iodine-refractory differentiated thyroid cancer in early 2015. However, in April 2016, the FDA released a safety warning about lenvatinib capsules for oral use.

“Serious tumor-related bleeds, including fatal hemorrhagic events in Lenvima-treated patients, have occurred in clinical trials and been reported in postmarketing experience,” reported the FDA in a written statement.

The recommended lenvatinib dosage for renal cell carcinoma patients is 18 mg/day, lower than the 24 mg/day that was recommended to thyroid cancer patients prior to the FDA’s warning.

[email protected]

On Twitter @JessCraig_OP

The Food and Drug Administration has approved lenvatinib capsules, in combination with everolimus, for the treatment of patients with advanced renal cell carcinoma following one prior antiangiogenic therapy.

Approval was based on prolonged progression-free survival (PFS) in a randomized, phase II, open-label multicenter clinical trial of 153 patients, the FDA said in a written statement.

 

Patients who received lenvatinib plus everolimus (n = 51) had significantly prolonged PFS, compared with patients who received only everolimus (n = 50) (14.6 months vs. 5.5 months; HR, 0.40; 95% CI, 0.24-0.68; P = .0005) but not compared with patients who received lenvatinib alone (n = 52) (7.4 months; HR, 0.66; 95% CI, 0.30-1.10; P = .12). Of patients receiving the combination of drugs, 71% experienced adverse events, compared with 79% of patients receiving lenvatinib alone and 50% of patients receiving everolimus alone. The most common treatment-related adverse events reported included diarrhea, decreased appetite, and severe fatigue.

The FDA previously granted lenvatinib, marketed as Lenvima by Eisai, a breakthrough therapy designation and priority review.

“This is the only combination regimen to significantly prolong progression-free survival … when compared with a standard of care in patients with advanced renal cell carcinoma,” representatives from Eisai said in a written statement.

Lenvatinib was previously approved for the treatment of recurrent, progressive, radioactive iodine-refractory differentiated thyroid cancer in early 2015. However, in April 2016, the FDA released a safety warning about lenvatinib capsules for oral use.

“Serious tumor-related bleeds, including fatal hemorrhagic events in Lenvima-treated patients, have occurred in clinical trials and been reported in postmarketing experience,” reported the FDA in a written statement.

The recommended lenvatinib dosage for renal cell carcinoma patients is 18 mg/day, lower than the 24 mg/day that was recommended to thyroid cancer patients prior to the FDA’s warning.

[email protected]

On Twitter @JessCraig_OP

Publications
Oncology Practice
MDedge Hematology and Oncology
Publications
Oncology Practice
MDedge Hematology and Oncology
Topics
Genitourinary Cancer
Renal Cell Carcinoma
Article Type
News
Display Headline
FDA approves lenvatinib for advanced renal cell carcinoma
Display Headline
FDA approves lenvatinib for advanced renal cell carcinoma
Disallow All Ads
Teaser Media
Subscribe to