Risk adjustment for patient experience measures needs to be modified by including the CMS Hierarchical Condition Categories (HCC) risk scores of home health beneficiaries.
Objectives: To understand the association between agency-level CMS Hierarchical Condition Categories (HCC) risk scores and patient experience measures for home health.
Study Design: This was a cross-sectional study.
Methods: We extracted variables from the 2014 Medicare Provider Utilization and Payment Data for Home Health Agencies and Home Health Compare file. We applied fixed-effects models for the analyses. Our dependent variables included both global and composite patient experience measures. The 2 global patient experience measures were the patient’s overall rating of care provided by the agency (rating) and the patient’s willingness to recommend the home health agency to others (recommendation). The 3 composite patient experience measures were how often the patient felt the provider gave care in a professional way (professional way), how well the home health team communicated with the patient (communication), and whether the home health team discussed medicines, pain, and home safety with the patient (discussion).
Results: Increased agency-level CMS HCC risk scores were negatively associated with all patient experience measures: rating (—2.04; P ≤.001), recommendation (—2.75; P <.001), professional way (—1.56; P <.001), communication (—1.67; P <.001), and discussion (—1.69; P ≤.001). Several covariates, including the percentage of racial/ethnic minority beneficiaries, ownership of the agency, and number of tenured years with the Medicare program, were significantly associated with patient experience measures.
Conclusions: A negative association exists between CMS HCC risk scores and patient experience measures. To avoid unintended consequences, patient experience measures need further risk adjustment under the CMS 5-star patient survey rating system and the Home Health Value-Based Purchasing pilot program.
Am J Manag Care. 2018;24(10):e319-e324Takeaway Points
Patient experience is a critical quality dimension of healthcare and is also associated with patient outcomes, such as patient safety and readmissions.1-3 In 2010, CMS used the Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) survey to assess patient experience for home health care.4 In 2016, CMS used the results from the HHCAHPS survey to create a 5-star patient survey rating system and made it publicly available.5 Patient experience is also part of quality metrics in the Home Health Value-Based Purchasing (HHVBP) pilot program that financially rewards or penalizes home health agencies in 9 states.6
In the United States, about 12,000 home health agencies deliver care to approximately 3.5 million Medicare beneficiaries at their residences.7 Medicare home health beneficiaries are homebound and often experience polypharmacy.7-9 They have, on average, 4.2 diagnostic conditions and have a higher prevalence of limitations in activities of daily living and cognitive function than general Medicare beneficiaries.10,11 Most of them receive assistance from informal caregivers to meet their medical and daily needs.12 Home health providers not only conduct professional care (eg, physical therapy and wound care) but also educate beneficiaries and their informal caregivers about how to conduct safe care (eg, proper use of medications and oxygen inhalation) and coordinate necessary resources to meet patients’ needs and expectations at home.
Evidence from surveys for health plans, hospitals, community health centers, and primary care physicians shows that several factors affecting patient experience are beyond health providers’ control. The most common factors are patient sociodemographics, self-reported health status, depression, survey language, mode of survey (eg, mail or telephone interview), and whether the survey was completely by a proxy.13-20 Other studies focusing on hospital care found that patients with high severity of illness and complicated conditions were likely to have lower ratings of patient experience than their counterparts. They concluded that risk factors used in patient experience for hospital care insufficiently adjusted for clinical conditions that affected patient experience ratings.21-23
In home health, CMS and the HHCAHPS coordination team reported that the factors associated with patient experience are (1) patient sociodemographics, including age and education; (2) self-reported health status; (3) whether patients live alone; (4) self-reported mental/emotional status; (5) non-English survey response; (6) a diagnosis of schizophrenia or dementia/cerebral degeneration; and (7) whether the survey was completely by a proxy.24 These 7 factors are currently used to adjust patient experience for the 5-star patient survey rating system and the HHVBP pilot program. A study also found that minorities had lower ratings of patient experience for their home health care.25
Medicare home health beneficiaries are among the most vulnerable Medicare populations. Whether the risk factors currently used by CMS adequately adjust patient experience for home health care is largely unknown. Our study aimed to fill this critical knowledge gap by examining the interplay between agency-level CMS Hierarchical Condition Categories (HCC) risk scores (details in Methods section) and the patient experience metrics. We used the agency-level CMS HCC risk score as a proxy variable for the clinical and functional conditions of home health beneficiaries within home health agencies. Associations between patient experience measures and agency-level CMS HCC risk scores from our study provide critical information about whether the current risk-adjusted model for patient experience measures needs modification.
We merged 2 national Medicare databases at the home health agency level, the 2014 Medicare Provider Utilization and Payment Data for Home Health Agencies (PUPDHHA) and the 2014 Home Health Compare file.26,27 The PUPDHHA provides several agency-level variables based on 100% of Medicare enrollment and fee-for-service claims data during the 2014 calendar year.28 These variables include an agency-level HCC risk score based on the HCC risk scores from all of the beneficiaries cared for by a particular home health agency, provider identification number, number of distinct beneficiaries without a low-utilization payment episode (with ≤4 home health visits during an episode of home health care, namely, up to 60 days), and number of beneficiaries from different racial/ethnic groups.
The Home Health Compare file is updated quarterly by CMS.27 It consists of provider identification numbers and patient experience measures extracted from the HHCAHPS survey. The Home Health Compare file also provides characteristics of home health agencies, including ownership and CMS certification year. We extracted agency-level patient experience measures based on data collected from January 1, 2014, through December 31, 2014.
Study Design and Study Sample
We used a cross-sectional study design in which the home health agency was the unit of analysis. The study sample consisted of Medicare-certified home health agencies in 50 states and the District of Columbia. Home health agencies with fewer than 60 patients were excluded because they were exempted from the HHCAHPS survey.1
Dependent variables. The dependent variables are the 5 risk-adjusted agency-level patient experience measures from the 2 global questions and 3 composite measures extracted from CMS Home Health Compare. The HHCAHPS survey has 2 global questions; they include the “overall rating of care” provided by the home health agency (hereafter termed rating) and “patient willingness to recommend the home health agency to family or friends” (hereafter termed recommendation).29 The scale for rating in the survey ranges from 0 to 10. CMS reports the percentage of rating for an agency based on the percentage of patients who gave their home health care a rating of 9 or 10. The choices in the survey for recommendation include (1) definitely no, (2) probably no, (3) probably yes, and (4) definitely yes. The percentage of recommendation at the agency level is the number of patients who answered “definitely yes” divided by the total number of patients.
For the composite measures, CMS uses the data from the other 17 patient experience questions from the HHCAHPS survey. Each of these 3 measures is calculated from 4 or more topically related survey questions.1 The resulting composite measures include the following: “how often the home health team gives care in a professional way” (hereafter termed professional way), “how well the home health team communicates with patients” (hereafter termed communication), and whether or not the “home health team discuss[ed] medicines, pain, and home safety with patients” (hereafter termed discussion). The HHCAHPS survey website provides detailed information for the composite measures.29
The risk-adjusted global and composite measures for patient experience at the agency level are measured as a percentage. A higher percentage of patient experience measures indicates that the patients perceive higher-quality care from home health agencies.
Key independent variables. The key independent variable is the agency-level HCC risk score, extracted from PUPDHHA.26 The agency-level HCC risk score is the sum of CMS HCC risk scores from individual Medicare home health beneficiaries divided by the total number of Medicare home health beneficiaries for an individual home health agency.28 CMS constructs a CMS HCC risk score for an individual Medicare home health beneficiary as a risk factor to calculate a beneficiary’s likelihood of unplanned hospitalization and emergency department visits within 60 days of home health care for public reporting.30 The CMS HCC risk score for an individual Medicare home health beneficiary is a function of the beneficiary’s age, gender, original reason for Medicare entitlement, prior care setting, clinical conditions from CMS HCC, and their interaction terms. The CMS website provides detailed information about the CMS HCC risk score for home health beneficiaries.31,32
Control variables. In addition to the average CMS HCC risk score at the agency level, previous studies show that beneficiaries of different races/ethnicities rate their respective patient experience surveys differently.19,20,25 Therefore, we included variables describing the percentage of beneficiaries who were African American, Hispanic, and of other racial group, which includes beneficiaries who were not white, African American, or Hispanic. Finally, because the characteristics of home health agencies are associated with the quality of care provided,33,34 we included 2 dummy variables for ownership of a home health agency (ie, not-for-profit and public home health agencies, with for-profit home health agencies as the reference group) and the number of years that home health agencies had been certified by the Medicare program in the analytical models.
Home health agencies practice within a given state, facing that state’s regulations, which may differ from those of other states. Additionally, there is high variation in home health utilization across states, with $176 per beneficiary per year for the states at the 10th percentile and $866 per beneficiary per year for those at the 90th percentile.35 We applied fixed-effects models at the state level, with robust standard errors for each patient experience measure to account for differences in regulations and other state characteristics that affect the practice of home health agencies. Stata 14.2 (StataCorp; College Station, Texas) was used for data management and analyses. We used xtreg for the fixed-effects model.
There were 7756 home health agencies with data for patient experience measures available in the Home Health Compare file. Due to missing data, such as the agency-level CMS HCC risk score or race/ethnicity, the total number of observations included in our analytical models was 7637.
Table 1 presents descriptive statistics. For the overall patient experience measures, the average of rating with score 9 or 10 was about 84%, and the average of recommendation with “definitely yes” was about 79%. For the composite measures, the averages of professional way, communication, and discussion were 88%, 85%, and 84%, respectively. The average agency-level HCC risk score was 2.14. Home health agencies, on average, had 16% African American and 10% Hispanic beneficiaries, and about 2% of beneficiaries identified their race/ethnicity as other or unknown. Among our study sample, 21% of home health agencies were not-for-profit, and 6% of home health agencies were public. Home health agencies had an average of about 18 tenured years with the Medicare program.
The results from the fixed-effects models, in Table 2, showed that increases of 1 SD in agency-level CMS HCC risk scores (0.37 in Table 1) significantly lowered rating by about 0.75% (coefficient, —2.04, multiplied by 0.37) (P <.001) and recommendation by about 1% (P <.001), as well as professional way, communication, and discussion by about 0.6% (P <.001 for all 3 measures).
For the control variables related to race/ethnicity, increases in the percentage of African American beneficiaries were negatively associated with patient experience measures in rating (—0.06; P <.001), recommendation (—0.07; P <.001), and communication (—0.05; P <.001). Similarly, an increase in the percentage of beneficiaries of other racial/ethnic group was negatively associated with patient experience measures in rating (—0.08; P <.001), recommendation (—0.11; P <.01), professional way (—0.10; P <.001), and communication (—0.08; P <.001). We did not observe a significant association between the percentage of Hispanic beneficiaries and patient experience measures.
Characteristics of home health agencies were also associated with patient experience measures. Not-for-profit home health agencies had patient experience measures that were about 1 to 3 percentage points higher than for-profit home health agencies in all aspects: rating (1.74; P <.001), recommendation (2.92; P <.001), professional way (1.20; P <.001), communication (1.48; P <.001), and discussion (1.28; P <.001). Public home health agencies had patient experience measures that were about 2 to 4 percentage points higher than for-profit home health agencies in all aspects: rating (3.34; P <.001), recommendation (4.49; P <.001), professional way (1.97; P <.001), communication (1.77; P <.001), and discussion (2.10; P <.001). Increases in years tenured with the Medicare program were significantly associated with more positive patient experience measures of rating (0.03; P <.05), recommendation (0.04; P <.001), and communication (0.02; P <.01).
Our findings showed that increases in agency-level CMS HCC risk score were significantly associated with lower scores for all 5 patient experience measures. The findings indicated that current risk factors insufficiently adjust for the variation in beneficiaries’ clinical and functional conditions that affects patient experience. The differences in patient experience based on current risk factors partially reflect the differences in case mix among home health agencies.
CMS uses risk-adjusted patient experience to construct the 5-star patient survey rating system posted on the Home Health Compare website. The system was developed in an effort to improve the quality of home health care by allowing consumers the opportunity to find information on their community’s providers and choose the best providers. However, the CMS 5-star patient survey rating system of home health based on current risk factors could be misleading. Evidence shows that CMS public reporting affects market shares for health plans, hospitals, and nursing homes.36-38 Future studies examining how the CMS 5-star patient survey system affects market shares for home health agencies with high and low HCC risk scores are recommended.
Risk-adjusted patient experience is part of quality metrics in the HHVBP pilot program that have a financial impact on home health agencies. Considering the negative association between agency-level CMS HCC risk score and patient experience measures, home health agencies with a high proportion of clinically complicated beneficiaries are likely to be financially penalized under the HHVBP pilot program. Home health agencies can also simply dump or avoid beneficiaries with complicated conditions to improve their performance in patient experience measures, rather than investing resources in truly improving patient experience. Access to care for beneficiaries with complicated clinical and functional conditions can become problematic, given the fact that more than 80% of home health agencies are for-profit entities that pursue profit maximization.7
We also observed that increases in the percentage of African Americans and other racial/ethnic minority populations were negatively associated with patient experience measures, with 1 exception (discussion). Our findings are consistent with evidence from a recent patient-level study, which indicated that minority home health beneficiaries had lower patient experience rates in professional way and communication than home health beneficiaries who were non-Hispanic whites.25 Evidence from the same study also showed that Hispanic, African American, Asian, and Native Hawaiian or Pacific Islander home health beneficiaries had better experience measures in discussion than the non-Hispanic white population.25 The coefficients for discussion in our study are positive but not statistically significant. It is unclear why minorities gave a high rating for their home health providers discussing medicines, pain, and home safety but gave a low rating for the other measures. This may be explained by cultural differences among racial/ethnic groups regarding pain tolerance and differing attitudes among patients or informal caregivers about medication or patient safety. However, future studies that focus on the cultural differences affecting the rating of patient experience are needed.
Evidence from other studies showed that compared with not-for-profit agencies, for-profit agencies provided higher-cost and lower-quality home health care, including worsened process of care, poor functional improvement, and high rates of avoidable hospitalizations and bedsores.33 Our study found that compared with public and not-for-profit agencies, for-profit agencies had poorer patient experience performance in all 5 measures. We also found that increases in the number of tenured years with the Medicare program increased patient experience measures by about 0.01% to 0.04%. Over the past decade, the number of home health agencies increased dramatically, from about 7500 in 2000 to about 12,400 in 2014. This increase was partly due to the high profit margin from therapy visits in the Medicare home health payment system.7 Among all new home health agencies, 95% were for-profit entities, which are more likely to target therapy visits with high profit margins.34 The Medicare program is the primary payer for home health care and paid about $18 billion in 2015.7 The Medicare program strives to improve the quality and efficiency of care for Medicare home health beneficiaries. However, with the high cost and lower quality of care associated with for-profit agencies and with the rapidly growing number of for-profit agencies, CMS’ efforts to improve quality while controlling rising home health costs are limited because the license approval system and regulations for home health agencies occur at the state level. To overcome this problem, both payment reforms at the federal level and modifications at the state level for licensure and regulation are necessary to improve the quality and control the cost of home health care.
Our study has limitations. First, we used data at the agency level. The patient experience measures are based on the HHCAHPS survey from nonhospice, nonmaternity home health patients who are 18 years or older.39 However, CMS HCC risk scores are based on Medicare beneficiaries who are 65 years or older.31,32 Although the sources of patient data provided by the HHCAHPS survey and the CMS HCC risk score were not completely the same, the majority of home health beneficiaries in our sample were Medicare beneficiaries who were 65 years or older11 and were more likely to be in the random sampling of the HHCAHPS survey. Therefore, the inconsistent patient sources among the HHCAHPS survey and CMS HCC risk scores are less likely to affect the estimates in our study. However, we recommend that future studies use patient-level data to examine CMS HCC risk scores and patient experience measures.
Secondly, due to missing data, we excluded 119 home health agencies that qualified for the HHCAHPS survey. These agencies were more likely to be for-profit home health agencies with lower patient volume than those included in our study. Additionally, the HHCAHPS survey exempted home health agencies that had fewer than 60 patients per year, and thus these agencies were excluded from our study.
Our findings have research and policy implications that apply to the 5-star patient survey rating system and the HHVBP pilot program. In terms of research, access to care for minorities and beneficiaries with complicated clinical conditions needs to be carefully monitored under the 5-star patient survey rating system and the HHVBP pilot program. Additionally, it is necessary to investigate the practice patterns of home health agencies and the home health market and to determine how home health agencies with a high proportion of beneficiaries who have advanced clinical and functional conditions fare. In terms of policy implications, the current risk factors used to adjust patient experience measures need to be modified. The CMS HCC risk score is based on Medicare home health beneficiaries’ enrollment data and inpatient and outpatient claims data.30 The enrollment data are collected by CMS, and inpatient and outpatient claims data are provided by hospitals and physicians, so there is little chance for home health agencies to practice upcoding that may affect the CMS HCC risk score. Thus, it is feasible to include the CMS HCC risk score of a home health beneficiary as a risk factor in the current risk adjustment for patient experience measures in an effort to avoid potential unintended consequences.
The authors thank Madison Hedrick, MA, of the Science Communication Group at the University of Arkansas for Medical Sciences, who provided editing for the original version of the manuscript.Author Affiliations: Department of Health Policy and Management (H-FC, JMT, RS), and Department of Biostatistics (FW), College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR.
Source of Funding: None.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (H-FC, FW); acquisition of data (H-FC, RS); analysis and interpretation of data (JMT, FW); drafting of the manuscript (H-FC, JMT); critical revision of the manuscript for important intellectual content (H-FC, JMT, FW, RS); statistical analysis (H-FC); and provision of patients or study materials (RS).
Address Correspondence to: Hsueh-Fen Chen, PhD, Department of Health Policy and Management, College of Public Health, University of Arkansas for Medical Sciences, 4301 W Markham St, Mail Slot 820-12, Little Rock, AR 72205. Email: firstname.lastname@example.org.REFERENCES
1. Isaac T, Zaslavsky AM, Cleary PD, Landon BE. The relationship between patients’ perception of care and measures of hospital quality and safety. Health Serv Res. 2010;45(4):1024-1040. doi: 10.1111/j.1475-6773.2010.01122.x.
2. Carter J, Ward C, Wexler D, Donelan K. The association between patient experience factors and likelihood of 30-day readmission: a prospective cohort study. BMJ Qual Saf. 2018;27(9):683-690. doi: 10.1136/bmjqs-2017-007184.
3. Glickman SW, Boulding W, Manary M, et al. Patient satisfaction and its relationship with clinical quality and inpatient mortality in acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2010;3(2):188-195. doi: 10.1161/CIRCOUTCOMES.109.900597.
4. Home Health Care CAHPS Survey. Home Health Care CAHPS Survey website. homehealthcahps.org/General-Information/About-Home-Health-Care-CAHPS-Survey. Accessed August 5, 2017.
5. Home health star ratings. CMS website. cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HomeHealthQualityInits/HHQIHomeHealthStarRatings.html. Accessed August 10, 2017.
6. CMS, HHS. Medicare and Medicaid Programs; CY 2017 Home Health Perspective Payment System Rate Update; Home Health Value-Based Purchasing Model; and Home Health Quality Reporting Requirements: final rule. Fed Regist. 2016;81(213):76702-76797.
7. Chapter 8: home health care services. In: Medicare Payment Advisory Commission. Report to the Congress: Medicare Payment Policy. Washington, DC: MedPAC; 2016:206-234.
8. Ellenbecker CH, Frazier SC, Verney S. Nurses’ observations and experiences of problems and adverse effects of medication management in home care. Geriatr Nurs. 2004;25(3):164-170. doi: 10.1016/j.gerinurse.2004.04.008.
9. Cannon KT, Choi MM, Zuniga MA. Potentially inappropriate medication use in elderly patients receiving home health care: a retrospective data analysis. Am J Geriatr Pharmacother. 2006;4(2):134-143. doi: 10.1016/j.amjopharm.2006.06.010.
10. Caffrey C, Sengupta M, Moss A, Harris-Kojetin L, Valverde R. Home health care and discharged hospice care patients: United States, 2000 and 2007. Natl Health Stat Report. 2011;(38):1-27.
11. The Alliance for Home Health Quality and Innovation. The Medicare home health benefit: program costs and beneficiary characteristics. Almost Family website. almostfamily.com/pdf/20090715_AHHQI%20Chart%20Book.pdf. Published July 15, 2009. Accessed April 10, 2017. Available at Wayback Machine at: web.archive.org/web/20131029215845/http://www.almostfamily.com/pdf/20090715_AHHQI%20Chart%20Book.pdf.
12. Cho E, Kim EY, Lee NJ. Effects of informal caregivers on function of older adults in home health care. West J Nurs Res. 2013;35(1):57-75. doi: 10.1177/0193945911402847.
13. Zaslavsky AM, Zaborski LB, Ding L, Shaul JA, Cioffi MJ, Cleary PD. Adjusting performance measures to ensure equitable plan comparisons. Health Care Financ Rev. 2001;22(3):109-126.
14. O’Malley AJ, Zaslavsky AM, Elliott MN, Zaborski L, Cleary PD. Case-mix adjustment of the CAHPS Hospital Survey. Health Serv Res. 2005;40(6, pt 2):2162-2181. doi: 10.1111/j.1475-6773.2005.00470.x.
15. Hall JA, Milburn MA, Roter DL, Daltroy LH. Why are sicker patients less satisfied with their medical care? tests of two explanatory models. Health Psychol. 1998;17(1):70-75.
16. Martino SC, Elliott MN, Kanouse DE, Farley DO, Burkhart Q, Hays RD. Depression and the health care experiences of Medicare beneficiaries. Health Serv Res. 2011;46(6, pt 1):1883-1904. doi: 10.1111/j.1475-6773.2011.01293.x.
17. Johnson ML, Rodriguez HP, Solorio MR. Case-mix adjustment and the comparison of community health center performance on patient experience measures. Health Serv Res. 2010;45(3):670-690. doi: 10.1111/j.1475-6773.2010.01101.x.
18. Paddison C, Elliott M, Parker R, et al. Should measures of patient experience in primary care be adjusted for case mix? evidence from the English General Practice Patient Survey. BMJ Qual Saf. 2012;21(8):634-640. doi: 10.1136/bmjqs-2011-000737.
19. Goldstein E, Elliott MN, Lehrman WG, Hambarsoomian K, Giordano LA. Racial/ethnic differences in patients’ perceptions of inpatient care using the HCAHPS survey. Med Care Res Rev. 2010;67(1):74-92. doi: 10.1177/1077558709341066.
20. Price RA, Haviland AM, Hambarsoomian K, et al. Do experiences with Medicare managed care vary according to the proportion of same-race/ethnicity/language individuals enrolled in one’s contract? Health Serv Res. 2015;50(5):1649-1687. doi: 10.1111/1475-6773.12292.
21. Merlino JI, Kestranek C, Bokar D, Sun Z, Nissen SE, Longworth DL. HCAHPS survey results: impact of severity of illness on hospitals’ performance on HCAHPS survey results. J Patient Exp. 2014;1(2):16-21. doi: 10.1177/237437431400100204.
22. Thiels CA, Hanson KT, Yost KJ, Zielinski MD, Habermann EB, Cima RR. Effect of hospital case mix on the Hospital Consumer Assessment of Healthcare Providers and Systems star scores: are all stars the same? Ann Surg. 2016;264(4):666-673. doi: 10.1097/SLA.0000000000001847.
23. Cowen ME, Czerwinski J, Kabara J, Blumenthal DU, Kheder S, Simmons S. The risk-outcome-experience triad: mortality risk and the Hospital Consumer Assessment of Healthcare Providers and Systems survey. J Hosp Med. 2016;11(9):628-635. doi: 10.1002/jhm.2611.
24. Patient-mix adjustment factors for Home Health Care CAHPS Survey results publicly reported on Home Health Compare in October 2017. Home Health Care CAHPS Survey website. homehealthcahps.org/Portals/0/PublicReporting/PMAandHHCAHPSresults_OctPublicReporting2017.pdf. Published October 2017. Accessed December 18, 2017.
25. Smith LM, Anderson WL, Kenyon A, et al. Racial and ethnic disparities in patients’ experience with skilled home health care services. Med Care Res Rev. 2015;72(6):756-774. doi: 10.1177/1077558715597398.
26. Medicare provider utilization and payment data: home health agencies. CMS website. cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/HHA.html. Updated April 5, 2018. Accessed June 15, 2017.
27. About Home Health Compare data. CMS website. medicare.gov/homehealthcompare/Data/About.html. Accessed June 15, 2017.
28. Medicare Fee-For-Service Home Health Agency Utilization & Payment Public Use File: a methodological overview. CMS website. cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/HHA_Methodology.pdf. Published August 2018. Accessed September 5, 2018.
29. Steps for calculating global ratings and composite scores for the Home Health Care CAHPS Survey. Home Health Care CAHPS Survey website. homehealthcahps.org/Portals/0/HHCAHPS_steps_calculate_composites.pdf?ver=2016-11-07-101103-157. Accessed December 18, 2017.
30. Acumen, LLC. Specifications for home health claims-based utilization measures. CMS website. cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HomeHealthQualityInits/Downloads/ClaimsBasedUtilizationMeasuresSpecifications.pdf. Published August 21, 2012. Accessed December 18, 2017.
31. Risk adjustment. CMS website. cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors.html. Updated July 31, 2018. Accessed December 18, 2017.
32. Claims-based AC and ED use measures: technical documentation and risk adjustment. CMS website. cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HomeHealthQualityInits/Downloads/Claims-Based-AC-and-ED-Use-Measures-Technical-Documentation-and-Risk-AdjustmentZIP.ZIP. Updated August 27, 2018. Accessed December 20, 2017.
33. Cabin W, Himmelstein DU, Siman ML, Woolhandler S. For-profit Medicare home health agencies’ costs appear higher and quality appears lower compared to nonprofit agencies. Health Aff (Millwood). 2014;33(8):1460-1465. doi: 10.1377/hlthaff.2014.0307.
34. Kim H, Norton EC. Practice patterns among entrants and incumbents in the home health market after the prospective payment system was implemented. Health Econ. 2015;24(suppl 1):118-131. doi: 10.1002/hec.3147.
35. Home health agency reimbursements per enrollee, by adjustment type. The Dartmouth Atlas of Health Care website. dartmouthatlas.org/data/table.aspx?ind=229. Accessed December 20, 2017.
36. Werner RM, Konetzka RT, Polsky D. Changes in consumer demand following public reporting of summary quality ratings: an evaluation in nursing homes. Health Serv Res. 2016;51(suppl 2):1291-1309. doi: 10.1111/1475-6773.12459.
37. Reid RO, Deb P, Howell BL, Shrank WH. Association between Medicare Advantage plan star ratings and enrollment. JAMA. 2013;309(3):267-274. doi: 10.1001/jama.2012.173925.
38. Romano PS, Marcin JP, Dai JJ, et al. Impact of public reporting of coronary artery bypass graft surgery performance data on market share, mortality, and patient selection. Med Care. 2011;49(12):1118-1125. doi: 10.1097/MLR.0b013e3182358c78.
39. CMS. Home Health Care CAHPS Survey: protocols and guidelines manual: version 10.0. homehealthcahps.org/Portals/0/PandGManual.pdf. Published January 2018. Accessed September 5, 2018.