Marc N. Elliott, PhD

 Knowing which patient-centered medical home (PCMH) care delivery changes and quality improvement (QI) practices further PCMH implementation is essential.

We used the 2008-2017 National Committee of Quality Assurance (NCQA) PCMH directory of 15,188 primary care practices that received Level 1, 2, or 3 NCQA PCMH recognition to construct a stratified national sample of 105 practices engaged in PCMH transformation. We examined their QI practices and PCMH changes associated with PCMH transformation.

We derived QI practice and PCMH change variables from semistructured interviews. Practice leaders completed the PCMH Assessment (PCMH-A) measuring the practice’s degree of PCMH implementation, which is a proxy for patient-centeredness. Controlling for practice characteristics, we regressed PCMH-A scores on QI practice and PCMH change variables.

 Practices undergoing PCMH transformation nationwide most commonly made care delivery changes in access and continuity of care. To improve quality, practices most commonly engaged in discussing and targeting areas of patient experience improvement, trending performance, and conducting targeted QI. However, practices lower in patient-centeredness as measured by the PCMH-A were more likely to engage in efforts to improve patient experiences, such as reviewing patient experience data or engaging in 1-on-1 provider counseling related to patient interactions. Mature PCMH practices focused on changes in continuity of care.

 Practices undertake a wide variety of care delivery changes and QI practices simultaneously to meet PCMH requirements. The patient experience–specific QI practices and PCMH care delivery changes that practices make to improve patient-centeredness differ by years of PCMH recognition.

Evidence shows that practice leaders who implement the patient-centered medical home (PCMH) care model encounter challenges in driving change. Understanding which PCMH changes and quality improvement (QI) practices further PCMH implementation is essential.

Practices implementing PCMH made a wide range of changes in care delivery and QI practices.

The most common changes were in access and continuity of care. QI practices used most often were discussing and targeting areas of patient experience improvement and trending performance.

Mature PCMH practices focused on changes to continuity of care, whereas practices lower in patient-centeredness focused on improving patient experiences. These differences provide guidance for practices undergoing PCMH implementation.

Health care organizations in the United States face challenges in delivering high-quality primary care.

 For reform measures to successfully improve health, affordability, and patient experience, as well as meaning in work for providers, the care delivery system must be transformed.

 The patient-centered medical home (PCMH) model is a leading effort to improve quality of care

 by reorganizing a practice’s care delivery process to promote comprehensive care, patient-centeredness, care coordination, accessible services, safety, and high-quality care.

 The model offers many pathways to reduce costs and improve quality, but evidence of its impact is mixed.

 Due in part to variations in the amount and types of practices implemented, PCMH implementation varies significantly by site, with practices undergoing many changes.

 Although there is no single way to achieve PCMH transformation, there is concordance in the main implementation steps.

PCMH recognition and certification require that core criteria are satisfied; they also provide various optional criteria.

 Practices target PCMH changes that are required for recognition and utilize a variety of quality improvement (QI) activities for PCMH transformation. Practices are required to collect and use patient experience surveys and other data to improve their care and services. Which QI activities and PCMH changes are most useful is unclear.

 Research has examined organizational climate,

 structural factors associated with PCMH changes,

 and which PCMH implementation resources are widely used,

 but it has not studied these factors alongside a validated measure of PCMH implementation with established psychometric qualities. One such measure is the PCMH Assessment (PCMH-A), a proxy for patient-centeredness that measures the degree of PCMH implementation at the practice level.

 PCMH-A scores are higher for recognized PCMH practices than for nonrecognized practices.

 In this study, we investigate QI practices and PCMH changes associated with higher levels of PCMH transformation using the PCMH-A.

The National Committee for Quality Assurance (NCQA) is the largest program to certify PCMH practices. To select our sample, we used NCQA’s PCMH directory of 15,188 primary care practices that had applied for and received Level 1, 2, or 3 NCQA PCMH recognition between 2008 (when NCQA PCMH began) and 2017. We excluded practices that lacked a current NCQA PCMH status, were pediatric only, were in Puerto Rico, or provided care to the Armed Forces.

From the remaining 10,712 practices, we developed a stratified sample of 900 practices by US Census region, whether a statewide PCMH initiative existed, physician count, level and years of PCMH recognition, and use of the Consumer Assessment of Healthcare Providers and Systems (CAHPS) PCMH survey. Of these, 55 were ineligible (eg, merged, closed, HIV only, child only), leaving 845 practices. We drew a stratified random sample of 294 practices, contacted them via email and phone, and received completed responses from 105 (36% response rate). 

 summarizes final sample characteristics. Details of the sample and qualitative findings are described elsewhere.

Four researchers (2 coauthors [D.D.Q., N.Q.], 2 analysts) conducted semistructured, hour-long interviews with practice leaders in charge of PCMH transformation. Each interview discussed practice characteristics, such as PCMH history (ie, level and years of PCMH recognition), motivation for PCMH transformation, areas of care delivery change targeted for PCMH implementation, and QI practices used to make PCMH changes. Practice leaders completed the PCMH-A after the interview. We recorded and transcribed the phone interviews and paid participants a $75 honorarium.

We entered transcripts into Dedoose (SocioCultural Research Consultants), a web application for analyzing qualitative data. We created codes for care delivery changes identified in the NCQA PCMH standards and related QI practices using content analysis to identify key topics and develop a codebook.

 We developed the code structure using systematic, inductive procedures to generate insights from responses and mapped codes to research questions.

 We used content analysis to identify themes.

The 4 interviewers coded transcripts individually, using team meetings to reach consensus. We mapped emerging topics and processes to the 2017 NCQA PCMH standards and designated whether areas of improvement were part of core or optional criteria for PCMH recognition.

 Practices must meet core criteria but have leeway on optional criteria. We also coded QI practices discussed during the interviews.

Interrater reliability was 0.93, indicating “very good” agreement.

 We employed ongoing training among the coding team on emerging subcodes using the Dedoose training module. Study protocols were approved by RAND’s Human Subjects Protection Committee (IRB Assurance No. FWA00003425; IRB No. IRB00000051) and the Office of Management and Budget (OMB No. 0935-0236).

PCMH-A scores range from 1 to 12, with higher scores indicating greater implementation of PCMH recommended practices. Scores assess 36 PCMH practices and behaviors associated with 8 PCMH change concepts. The overall score is the mean of the 8 change-concept scores. PCMH-A scores are sensitive to practice change over a time period as short as 6 months,

 and we used the overall score as our dependent variable.

QI practice and PCMH change variables were elicited from semistructured interview questions about practice changes related to PCMH or its requirements. The 

) provides quotes and terms to establish these indicators. Responses were coded to align with NCQA PCMH standards and to note whether changes were core or optional.

We derived QI practice indicators from questions on PCMH implementation and on efforts to improve patient experiences. The 4 general QI practices were (1) tracking performance and trending data over time, (2) discussing best practices with other providers or practices, (3) using staff meetings to review performance data, and (4) starting or continuing QI initiatives for PCMH change. The 5 patient experience QI practices were (1) sharing patient experience survey scores with leadership, (2) benchmarking and comparing patient experience scores over time, (3) discussing/targeting areas of needed improvement related to patient experiences of care, (4) reviewing provider performance on patient experience surveys, and (5) 1-on-1 counseling with low-performing providers. We scored these QI practice measures as dichotomous variables. We also created 2 count measures of the number of general (0-4) or patient-specific (0-5) QI practices.

The 7 core PCMH changes were for access, communicate lab test results, continuity of care, flow of patient visit, referrals to specialists, team-based care, and team huddles. The 4 optional PCMH changes were for behavioral health

 use of electronic health records (EHRs) to support patient care, and population health management. We analyzed dichotomous indicators of whether the practice implemented each of these changes and also total counts of the 7 core and 4 optional changes made by a practice.

We classified practices’ PCMH history into 3 groups: (1) practices that had not yet achieved Level 3 PCMH recognition (ie, received NCQA Level 1 or Level 2), (2) practices that had achieved Level 3 PCMH recognition within the past 3 years, and (3) practices that had held Level 3 PCMH recognition for more than 3 years.

We examined the percentages of practices reporting each QI and PCMH initiative overall and by PCMH history (

). We fit separate multiple linear regression models for the 9 QI practices, 11 PCMH changes, and 4 count variables, controlling for PCMH history, CAHPS PCMH survey status, location, and number of physicians. Next, we fit these same models with an interaction term between each QI practice or PCMH change and PCMH history. We added the interaction terms because PCMH history is a potential effect modifier. Because access and continuity of care changes were endorsed by nearly all practices, we did not include them in the interaction model.

From these models, we calculated estimated marginal means (EMMs) for all practices (using the noninteraction model) and by practice subgroups (using the interaction model). For the 4 count variables, we calculated the EMM to compare practices that had a sum of 1 with those that had a sum of 0. In calculating the EMM, we set all other control variables to their mean values. All analyses used R version 3.6.1 (R Foundation for Statistical Computing), including the 

Table 1 summarizes characteristics of the 105 practices. About half the practices were in the Northeast, and about half had Level 3 certification for at least 3 years. Most had fewer than 10 physicians on staff. The mean PCMH-A score for practices with Level 3 PCMH recognition for 3 or more years (9.6) was higher than the overall mean score of 9.2. It was also higher for non–PCMH initiative states (8.8), other Northeast states (10.0), and sites that were primary care only (9.4). Seventy-eight percent of practice leaders were present since the start of PCMH implementation (described elsewhere).

Table 2 shows the proportion of the 105 practice managers reporting the implementation of QI practices and PCMH changes. The most widely implemented PCMH changes were changes to continuity of care and access to care, followed by use of the EHR for patient-centered care, conducting team huddles, and changing the flow of a patient visit. The least implemented PCMH change was investing in population health management activities. Sixteen percent of practices had implemented all 11 changes; the mean number of core changes was 6.0 (of 7) and the mean number of optional changes was 2.9 (of 4).

Eighty-eight percent of practices employed at least 1 of the 9 QI practices during PCMH implementation, but none employed all 9 practices. Sixty-three percent of practices engaged in at least 1 general QI practice and 60% in at least 1 patient experience–specific QI practice. The most-used QI practices were discussing and targeting areas of patient experience improvement (34%), tracking performance and trending data over time (33%), and starting or continuing QI activity for PCMH change areas (30%). The least-used practices were discussing best practices with other providers or practices (10%), benchmarking or comparing patient experience data over time (15%), and 1-on-1 counseling with low-performing providers (15%). The mean count of general QI practices was 0.9 (of 4) and of patient experience–specific practices was 1.1 (of 5).

Our noninteraction models show that review of provider performance on patient experience, 1-on-1 counseling for patient experience, and referrals to specialists were associated with lower mean PCMH-A scores (

). Use of more patient experience–specific QI practices was also associated with lower mean PCMH-A scores.

Our exploratory interaction models for each group of practices by PCMH maturity indicate that among practices with less than 3 years of PCMH recognition, those that engaged in benchmarking and comparing data over time or 1-on-1 counseling for patient experience had lower PCMH-A scores than those that did not (

). In addition, a greater number of QI-specific practices or optional PCMH changes were associated with lower PCMH-A scores. The only reported practice type associated with significantly higher PCMH-A scores was those with 3 or more years of PCMH recognition that worked on continuity of care.

Implementing the PCMH model of care includes a wide range of changes in care delivery, particularly changes related to continuity of care and access. This is not surprising, because access and continuity of care are core aspects of PCMH recognition and transformation. Many sites across the 3 groups of PCMH transformation reported implementing the core set of NCQA PCMH changes. This is consistent with attempts to implement the standards and elements of the PCMH model. There is variation in how practices chose to implement optional criteria, with, for example, 80% implementing referrals to specialists and 44% implementing population health practices. Our findings support the adage that what is required is implemented and what is measured is managed.

Our results confirm that QI is a key activity of PCMH implementation, with the majority of practices engaging in combinations of multiple QI practices. PCMH transformation most often included the general QI practices of tracking and trending performance over time and starting or continuing a QI activity, as well as the more patient experience–specific QI practices of sharing patient experience data with leadership and discussing and targeting areas of patient experience improvement. Tracking and trending performance can identify target areas for QI efforts. Discussing and targeting areas of patient experience improvement indicates that patient experience data, metrics, and improvement are key for practices as they become more patient centered. However, practices undertook each individual QI practice much less often than making one of the central changes to care delivery outlined to achieve required PCMH standards.

Controlling for site characteristics, we found that practices using 1-on-1 counseling to improve patient experience scores or reviewing provider performance on patient experience had significantly lower PCMH implementation than others. Reviewing provider performance on patient experience and using 1-on-1 counseling for providers can identify and target modifiable provider behaviors.

 Such coaching has been effective in building and maintaining competencies among providers and in increasing compliance with practice guidelines.

Furthermore, among practices with the same stage of PCMH implementation from our exploratory analysis, we find that sites with less than 3 years of PCMH Level 3 recognition significantly differ in their use of 1-on-1 counseling, benchmarking and comparing data, and utilizing an increased number of patient experience–specific QI practices. Controlling for site characteristics, those using 1-on-1 counseling for patient experience performance improvement, benchmarking and comparing data over time, and more QI practices related to patient experience have lower PCMH implementation. This suggests that sites lacking PCMH care delivery (ie, low PCMH implementation indicating low patient-centeredness) focus more on benchmarking and comparing data over time, 1-on-1 counseling for patient experience performance improvement, and QI activities specific to improving patient experience. That is, practices recognize that they are lacking in patient-centeredness and search for ways to improve patient experiences of care through identifying low performers and using targeted, individual counseling as a means of improving quality.

Our study extends previous evidence on PCMH implementation. Solberg et al

 found among 123 primary practices in Minnesota that setting goals and benchmarking performance at least yearly worked well for PCMH implementation. We found that benchmarking practice-level performance was used most often during the early stages of PCMH transformation.

Previous research found that the culture of quality improvement and specifically conducting QI efforts during PCMH implementation is important; however, studies that investigated QI as part of PCMH implementation were a single health care organization

 To our knowledge, the only nationwide PCMH implementation study was a cross-sectional study of 6464 Veterans Health Administration primary care personnel within clinics nested within 135 hospitals.

 That study found that team huddles were most widely used and QI was least frequently used.

Additionally, we found that practices with mature PCMH recognition achieve higher levels of PCMH implementation by focusing on changes to continuity of care. We examined the association of team huddles and changes to continuity of care or access to care in addition to the role of QI practices in assessing a site-level measure of PCMH implementation. Although we did not find a significant association between most PCMH changes and QI practices and higher levels of PCMH implementation, we did find that practices lacking in PCMH implementation engaged more in benchmarking and comparing data over time and more QI activities related to improving patient experience, specifically 1-on-1 counseling. When needing to improve patient-centeredness, practices focused on improving their referrals to specialists and engaging in benchmarking and comparing data over time. They did not engage in general QI but instead in specific QI practices for improving patient experience.

The lack of significant findings for PCMH changes by levels of implementation may be because of the high endorsement of PCMH changes, yielding little variation. Or, the nonsignificant findings for conducting QI may result from the wide range of general QI efforts that practices can undertake during PCMH transformation, with each connected to PCMH implementation. Further work should characterize general PCMH QI initiatives during PCMH transformation and investigate their association with PCMH history and implementation.

Our study has limitations. Although the study design included a national, large, stratified set of primary care practices, it was not nationally representative. Also, although we recruited and identified leaders most knowledgeable and responsible for PCMH changes in the practice, we typically interviewed 1 representative per organization. There may be additional perspectives within practice leadership that we did not capture, and leaders with more positive feelings toward PCMH may have chosen to participate. Our participation rate was 36% and may not have been random within each stratum. Nevertheless, this study explored a rich, wide range of perspectives from practice leaders about PCMH implementation. It was able to show which PCMH changes were more common and provide evidence that practices do engage primarily in making core PCMH changes and that they vary in making optional PCMH changes. Our data are limited in that they are cross-sectional, but we were able to control for practice characteristics including PCMH history. The PCMH-A was completed after the interview, which may have resulted in higher PCMH-A scores. Because the PCMH-A is meant to be an inventory of all PCMH changes, these scores likely reflect more accurate assessments than if the interviews were conducted after PCMH-A completion. Finally, we focused on practices that obtained PCMH recognition from NCQA, the most widely used organization to provide PCMH recognition, and did not include practices that sought PCMH recognition from different programs (eg, The Joint Commission).

Implementation of the PCMH model includes a wide range of changes in care delivery and a varied set of QI practices. Practices that recognize they are lacking in patient-centeredness seek to specifically improve patient experiences of care; they review patient experience scores or engage in 1-on-1 counseling of providers to improve their patient experience scores. Practices that have recently obtained PCMH Level 3 recognize their lack of patient-centeredness (ie, low PCMH implementation) and engage in ways to improve their implementation. This includes increasing the number of optional PCMH changes, benchmarking and comparing data over time, and increasing QI-specific practices related to patient experience, particularly 1-on-1 counseling for patient experience improvement. Such actions can advance the PCMH transformation process, whereas practices with mature PCMH recognition can achieve higher implementation scores by focusing on changes to continuity of care.

Practices undertake a wide variety of care delivery changes and QI practices simultaneously to meet PCMH requirements. Given limited resources, it is important to know what care delivery changes or QI practices are needed when PCMH implementation is lacking and when those changes or practices should be implemented. Our study provides considerations for which PCMH changes and what QI practices, particularly those related to patient experience performance, should be instituted to improve PCMH implementation. Differences in timing of care delivery changes during the course of PCMH recognition provide guidance for practices in PCMH implementation.

The authors acknowledge the time and support of the interview participants in this study.

 RAND Corporation (DDQ, MS, NQ, MNE, RDH), Santa Monica, CA; University of California, Los Angeles (RDH), Los Angeles, CA.

 This work was supported by a cooperative agreement from the Agency for Healthcare Research and Quality (contract No. U18 HS016980 and U18 HS025920).

 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.

 Concept and design (DDQ); acquisition of data (DDQ, NQ); analysis and interpretation of data (DDQ, MS, NQ, MNE); drafting of the manuscript (DDQ, MS, NQ); critical revision of the manuscript for important intellectual content (DDQ, NQ, MNE, RDH); statistical analysis (MS, NQ); provision of patients or study materials (DDQ, NQ); obtaining funding (DDQ, MNE, RDH); administrative, technical, or logistic support (DDQ, NQ); and supervision (DDQ, RDH).

 Denise D. Quigley, PhD, RAND Corporation, 1776 Main St, Santa Monica, CA 90406. Email: quigley@rand.org.

1. Berwick DM. A user’s manual for the IOM’s ‘Quality Chasm’ report. 

. 2002;21(3):80-90. doi:10.1377/hlthaff.21.3.80

Crossing the Quality Chasm: A New Health System for the 21st Century

3. Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. 

. 2014;12(6):573-576. doi:10.1370/afm.1713

4. Rittenhouse DR, Shortell SM. The patient-centered medical home: will it stand the test of health reform? 

. 2009;301(19):2038-2040. doi:10.1001/jama.2009.691

5. Robert Graham Center. The patient centered medical home: history, seven core features, evidence and transformational change. American Academy of Family Physicians. November 2007. Accessed June 27, 2019. https://www.aafp.org/dam/AAFP/documents/about_us/initiatives/PCMH.pdf

6. Rosenthal TC. The medical home: growing evidence to support a new approach to primary care. 

. 2008;21(5):427-440. doi:10.3122/jabfm.2008.05.070287

7. Defining the PCMH. Agency for Healthcare Research and Quality. Accessed April 16, 2020. https://pcmh.ahrq.gov/page/defining-pcmh

8. Hoff T, Weller W, DePuccio M. The patient-centered medical home: a review of recent research. 

. 2012;69(6):619-644. doi:10.1177/1077558712447688

9. Jabbarpour Y, DeMarchis E, Bazemore A, Grundy P. The impact of primary care practice transformation on cost, quality, and utilization: a systematic review of research published in 2016. Patient-Centered Primary Care Collaborative. July 2017. Accessed March 15, 2020. https://www.pcpcc.org/sites/default/files/resources/pcmh_evidence_report_08-1-17%20FINAL.pdf

10. Jackson GL, Powers BJ, Chatterjee R, et al. The patient centered medical home. a systematic review. 

. 2013;158(3):169-178. doi:10.7326/0003-4819-158-3-201302050-00579

11. Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials. 

. 2009;301(6):603-618. doi:10.1001/jama.2009.126

12. Peikes D, Zutshi A, Genevro JL, Parchman ML, Meyers DS. Early evaluations of the medical home: building on a promising start. 

13. Lieberthal RD, Karagiannis T, Bilheimer E, et al. Exploring variation in transformation of primary care practices to patient-centered medical homes: a mixed methods approach. 

. 2017;20(5):411-418. doi:10.1089/pop.2016.0132

14. Quigley DD, Predmore ZS, Chen AY, Hays RH. Implementation and sequencing of practice transformation in urban practices with underserved patients. 

2017;26(1):7-14. doi:10.1097/QMH.0000000000000118

15. NCQA PCMH standards and guidelines (2017 edition). National Committee for Quality Assurance. 2018. Accessed October 2, 2018. http://store.ncqa.org/index.php/catalog/product/view/id/2776/s/2017-pcmh-
standards-and-guidelines-epub

16. Gale RC, Asch SM, Taylor T, et al. The most used and most helpful facilitators for patient-centered medical home implementation. 

. 2015;10:52. doi:10.1186/s13012-015-0246-9

17. Khanna N, Shaya FT, Chirikov VV, Sharp D, Steffen B. Impact of case mix severity on quality improvement in a patient-centered medical home (PCMH) in the Maryland Multi-Payor Program. 

. 2016;29(1):116-125. doi:10.3122/jabfm.2016.01.150067

18. Yoon J, Chow A, Rubenstein LV. Impact of medical home implementation through evidence-based quality improvement on utilization and costs. 

. 2016;54(2):118-125. doi:10.1097/MLR.0000000000000478

19. Reddy A, Pollack CE, Asch DA, Canamucio A, Werner RM. The effect of primary care provider turnover on patient experience of care and ambulatory quality of care. 

. 2015;175(7):1157-1162. doi:10.1001/jamainternmed.2015.1853

20. Korom-Djakovic D, Canamucio A, Lempa M, Yano EM, Long JA. Organization complexity and primary care providers’ perceptions of quality improvement culture within the Veterans Health Administration. 

. 2016;31(2):139-146. doi:10.1177/1062860614559743

21. Solberg LI, Stuck LH, Crain AL, et al. Organizational factors and change strategies associated with medical home transformation. 

. 2015;30(4):337-344. doi:10.1177/1062860614532307

22. Resources and tools: assessment. Safety Net Medical Home Initiative. Accessed October 3, 2018. http://www.safetynetmedicalhome.org/resources-tools/assessment

23. Daniel DM, Wagner EH, Coleman K, et al. Assessing progress toward becoming a patient-centered medical home: an assessment tool for practice transformation. 

. 2013;48(6, pt 1):1879-1897. doi:10.1111/1475-6773.12111

24. Quigley DD, Qureshi N, Al-Masarweh L, Hays RD. Practice leaders report targeting several types of changes in care experienced by patients during patient-centered medical home transformation. 

. 2020;7(6):1509-1518. doi:10.1177/2374373520934231

Content Analysis: An Introduction to Its Methodology.

26. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. 

. 2007;42(4):1758-1772. doi:10.1111/j.1475-6773.2006.00684.x

27. De Vries H, Elliott MN, Kanouse DE, Teleki SS. Using pooled kappa to summarize interrater agreement across many items. 

2008;20(3):272-282. doi:10.1177/1525822X08317166

28. Quigley DD, Predmore ZS, Hays RD. Tools to gauge progress during patient-centered medical home transformation. 

29. Patient-centered medical home (PCMH). National Committee for Quality Assurance. Accessed October 2, 2018. https://www.ncqa.org/programs/health-care-providers-practices/patient-centered-medical-home-pcmh/

30. Romaire MA, Keyes V, Parish WJ, Kim K. Impact of medical homes on expenditures and utilization for beneficiaries with behavioral health conditions. 

. 2018;69(8):871-878. doi:10.1176/appi.ps.201700433

31. Aysola J, Werner RM, Keddem S, SoRelle R, Shea JA. Asking the patient about patient-centered medical homes: a qualitative analysis. 

. 2015;30(10):1461-1467. doi:10.1007/s11606-015-3312-8

32. Qureshi N, Quigley DD, Hays RD. Nationwide qualitative study of practice leader perspectives on what it takes to transform into a patient-centered medical home. 

. 2020;35(12):3501-3509. doi:10.1007/s11606-020-06052-1

33. Wolever R, Moore MA, Jordan M. Coaching in healthcare. In: Bachkirova T, Spence G, Drake D, eds. 

34. Nelson JL, Apenhorst DK, Carter LC, Mahlum EK, Schneider JV. Coaching for competence. 

35. Wise T, Gautam B, Harris R, Casida D, Chapman R, Hammond L. Increasing the registered nursing workforce through a second-degree BSN program coaching model. 

. 2016;41(6):299-303. doi:10.1097/NNE.0000000000000284

36. Poe SS, Abbott P, Pronovost P. Building nursing intellectual capital for safe use of information technology: a before-after study to test an evidence-based peer coach intervention. 

. 2011;26(2):110-119. doi:10.1097/NCQ.0b013e31820b221d

Articles

Practices undertake many care delivery changes and quality improvement practices to become patient-centered medical homes (PCMHs), and these differ by years of PCMH recognition.

Practices and Changes Associated With Patient-Centered Medical Home Transformation

 To develop an easy-to-interpret, patient-reported Functional Limitations Index (FLI) that can be used to assess and monitor the full spectrum of functioning in a community-dwelling population.

 Observational design using nationally representative survey data.

 We used self-rated health as a criterion for empirically assigning weights to 5 National Health Interview Survey items assessing difficulty with seeing, hearing, walking, cognition, and self-care. In addition to succinctly summarizing cumulative limitations, we addressed 2 main questions: (1) Which limitations have stronger associations with self-rated health? and (2) How does severity (from 0, no difficulty, to 3, unable to do) relate to self-rated health? We generated a respondent-level summary score based on a model predicting self-rated health from the 5 linearly scored (0-3) items and used splines to account for nonlinear severity—self-rated health associations.

 The strongest association of specific functional limitations with self-rated health involved mobility; the weakest associations involved sensory limitations. The association of severity with self-rated health was nonlinear and largest moving from 

. Nationally, 5% of noninstitutionalized adults were considered 

. Great mobility limitations (defined as 

) most distinguished limitation groups (present in 0% of 

 The FLI is an easy-to-administer, easy-to-interpret, and valid summary measure of disability that health plans and health care organizations can use for quality-of-care monitoring across a variety of settings to improve care for patients with disabilities.

The 5-item Functional Limitations Index is an easily administered and easily interpretable measure that distinguishes patients along the disability continuum (least to most limited).

The strongest associations of specific functional limitations with self-rated health involved mobility and self-care, possibly reflecting acute illness sequalae.

The weakest associations involved sensory limitations, possibly due to long-term adaptation through accommodations.

Greater severity was associated with worse self-rated health, with the largest increase from no difficulty to somewhat difficult, possibly reflecting challenging adjustment to new health problems.

Limitations groups were most distinguished by being unable to or having a lot of difficulty walking or climbing stairs.

An estimated 12% to 30% of Americans have a disability,

 In clinical populations, greater disability is associated with higher costs and lower quality of life.

 There is a national commitment to improving the health and health care of persons with disabilities,

 who make up one of the largest groups of underserved adults in the United States.

 This requires a consistent, efficient means of identifying and monitoring the spectrum of community-dwelling individuals with disabilities.

Some widely used measures of disability are used to classify working-aged adults as unable to work because of a health condition (eg, CMS original entitlement code); such measures do not capture individuals with disabilities who are able to work or who developed disabilities after age 65 years. Other measures involve a clinical diagnosis (eg, the Sheehan Disability Scale

) or are limited to 1 or 2 domains (eg, SF-36 Physical Function

 score of mobility), limiting their usefulness in the general population.

Responding to the need for a standardized, broadly applicable measure of disability,

 a group of disability measurement experts developed the International Classification of Functioning, Disability and Health (ICF) to classify the consequences of disease and complement the 

 functioning, defined here as activity limitations, results from dynamic interactions among health conditions, body function and body structure impairments, participation in social roles, and environmental and personal factors. The ICF model focuses on functioning independent of health condition.

 For example, a patient with severe pain from a back injury and a patient with cerebral palsy may both have limited mobility, whereas a full-time student with depression may experience concentration limitations and a part-time worker with depression may not. The ICF model treats the first 2 people the same and the second 2 as distinct.

In this paper, we describe the development of a brief, easy-to-administer, and easy-to-interpret summary measure of patient functioning that health care organizations and providers can use to assess and monitor a wide spectrum of functioning independent of clinical diagnoses. In keeping with the ICF model, we focus on functional limitations independent of disease or condition. We considered only items in the 6-item Short Set of Disability Questions included in the National Health Interview Survey (NHIS) since 2010, as this measure was based on the ICF model.

 This short set of questions reliably and validly

 measures limitations associated with undertaking basic activities.

 It represents the majority of individuals (but not all) with functional limitations and assesses the most frequently occurring (but not all) domains of functional limitation.

We describe an empirically informed method of combining the items in this set to preserve distinctions among different types and severities of functional limitations while maximizing the correlation to self-rated health. Typically, individuals are classified as having a disability if they have at least 1 serious functional limitation.

 Another approach would be to classify individuals experiencing difficulty with at least 2 domains or activities of daily living as having a disability.

 Both of these dichotomous approaches ignore the possibility that the severity and different types of limitations may be associated with different levels of well-being and medical need.

Because the effect of a disability on well-being and medical need is likely to fall on a continuum, a continuous measure is needed to better capture the effect of disability and its severity, even if the continuous score is ultimately categorized for some purposes. One way to create a continuous measure is to count the number of limitations that a person reports

; although simple, a count assumes that each functional domain implies the same level of medical need or requires the same degree of intervention. Alternatively, functioning domains could be weighted to reflect their associations with health, using published weights (eg, those used in the World Health Organization [WHO] Disability Assessment Schedule

) or specific weights for the specific items in a given survey. For example, Altman and Bernstein

 constructed a weighted summary measure of disability based on 8 functions from the 2001-2005 NHIS, based on untested assumptions about the relative effects of different functional limitations and levels of severity.

Here we describe the development and application of a weighted summary measure of functioning with empirically derived weights reflecting the association between several functional limitations and self-rated health. Self-rated health was chosen as a criterion variable because it predicts health care utilization, morbidity, illness recovery, functional decline, and mortality,

 and because it is widely available in survey data. To inform our model, we addressed 2 questions: (1) Which limitations have stronger associations with self-rated health? and (2) How does the association of limitations with health vary by level of severity? Measuring the importance of severity in different patient functioning domains has practical clinical and policy implications for monitoring and improvement of quality of care.

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Data are from the 2014-2015 NHIS Adult Functioning and Disability (AFD) supplement, fielded with the Sample Adult module to a randomly chosen subset of all sampled adults (18,303 in 2014 and 16,939 in 2015).

We excluded 1818 AFD respondents (5.2%) who were missing all 6 WHO functional limitation items. We imputed a 0 (no difficulty) for 91 (0.3%) respondents who completed some but not all functional limitation items, as it is common for respondents to misunderstand long sequences of similar items as a “check-all-that-apply” format.

 Sampling weights incorporating study design and nonresponse were applied to all analyses, and robust variance estimation was used.

The NHIS-AFD module includes the following 6 functional limitation questions: vision (difficulty seeing), hearing (difficulty hearing), mobility (difficulty walking or climbing steps), communication (difficulty communicating), cognition (difficulty remembering or concentrating), and self-care (difficulty washing all over or dressing). All items had response options of no difficulty (level 0), some difficulty (level 1), a lot of difficulty (level 2), and unable to do (level 3). We sought to create a brief index based on these 6 well-validated items.

Constructing a Summary Functional Limitations Score

We developed a linear regression model that predicted self-rated health from the functional limitation items. The final model retained 5 functional limitation items scored linearly (0-3). Communication was excluded from the final model because an initial model that included all 6 items yielded a coefficient for communication that did not significantly differ from zero (

]). For each functional domain, we added a linear spline term that allowed the estimated difference between 

 (level 1) to differ from the estimated difference between 

Covariate-adjusted self-rated health scores were estimated using recycled predictions.

 The covariate-adjusted score is the expected self-rated health score for a given person if the person’s sociodemographic characteristics corresponded to those of the average person in the population. The coefficients of the functional limitations from the final model shown in 

 can be used to derive a predicted self-rated health score for every adult AFD respondent based on their self-reported vision, hearing, mobility, cognition, and self-care items (eAppendix B). We created a standardized (

 score) score, the Functional Limitations Index (FLI),

by subtracting the sample mean from each score and dividing the difference by the sample SD. The mean index score was 0 with an SD of 1. We classified respondents into 1 of 3 limitation groups based on the index scores. The cut points defining these groups were 1 SD apart on the index. Because the groups were meant to distinguish those with limitations, all cut points were below 0 (

). These cut points were selected to meet the potentially competing goals of having reasonably sized limitation groups (to ensure reliable subgroup estimates) and representing distinct levels of limitations. Most respondents were without limitations because the NHIS represents the general population. Respondents with no or very little limitation were assigned 0 or just above 0 scores. Although a small proportion of individuals with more limitations had large negative scores, there were no large positive scores; thus, the distribution of scores on the index is left-skewed.

As a preliminary validation and illustration of our approach, we compared the functional limitations and sociodemographic characteristics of the representative noninstitutionalized US adult population who were categorized as 

 per our FLI. We also determined which socio&shy;demographic characteristics were associated with which limitation group using bivariate and multivariate logistic regression models to predict being classified 

Predicting Self-rated Health From Functional Limitations

Negative coefficients for each functional limitation indicated a negative association with self-rated health (0-100 scale, where 0 represented poor health and 100 excellent health). Each retained functional limitation main effect coefficient was negative and statistically significant (

&thinsp;<&thinsp;.05). The strongest predictor of self-rated health was mobility limitation (—15 points for each additional severity level), followed by self-care (–10 points), cognition (–7 points), vision (–5 points), and hearing (–3 points).

, the negative association with functional limitations diminished (Figure 2). For example, a respondent with a single level 1 (

) mobility limitation was predicted to have a 15-point lower self-rated health score than those with no such limitation, but a respondent with a single level 2 (

) mobility limitation was predicted to have one 22 points (—15&thinsp;×&thinsp;2&thinsp;+&thinsp;8) lower, and a respondent with a single level 3 (

) limitation was predicted to have one 29 points (—15&thinsp;×&thinsp;3&thinsp;+&thinsp;8&thinsp;×&thinsp;2) lower. This means that the difference in quality of life between reporting 

 (15 points) was greater than the difference associated with reporting 

We describe an easy-to-interpret, patient-reported Functional Limitations Index that can be used to monitor care of patients along the disability continuum.

Marc N. Elliott, PhD

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