To examine the impact of paid and unpaid supplementary caregiving on preventable readmissions among Medicare home health beneficiaries with diabetes. 

 A retrospective cohort study with a home health episode as the unit of analysis. 

 We gathered multiple 2009 Medicare data and national datasets (eg, Area Health Resources File). We used propensity score matching based on beneficiaries’ predisposing, enabling, and health need factors to create matched cohorts for episodes solely assisted by paid supplementary caregivers versus those solely assisted by unpaid supplementary caregivers. We applied Cox regression on the matched cohorts to estimate the 30-day preventable readmissions, including diabetes-related conditions, heart disease-related conditions, chronic obstructive pulmonary disease, urinary tract infection (UTI), pneumonia, and dehydration.

Among beneficiaries (81,481) who received assistance several times during the day and night or several times during only the day, 7.34% were solely assisted by paid supplementary caregivers and 86.34% were solely assisted by unpaid supplementary caregivers. In the matched cohorts, we found that beneficiaries with paid supplementary caregivers had 68% higher hazards of readmission due to UTIs than those with unpaid supplementary caregivers.

The Medicare program is moving toward value-based purchasing and penalizing home health agencies with poor quality of care. It is critical for home health professionals to invite paid and unpaid supplementary caregivers to initiate care plans and assess their competence. Policies that support unpaid supplementary caregivers can also assist caregivers to care for their loved ones and prevent them from using expensive hospital resources. 

		Among beneficiaries who received assistance several times during the day and night or several times during only the day, 7.34% and 86.34% were assisted solely by paid and unpaid supplementary caregivers, respectively. 

		Beneficiaries with diabetes and paid supplementary caregivers had a 68% higher hazard of readmission due to urinary tract infections than those with unpaid supplementary caregivers. 

		The Medicare program is moving toward value-based purchasing. It is critical for home health professionals to invite paid and unpaid supplementary caregivers to initiate care plans and assess their competence. Policies supporting unpaid supplementary caregivers can reduce beneficiaries’ used of expensive hospital resources.

Approximately 3.4 million Medicare home health beneficiaries receive professional home health services that are paid for by Medicare.

 These beneficiaries, on average, received 19 visits from home health professionals during an episode of home healthcare, defined as up to 60 days, in 2014.

 A study based on a large home health agency in New York found that in addition to receiving services from home health professionals, 83.4% of home health beneficiaries received supplementary care from informal caregivers, of which 7% were paid, in order to meet their needs.

 These paid informal caregivers (hereafter, called paid supplementary caregivers) are paid through beneficiaries and/or families out of pocket or through other programs (eg, Medicaid). 

The interpersonal relationships between home health beneficiaries and unpaid informal caregivers (hereafter, called unpaid supplementary caregivers) and the interpersonal relationships between home health beneficiaries and paid supplementary caregivers are different. Unpaid supplementary caregivers are beneficiaries’ family members, relatives, and/or friends. They have solid relationships with their patients and understand what patients need without requiring much communication whereas paid supplementary caregivers are introduced by home health agencies and have no prior relationships with the beneficiaries and/or their families. Therefore, they need to learn how to meet patients’ needs through communication that is often difficult, given a high prevalence of limitation in cognitive function among Medicare home health beneficiaries.

Regardless of the differences in the interpersonal relationships, paid and unpaid supplementary caregivers provide emotional and social support and a wide scope of direct care to patients, ranging from necessary medical care (eg, medication dispensing and wound care) to assistance with activities of daily living (ADL) (eg, toileting and eating) and/or instrumental activities of daily living (eg, shopping and laundry).

 Given the scope of care activities, paid and unpaid supplementary caregivers are a vital part of a healthcare team and have a significant impact on home health beneficiaries’ health outcomes.

About 1.8 million Medicare home health beneficiaries received postacute care in 2012.

 In order to reduce readmission under the Affordable Care Act (ACA), the Medicare Payment Advisory Commission endorsed a home health financial incentive program for reducing preventable readmissions for home health agencies.

 Paid and unpaid supplementary caregivers play a critical role in caring for beneficiaries during the transition from hospitals to homes and postacute care,

 and can therefore impact the likelihood of preventable readmissions. Under a home health financial penalty program, investigating the potential difference in preventable readmissions of Medicare home health beneficiaries with these 2 types of supplementary caregivers is imperative for the home health industry.

The purpose of this study was to compare the likelihood of 30-day preventable readmissions among Medicare home health beneficiaries with diabetes who received care from unpaid supplementary caregivers with that of those who received care from paid supplementary caregivers. We chose diabetes because it is the most common disease (one-third) among Medicare home health beneficiaries,

 and diabetes significantly contributes to disabilities, such as blindness and amputation,

 that are associated with high demands for caregiving. 

We used Andersen’s Behavior Model of Health Services Use as a guiding framework for our statistical modeling.

 The Andersen model stated that healthcare utilizations and outcomes are a function of patients’ predisposing (eg, gender, race/ethnicity), enabling (eg, living alone), and health need factors (eg, ADL, cognitive disorders), as well as the characteristics of healthcare organizations and communities. This model fits our study well because evidence shows that the decision of whether to hire a paid caregiver depends on beneficiaries’ predisposing, enabling, and health need factors.

 Additionally, supplementary caregivers—one of the beneficiaries’ enabling factors&mdash;are part of the healthcare delivery team

 likely to affect beneficiaries’ health outcomes (eg, preventable readmissions).

 Lastly, the characteristics of organizations and communities are associated with beneficiaries’ health outcomes.

We applied Andersen’s framework in 2 stages: 1) we used beneficiaries’ predisposing, enabling, and health need factors to estimate the propensity of beneficiaries solely assisted by paid supplementary caregivers and those solely assisted by unpaid supplementary caregivers to construct a matched cohort

; and 2) we applied Cox regression on the matched cohort to estimate the difference in time to first 30-day preventable readmissions between these 2 groups, after controlling for the covariates. The conceptual framework is presented in the 

Based on the conceptual framework, we utilized several 2009 national datasets in the study. We extracted variables for beneficiary’s predisposing, enabling, and health need factors from several Medicare claim and assessment files discussed in our previous studies.

 (Operational definitions are provided in the 

 for review; eAppendices are available at 

.) The American Hospital Association (AHA) Annual Survey and Provider of Services (POS) file provide the characteristics (eg, ownership) of hospitals and home health agencies, respectively. Finally, the Area Health Resources File (AHRF) and Primary Care Service Area (PCSA) database from HHS provide the characteristics of communities where patients reside. 

We conducted a retrospective cohort study with home health episode for postacute care as the unit of analysis. We identified episodes of postacute care—defined as receiving home health services within 14 days of hospital discharge

—by merging the Medicare Provider Analysis and Review (MedPAR) that provides the date of hospital discharge and the Outcome Assessment Information Set (OASIS) that provides the start date of home healthcare, recommended by the literature.

In order to achieve our study aim, we used several criteria to select our study sample. First, beneficiaries must have received assistance several times during the day and night or several times only during the day. The OASIS B1 version provides the frequency of assistance (M0370),

 which allowed us to exclude beneficiaries who received assistance once daily or less, whose health outcomes are less likely to be significantly affected by supplementary caregivers. Second, beneficiaries could not be dual eligible because their family members could be paid supplementary caregivers under the Medicaid program. As discussed previously about interpersonal relationships, these family members as unpaid supplementary caregivers are quite different from the paid supplementary caregivers who are paid out of pocket by the beneficiaries and/or their families and do not have prior relationships to the beneficiaries and their families. We used the Master Beneficiary Summary File to determine dual eligibility and excluded these individuals from our study sample. Third, beneficiaries must solely rely on paid or unpaid supplementary caregivers (we provided detailed explanation in the subsection of “Paid and Unpaid Supplementary Caregivers”). Finally, beneficiaries must have been 65 years or older and enrolled in the fee-for-service (FFS) program.

Paid and unpaid supplementary caregivers. 

We used 2 assessment codes from the OASIS B1 version to identify beneficiaries with solely paid or unpaid supplementary caregivers. The assessment code M0360 provides information regarding which primary caregiver is taking the lead responsibility.

 The categories of primary informal caregivers in code M0360 include a spouse or significant other, daughter or son, other family member, friend, neighbor, member of the community or church members, or paid helper.

 The assessment code M0350A-M0350E (M0350A indicates that beneficiaries did not have an assisting individual) provides information on whether assisting individuals were paid (M0350B) or unpaid helpers (M0350C-M0350E).

Beneficiaries with solely paid supplementary caregivers were beneficiaries whose code for M0360 was paid helper and M0350B was coded as 1; namely, beneficiaries had paid helpers as primary informal caregivers and received assistance from paid helpers. Beneficiaries with solely unpaid supplementary caregivers were beneficiaries whose code for M0360 was unpaid helper (ie, a spouse or significant other, daughter or son, other family member, friend, neighbor, member of the community, or church members) and the code for M0350C, M0350D, or M0350E was 1; namely, beneficiaries had unpaid helpers as primary informal caregivers and received assistance from these individuals. Patients who had unpaid supplementary caregivers as primary caregivers and received assistance from paid helpers or vice versa were excluded because we are unable to determine the proportion of care provided by paid and unpaid supplementary caregivers. 

After identifying the qualified study sample, we selected a series of variables from beneficiaries’ predisposing, enabling, and health need factors and applied the propensity score matching to match beneficiaries with paid supplementary caregivers and those with unpaid supplementary caregivers.

 The predisposing factors included age, race/ethnicity, and gender. The enabling factors included whether or not beneficiaries lived alone. The heath need factors included ADL limitations, cognitive disorders, a presence of pressure or stasis ulcer, whether or not beneficiaries felt anxious or used a urinary catheter, and a dummy variable for beneficiaries with 3 or more comorbidities defined by Elixhauser and colleagues.

 The operational definition of the variables for matching was discussed in our previous studies (also presented in the eAppendix). 

The dependent variable was 30-day preventable readmissions. We applied the Agency for Health Research and Quality (AHRQ) Prevention Quality Indicator (PQI) software version 4.5 on the MedPAR to identify readmissions due to preventable conditions.

These conditions include diabetes with short-term complications (PQI 01), perforated appendix (PQI 02), diabetes with long-term complications (PQI 03), chronic obstructive pulmonary disease (COPD) or asthma in older adults (PQI 05), hypertension (PQI 07), heart failure (PQI 08), dehydration (PQI 10), bacterial pneumonia (PQI 11), urinary tract infection (UTI) (PQI 12), angina without procedure (PQI 13), uncontrolled diabetes (PQI 14), and lower-extremity amputation among patients with diabetes (PQI 16). Hospitalizations due to these conditions are preventable if patients are able to receive proper and timely care in the community.

 Home health beneficiaries receive professional home healthcare at their residences; with proper care and interventions after hospital discharge, readmissions due to preventable conditions identified by AHRQ are avoidable for our study population.

We excluded perforated appendix because none of the matched cohorts had readmission due to this condition. Because the frequency for some preventable conditions was small (eg, hypertension), we regrouped the preventable conditions based on the human organ system. The groups for preventable readmissions are: 1) diabetes-related conditions (PQI 01, PQI 03, PQI 14, and PQI 16), 2) heart disease-related conditions (PQI 07, PQI 08, PQI 13), 3) COPD, 4) UTI, 5) pneumonia, and 6) dehydration.  These preventable readmissions were finally defined as a time-to-first event in our analytical model.

 Given the conceptual framework, the covariates in the analytical model are the intensity of home health visits and the characteristics of hospitals, home health agencies, and communities. Hospital characteristics—extracted from the AHA Annual Survey&mdash;included hospitals’ ownership, teaching status, and system affiliation. Ownership of home health agencies was extracted from the POS files. Community characteristics, which were extracted from the AHRF, included a rural/nonrural county characterization and the number of primary care physicians and acute hospital beds per 1000 population at the county level. A log-transformed median household income at the zip code level was extracted from the PCSA. (Information regarding how to merge all datasets is available in the eAppendix.)

Approximately 4% of beneficiaries with diabetes had more than 1 home health episode for post acute care in 2009. To account for the cluster from the same beneficiary using multiple home health episodes, we applied a logistic regression with the Huber Sandwich Estimator on the selected variables from patients’ predisposing, enabling, and health need factors to estimate the propensity score. We then applied a caliper without replacement on propensity score to find the matched cohorts.

We compared the 3 factors of beneficiaries solely assisted by paid supplementary caregivers versus those solely assisted by unpaid supplementary caregivers, before and after matching, using χ

We applied a Cox proportional hazards regression model on the matched cohorts to estimate the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for paid supplementary caregivers, after adjusting for the covariates discussed previously. The Cox regression model was applied to 6 preventable conditions individually. Approximately 1% of the matched cohorts were from the same beneficiary; thus, we fit the model with standard errors adjusted for clustering on each individual beneficiary.

We identified 120,208 diabetes-related postacute home health episodes. Among them, 85.79% had primary caregivers (based on code M0360 in the OASIS) and 98% received assistance from paid or unpaid caregivers (based on codes M0350A-M0350E). After excluding unqualified episodes (previously discussed in the study sample), we identified 81,481 episodes in the unmatched study sample, with 70,350 (86.34%) and 5982 (7.34%) observations having solely unpaid and solely paid supplementary caregivers, respectively (5149 observations had both paid and unpaid supplementary caregivers). Before matching, patients’ predisposing, enabling, and health need factors statistically differed between beneficiaries with paid supplementary caregivers and those with unpaid supplementary caregivers (

 <.05). In our matched sample (5860 observations for each group), the difference in predisposing, enabling, and health need factors between these 2 groups became statistically insignificant. 

presents the descriptive analysis for these 3 matching factors between unpaid and paid supplementary caregivers before and after matching.

 presents the descriptive analysis for the dependent variables and covariates for the matched cohorts. Among readmission conditions, beneficiaries solely assisted by paid supplementary caregivers had about 0.46% higher readmissions due to UTIs than those solely assisted by unpaid supplementary caregivers (

 presents the HR and 95% CI from the Cox regression models for the matched cohorts. For UTIs, the HR is 1.68 (95% CI, 1.13-2.50); namely, beneficiaries with solely paid supplementary caregivers had 68% higher hazards of readmission due to UTIs than whose with solely unpaid supplementary caregivers. We did not find statistical differences for COPD, heart-related diseases, dehydration, diabetes-related conditions, and pneumonia. 

Although existing literature primarily focuses on volunteer family caregivers, there is a growing interest in studying paid informal caregivers due to the reduction in family size and rising number of women in the workforce. Lindquist et al (2011, 2012) found that paid informal caregivers had a good heart to help elderly patients, but were likely to have low health literacy and receive little training before they were assigned to patients’ homes.

 Among previous studies, one focused on Medicare home health beneficiaries found that beneficiaries with paid informal caregivers had poorer functional improvement than those with unpaid informal caregivers.

 Another study found that Medicare heart failure beneficiaries with paid informal caregivers had a higher risk of 1-year readmission than those with unpaid informal caregivers.

Among all preventable readmissions, we found that beneficiaries solely assisted by paid supplementary caregivers had a significantly higher hazard of readmissions due to UTIs than those solely assisted by unpaid supplementary caregivers. Given that prevention of a UTI requires good genital care and regular bladder emptying, this type of personal care necessitates a good relationship between patients and supplementary caregivers. Paid supplementary caregivers in our study were not beneficiaries’ family members; one study showed that patients with family caregivers felt safer and had higher satisfaction in their interpersonal relationship than those with paid nonfamily caregivers,

 which explains our significant findings for readmissions due to UTI. 

A previous study based on the information from code M0360 in the OASIS found that 83.4% of Medicare home health beneficiaries had primary informal caregivers.

 Based on code M0360, our study found that 85.79% of Medicare home health beneficiaries with diabetes had primary informal caregivers. Our study moved further and found that for the beneficiaries who received assistance several times during the day and night or several times during only the day, 7.34% were solely assisted by paid supplementary caregivers, 86.34% were solely assisted by unpaid supplementary caregivers, and 6.32% received assistance from both paid and unpaid supplementary caregivers. Our findings also showed that paid and unpaid supplementary caregivers heavily involved in caregiving had a significant impact on health outcomes for these frail, community-dwelling homebound beneficiaries. In order to obtain the full picture for the difference in quality of care provided by paid and unpaid supplementary caregivers, future studies focusing on other quality indicators, such as emergency visits and mortality, are recommended. 

Our datasets did not provide information on competence, stress, and support systems for paid and unpaid supplementary caregivers, which are likely to affect patients’ outcomes.

 Additionally, we focused on Medicare FFS home health beneficiaries with diabetes without identifying type 1 and 2 diabetes or insulin users. We also excluded beneficiaries with dual eligibility, those receiving assistance once daily or less, and those receiving care from both paid and unpaid supplementary caregivers. Furthermore, we used OASIS, which contains only data in the United States, to provide clinical and functional conditions for Medicare home health beneficiaries and allows researchers to merge with other Medicare claim data, such as MedPAR. Readers must also be aware of the debate concerning the reliability and validity of OASIS.

Moreover, we used preventable conditions defined by AHRQ. The conditions for preventable readmissions defined in the 3M Potentially Preventable Readmissions Grouping Software are slightly different from the preventable conditions defined by AHRQ.  Future studies based on the 3M Potentially Preventable Readmissions Grouping Software are encouraged. Also, the propensity score matching was based on the variables available in our data. There were missing variables in our study that should be taken into consideration when interpreting our findings. Our datasets do not have beneficiaries’ social/contextual factors, such as their education and whether they received social support from other organizations that were associated with the likelihood of hiring a supplementary caregiver and preventable readmissions. 

Finally, we used 2009 datasets. Several programs, such as the Hospital Readmissions Reduction Program introduced in 2013 under the ACA,

 are likely to have a spillover effect on preventable readmissions for Medicare home health beneficiaries. Future studies using recent data are recommended. 

Despite the study limitations, our findings have clinical and policy implications. For clinical implications, paid and unpaid supplementary caregivers are critical partners for home health professionals. Inviting supplementary caregivers to initiate care plans and assuring their competence to meet Medicare home health beneficiaries’ health needs becomes critical in the home health industry as the Medicare program is moving toward value-based purchasing and penalizing home health agencies with high preventable readmissions. For policy implications, our findings showed that our society heavily relies on unpaid supplementary caregivers to provide care to meet their loved ones’ health needs and that beneficiaries with unpaid supplementary caregivers had lower risks of readmission due to UTIs than those with paid supplementary caregivers. Policies that support unpaid supplementary caregivers are critical to help caregivers assist their loved ones and prevent them from using expensive hospital resources.

The authors thank Dr Sharon Homan’s contribution in the early stage of this project.

Department of Health Policy and Management  (HFC), College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR; Department of Biostatistics and Epidemiology, School of Public Health (SS), University of North Texas Health Science Center, Fort Worth, TX; Department of Health Policy and Management, School of Medicine, University of Kansas Medical Center (TP), Kansas City, KS.

 This study was funded by the Junior Faculty Seed Fund at the University of North Texas Health Science Center (UNTHSC). The views expressed in this publication are the views of the authors and do not necessarily reflect the views of UNTHSC. 

 The manuscript was accepted when Dr Chen was affiliated with UNTHSC and was published when Dr Chen was affiliated with the University of Arkansas for Medical Sciences. The remaining 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 (HFC); acquisition of data (HFC); analysis and interpretation of data (HFC, SS); drafting of the manuscript (HFC, TP, SS); critical revision of the manuscript for important intellectual content (HFC, TP, SS); statistical analysis (HFC, TP); obtaining funding (HFC); and administrative, technical, or logistic support (HFC). 

 Hsueh-Fen Chen, PhD, Department of Health Policy and Management, University of Arkansas for Medical Sciences 4301 West Markham St, Mail Slot 820-12 Little Rock, AR 72205. E-mail: hchen@uams.edu.

1. Report to the Congress: Medicare payment policy—chapter 9: home health services. Medicare Payment Advisory Commission website. http://www.medpac.gov/docs/default-source/reports/mar2015_entirereport_revised.pdf?sfvrsn=0. Published March 2015. Accessed February 3, 2017.

Articles

WEB EXCLUSIVE

Home health beneficiaries with diabetes using paid supplementary caregivers had 68% higher hazards of readmission due to urinary tract infection than those with unpaid supplementary caregivers.

Does Paid Versus Unpaid Supplementary Caregiving Matter in Preventable Readmissions?

We examined a wide range of performance outcomes after Lean methodology—a leading strategy to enhance efficiency and patient value&mdash;was implemented and scaled across all primary care clinics in a nonprofit, ambulatory care delivery system.

Using a stepped wedge approach, we assessed changes associated with the phased introduction of Lean-based redesigns across 46 primary care departments in 17 different clinic locations. Longitudinal analysis of operational metrics included: workflow efficiency, physician productivity, operating expenses, clinical quality, and satisfaction among patients, physicians, and staff. 

We used interrupted time series analysis with generalized linear mixed models to estimate Lean impacts over time. Projected outcomes in the absence of changes (ie, counterfactuals) were compared with observed outcomes after Lean redesigns were implemented, and mean differences were assessed using 95% bias-corrected bootstrap confidence intervals (CIs).

We observed systemwide improvements in workflow efficiencies (eg, 95% CI, 5.8-10.4) and physician productivity (95% CI, 3.9-27.2), with no adverse effects on clinical quality. Patient satisfaction increased with respect to access to care (95% CI, 15.2-20.7), handling of personal issues (95% CI, 2.1-6.9), and overall experience of care (95% CI, 11.0-17.0), but decreased with respect to interactions with care providers (95% CI, —13.4 to –5.7). Departmental operating costs decreased, and annual staff and physician satisfaction scores increased particularly among early adopters, with key improvements in employee engagement, connection to purpose, relationships with staff, and physician time spent working.

Lean redesigns can benefit primary care patients, physicians, and staff without negatively impacting the quality of clinical care. Study results may lead other delivery system leaders to innovate using Lean techniques and may further enhance support for Lean learning among public and private payers.

Lean is emerging as a leading strategy to enhance efficiency and patient value. Using a stepped wedge design, we assessed changes associated with the phased introduction of Lean-based redesigns across 46 primary care departments in a nonprofit, ambulatory care delivery system. 

		We observed systemwide improvements in workflow efficiencies, physician productivity, and patient satisfaction measures. 

		Operating costs decreased, and staff and physician satisfaction scores increased in key domains, including employee engagement and physician time spent working. 

		Study findings may lead other delivery system leaders to innovate using Lean techniques and may enhance support for Lean learning among public and private payers.

In the past decade, Lean methodology has emerged as one of the leading strategies for redesigning care to increase efficiency and patient value. Several prominent health system leaders have championed Lean’s potential contribution to reducing waste, enhancing quality, and facilitating patient and provider engagement.

 In the past several years, Lean has been highlighted by the National Academy of Medicine (formerly the Institute of Medicine) and the President’s Council of Advisors on Science and Technology as a powerful system approach.

Adapted from manufacturing, Lean is a change strategy with roots in continuous quality improvement. System leaders who adopt Lean provide frontline staff with training on how to use analytic tools and methods to identify and remedy sources of waste. In healthcare, waste is defined as anything that does not add value for patients or the process of delivering their care. Aided initially by Lean experts, staff members learn to pinpoint sources of waste and develop solutions to operational problems; these solutions often streamline work processes to enhance efficiency and workflow. Other aspects of Lean management include standardizing tasks to ensure reliability and coordination across roles and units, creating common baselines for measuring continuous improvements, and redefining roles to empower staff to improve quality and efficiency, as well as to accept shared responsibility for improving outcomes.

Despite widespread interest and growing use, only a few articles and a handful of books provide empirical details on systemwide Lean initiatives.

 These were ambitious programs lasting 5 years or more, driven by visionary leaders who were also highly effective managers. However, most research on Lean in peer-reviewed journals reports on the effects of specific Lean interventions on a few selected metrics, typically in 1 or a few sites. Also, most studies examine inpatient settings or integrated systems in which incentives are aligned for improving efficiency,

 but the implications of such research for fee-for-service (FFS) primary care are unclear. Most of the work on Lean in healthcare is anecdotal or relies on weak before-and-after study designs, and published studies rarely provide information on Lean’s effects on overall care quality and satisfaction among patients and providers. 

We report here on a longitudinal study using a wide range of efficiency, quality, and satisfaction measures to assess the implementation of Lean-based redesigns as they were spread across primary care clinics in a large FFS ambulatory care delivery system. Executive leaders envisioned the Lean initiative as a systemwide transformation, which began with the redesign of existing work spaces and care processes in all primary care departments and clinic locations throughout the system. These redesigns were intended to improve the work environment and process of delivering care among physicians and staff and to achieve tangible improvements in patient experiences of care. 

Specifically, redesigns that were introduced as part of the Lean initiative included: 1) standardization of medical equipment, supplies, and health education materials in patient exam rooms; 2) redesign of patient call center functions; 3) co-location of physician and staff care teams in a shared workspace; and 4) redesign of care team workflows. Standardized workflows included daily morning huddles to review patient schedules, agenda setting with patients by the medical assistant (MA) at the start of each office visit, and retrieval by the MA of all incoming items (eg, patient messages, lab/imaging results, prescription refills, referral requests) from the physician’s electronic inbox to address tasks as appropriate or to prepare them for the physician’s attention.

The implementation of Lean redesigns was formally staged: first, they were developed and implemented in 1 pilot clinic, then refined in 3 “beta” test clinics, and finally, scaled to 13 remaining clinics across the system. Each clinic location housed 1 to 3 primary care departments (Family Medicine, Internal Medicine, and/or Pediatrics) for a total of 46 primary care departments in which Lean redesigns were introduced. 

As indicated above, the implementation of Lean in primary care focused largely on efforts to improve workflow. Targeted improvements included reductions of physician time required for each patient encounter, with the aim to improve patient access to care. Reductions of other forms of waste during office encounters were also aimed to increase efficiency and productivity while reducing operating costs. It would not be acceptable, however, if Lean were to achieve these objectives at the expense of worsening clinical quality, patient satisfaction with services delivered, or physician and staff satisfaction. As the organization did not expend resources in developing major new metrics to monitor these outcomes, we relied on operational dashboards, billing and financial sources, scheduling systems, electronic health records (EHRs), and routinely administered surveys to study the effects of Lean implementation. The advantage in doing so is that we had longitudinal, uniformly collected measures across all clinics, both before and after Lean redesigns were implemented.

Workflow efficiency data were sourced from the EHRs and measured physicians’ timely completion of tasks associated with 4 types of patient encounters. These were the percentages of: 1) office visit charts closed within 2 hours of seeing the patient, 2) electronic reply to patient messages within 4 business hours, 3) prescription medications renewed within 4 business hours, and 4) telephoned patient care items resolved within 4 business hours. Physician productivity was measured by monthly work-relative value units (wRVUs) (restated to CMS 2012 valuation) per physician full-time equivalent (FTE) and per office visit. Departmental operating expenses, consisting mainly of nonphysician staff compensation and supply costs, per total RVU (tRVU), were calculated using data provided by the Finance department and adjusted for inflation using the Western Urban Consumer Price Index for medical care commodities.

 Clinical quality was assessed using pay-for-performance metrics routinely reported by the organization to the Integrated Healthcare Association (see the 

). Finally, physician, staff, and patient satisfaction data were collected by third-party survey administrators—the American Medical Group Association (physicians), Hay Group (staff), and Press-Ganey (patients). The physician and staff surveys are conducted annually, while Press-Ganey surveys are fielded to patients on an ongoing basis, with data aggregated by month.

To minimize the effects of turnover, metrics were based on physicians continuously employed during the study period. Continuous employment was defined as more than 5% FTE for at least two-thirds of the months both pre- and post-Lean implementation at a given clinic location. The number of qualifying months depended on when the clinic implemented Lean, with post-Lean periods ranging from 4 to 25 months. A total of 328 primary care physicians were included during the study period (2011-2014).

We assessed the effects of implementing Lean redesigns using an interrupted time series. In such analyses, an outcome is monitored over time and may have “interruptions” following an intervention that can be modeled in a segmented regression.

 Because Lean was deployed in phases, the data were analyzed using a nonrandomized stepped wedge design with 1-way crossover; thus, the observation period began with all locations initially without exposure to the intervention, with sequential training and “crossover” of clinics from control to intervention groups, until all clinics were exposed by the end of the study period (

For most analyses, generalized linear mixed models were used with the physician-month as the unit of observation. Fixed effects included the terms used for the segmented regression and potential confounders, including covariates such as physicians’ scheduled clinic hours, the mean age of patients on a physician’s panel, and the proportion of new patient visits. The nested structure of physicians working in departments within clinic locations was accounted for using random effects in the models. The autocorrelation of repeated measures over time was accounted for using a first-order autoregressive R-side covariance structure. 

A projected value for each performance outcome in the absence of Lean was estimated as of the end of the observation period, adjusting for secular trends and the potential confounders described above. This counterfactual was compared with the observed values at the end of the study period after Lean redesigns had been implemented in all clinic locations. To determine whether the mean difference was statistically significant, a 95% bias-corrected bootstrap confidence interval (CI) was calculated using 2000 samples.

Annual staff and physician satisfaction survey data were provided by vendors at aggregated levels to protect respondent confidentiality, so these data were analyzed at either the clinic or the overall system level. As physician satisfaction data were available at the clinic level, results were grouped by the 3 phases of Lean implementation. All data management and statistical analysis for all metrics were conducted using SAS version 9.3 (SAS Institute, Cary, North Carolina). 

 displays comparisons of several performance outcomes. Across the system, provider workflow efficiency improved in the majority of metrics examined. Timely office visit chart closures attributable to Lean implementation increased by roughly 10% (95% CI, 0.9%-8.2%), from 51.2% to 56.2%. Similar results were observed for prescription medications renewed and for telephone encounters resolved within 4 business hours; there was no statistically significant change in responsiveness to patient messages. One measure of physician productivity—monthly wRVU production per physician&mdash;increased approximately 5%, from 252.3 to 265.0, while wRVUs per office visit remained unchanged. Total operating expenses and its major components (ie, nonphysician compensation, drugs and supply costs) were lower per department, although these reductions were not statistically significant.

Overall, patient satisfaction increased from 49.1% to 63.2% following implementation of Lean redesigns. Patient perceptions of access to care via appointments, phone calls, online messages, and referrals increased markedly by 48.4%. Patient satisfaction with the handling of personal issues regarding safety, privacy, and exam room cleanliness also improved by nearly 8%; however, patient satisfaction with interactions with care providers decreased by approximately 11.6%. 

A range of clinical quality metrics were examined to determine whether changes, particularly in efficiency or productivity, adversely affected quality of care. Univariate analyses were first conducted on a range of pay-for-performance metrics, as noted previously, with significant differences initially found in metrics involving coordinated diabetes care, cervical cancer screening, chlamydia screening, and meningococcal immunization among adolescents. After adjusting for potential confounders, statistically significant differences were observed only among diabetes care metrics, where glucose and cholesterol control and rates of nephropathy screening all increased anywhere from 3.4% to 5%. Meningococcal vaccination among adolescents decreased. 

There is a yearly survey of all clinical and nonclinical nonphysician staff members to assess their experience of work. As these data were provided on an annual basis and only in aggregate across the organization, a counterfactual was not generated for these results (shown in the eAppendix). Overall staff satisfaction in all primary care clinics, as measured by the composite score, increased between surveys conducted at baseline and after Lean was implemented. Across specific domains, nearly all dimensions of primary care staff satisfaction improved, with the largest being in credible leadership, followed by the domains of employee engagement, connection to purpose, growth and development, healthy partnerships, and empowerment and autonomy. All domains, except for pay and benefits, showed improvements following implementation of Lean redesigns. 

Physician satisfaction data were available by clinic location, allowing more detailed assessments based on the clinic’s phase of Lean implementation (

). When examined in aggregate, ignoring phase, physician satisfaction scores remained virtually unchanged. However, overall physician satisfaction in the pilot and beta clinics increased by approximately 2%, but decreased by nearly 4% in the last clinics to implement Lean redesigns. Physicians in the pilot phase reported improved satisfaction in areas targeted by Lean, including leadership and communication, perception of administrators, time spent working, and relationships with staff members. Physicians in the beta clinics did not report improved perceptions of leadership or administrators, but reported improved relationships with staff, available resources, time spent working, and quality of care. Physicians in the remaining clinics displayed smaller changes in Lean-targeted areas, with slight decreases in satisfaction with leadership and communication and time spent working, as well as slight increases in perceptions of administration and quality of care. A summary of all study results is displayed in 

Process Changes Underlying Improvements in Efficiency, Productivity, and Patient Satisfaction

Using a stepped wedge design, we assessed the changes associated with the phased introduction of Lean redesigns in 17 primary care clinics at a nonprofit, ambulatory care delivery system. Study findings are consistent with the intent of changes that were implemented as part of the organizational initiative. Although Lean was initiated with strong support from executive leadership, the Lean approach is to solicit extensive input from frontline physicians and staff to identify wasted resources, including excessive time spent by staff and physicians in delivering patient care. In this organization, the Lean approach led to a focus on creating new workflows and reassigning tasks among all care team members. Chief among these changes was a new role for medical assistants who, as “flow managers,” maintained a constant flow of work by directly addressing tasks they were trained to handle and preparing all other patient care items for the physician to address. Such changes aimed to create a just-in-time approach to workflow, including real-time completion of visit documentation and avoidance of work task “batching.”

These workflow changes resulted in tangible improvements among the examined metrics on efficiency and may have also increased productivity as measured by physician wRVUs generated per month. Physician compensation was largely based on wRVUs throughout the study period (ie, the financial incentive for productivity was always present), so workflow efficiencies likely facilitated physicians’ abilities to accommodate more patients into their clinic schedules. The finding of increased efficiency and productivity is consistent with previous reports in other settings where the “system” was capacity constrained. For example, Lean intervention in emergency departments led to increases in physician wRVUs and improved workflow, as measured by reduced patient wait times and the proportion of patients leaving without being seen.

We observed significantly higher patient satisfaction in the domains of access to care, handling of personal issues, and overall satisfaction. Improvements in perceived access aligned with our objective findings on workflow efficiencies. Patient satisfaction with the handling of personal issues, which included the cleanliness of the practice, protection of patient safety and privacy, and sensitivity to patient needs, reflected Lean standardization activities that focused on the proper ordering and maintaining of patient exam rooms and all spaces where patient care is provided. However, patients reported lower satisfaction with their interactions with care providers. This domain consisted of survey items assessing the perceived concern for patient questions or worries; explanation of medical problems, medications, and follow-up care; and time spent with the patient. 

Decreases in patient satisfaction in this domain may be related to the same work design factors that enabled provider efficiency, such as just-in-time workflows, which encourage physicians to move more quickly through each patient visit. Using medical assistants to offload physician work also impacts the patient-physician relationship and may result in less satisfying interactions. For example, with the new practice of “agenda setting” to streamline visits, patients are asked by the MA to identify priority concerns, with less urgent matters to be addressed at a future scheduled appointment. This procedure substitutes MA for physician time, implicitly places limits on the current office visit, and depends substantially on the skill of the MA in negotiating patient concerns. Patients may perceive that this agenda setting by the MA renders their care impersonal and unresponsive to the full range of their concerns.

Observations in Other Areas: Clinical Quality, Staff and Physician Satisfaction 

Clinical quality improvements were observed in measures of diabetes care, likely reflecting specific Lean redesigns (eg, co-location, shared workflows) that aimed to improve communication and coordination between care teams. The quality improvement literature frequently cites enhanced communication between care team members, use of multidisciplinary teams or nonphysician staff, and expansion or revision of professional roles as the greatest facilitators for improving diabetes outcomes relative to other strategies.

 The only quality area in which a decline occurred was adolescent immunization for 

. However, this decline was likely related to changes in immunization guidelines that coincided with the study period. Beginning in 2011, new clinical guidelines recommend a meningococcal booster between ages 16 and 18 years following the initial injection originally given once to 11- to 12-year-old adolescents.

 Thus, over time, the criteria for completing these immunizations have become more stringent and potentially more difficult to achieve.

The greatest improvements in nonphysician primary care staff satisfaction occurred in perceptions of credible leadership, employee engagement, growth and development, connection to purpose, empowerment and autonomy, and overall staff satisfaction. This pattern is consistent with studies of Lean in hospital settings, which show positive effects on workforce satisfaction and highlight the benefits of increased participation of frontline staff in designing and implementing standard workflows.

 By participating in redesign efforts, staff members gain a better understanding of daily work processes relevant to both themselves and others and the rationale for needed changes and improvements. Ideally, employees become problem solvers rather than passive recipients of operational mandates, a role change that can be both empowering and rewarding.

Satisfaction increases among physicians in clinics that implemented Lean during the first 2 phases, coupled with the decrease in satisfaction among physicians in the final phase of implementation, warrant further investigation. When the post-Lean physician satisfaction survey was administered, the pilot and beta clinics had accumulated 1 to 2 years of experience with Lean redesigns, whereas the remaining clinics had launched their redesigns as recently as 4 months prior to being surveyed. It is possible the decline in satisfaction among the physicians in this last phase reflected a period of transition and adjustment. Some disruption and discomfort accompanied all clinics’ early months of implementation, including spatial redesigns that required physicians to relinquish their offices and relocate next to their MA care team partner, as well as new workflows that called for management or delegation of tasks to nonphysicians. Benefits from such changes may only become apparent after several months of adjustment and experimentation. 

Another explanation involves differences in physician engagement with Lean implementation. Those in the last phase of clinics were much less involved with actual planning and development of Lean redesigns.

 Active involvement in identifying sources of waste and redesigning workflows—a hallmark of the Lean improvement approach&mdash;may be gratifying in itself. Research on participative decision processes and the use of participation in change management suggests that such direct engagement leads to greater commitment to new work initiatives.

 It may, therefore, be that the process of developing a Lean workflow is as important for its acceptance as the content of that workflow itself.

The observed variations in physician and patient satisfaction point to areas for further research. First, data from longer post

Lean time periods than those reported here are needed to assess more complete effects of Lean in all clinics and whether reactions to the Lean redesigns remained stable over time.

 Second, by combining our findings with qualitative data or more fine-grained quantitative data, we can examine specific physician- and patient-level perceptions underlying their responses to the closed-ended satisfaction questions reported here. Third, availability of nonphysician satisfaction data at the clinic level would allow comparison of primary care staff satisfaction results across implementation phases (ie, pilot, beta, all remaining clinics). Last, a logic or program model identifying the steps and underlying organizational and social psychological mechanisms would help specify important stages of the Lean initiative—such as staff and leadership participation in workflow redesign, management communication of expected practice changes, practice-level coaching, and staff implementation of redesigns&mdash;at which quality, efficiency, and satisfaction outcomes could then be assessed.

 Such a model could be used as a guiding framework for an in-depth process evaluation of the intermediate results at each step of the change process.

Organizations ranging from private hospitals and physician practices to government health systems and agencies, including the Veterans Health Administration and CMS, are now implementing Lean to improve efficiency and value. Despite its growing popularity, few studies have addressed the concerns of healthcare leaders and practitioners, including whether Lean can help delivery systems identify waste and redesign care processes to enhance multiple performance outcomes simultaneously—particularly in FFS ambulatory settings where the vast majority of healthcare is delivered. Moreover, questions remain as to whether productivity gains achieved through Lean will undermine quality and worsen already high levels of discontent among primary care providers. Our findings indicate that an ambulatory care system can develop and scale Lean redesigns with largely beneficial consequences. These results may lead other delivery system leaders to innovate using Lean management techniques, and, if the findings replicate in other systems, they may further enhance support for Lean learning among public and private payers. Additionally, our study underscores the need for careful analysis of both desired effects and the potential unintended consequences of implementing Lean to improve value in healthcare.

The authors would like to thank Mr Eric Wong, MS, and Ms Maayan Yakir, BS, for analytic support and assistance with the preparation of this manuscript.

 Palo Alto Medical Foundation Research Institute (DYH, HSL, MCM), Mountain View, CA; Agency for Healthcare Research and Quality (MIH), Rockville, MD.

 The research reported in this paper was funded by the Agency for Healthcare Research and Quality under its ACTION II contract HHSA2902010000221, Task Order 2. The views expressed in this paper are solely those of the authors and do not represent any US government agency or any institutions with which the authors are affiliated.

 Drs Hung and Luft and Ms Martinez are employed by the study organization, but the Research Institute exists as an independent unit and all research is conducted without being subject to review and approval by the organization. Dr Harrison reports 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 (DYH, MIH, HSL); acquisition of data (DYH); analysis and interpretation of data (DYH, HSL, MIH); drafting of the manuscript (DYH, MIH, HSL, MCM); critical revision of the manuscript for important intellectual content (DYH, MIH, HSL, MCM); obtaining funding (DYH); administrative, technical, or logistic support (MCM); and supervision (DYH, HSL). 

MANAGERIAL

Lean redesigns in primary care improved workflow efficiencies, physician productivity, and overall satisfaction among patients, physicians, and staff, with no adverse effects on clinical quality.

Scaling Lean in Primary Care: Impacts on System Performance

To explore accountable care organizations (ACOs) as they develop in the private sector, including their motivation for development, perspectives from consumers regarding these emerging ACOs, and the critical success factors associated with ACO development.

Comprehensive organizational case studies of 4 full-risk private sector ACOs that included in-person interviews with providers and administrators and focus groups with local consumers.

Sixty-eight key informant interviews conducted during site visits, supplemented by document collection and telephone interviews, and 5 focus groups were held with 52 consumers associated with the study ACOs.  

We found 3 main motivators for private sector ACO development: 1) opportunity to improve quality and efficiency, 2) potential to improve population health, and 3) belief that payment reform is inevitable. With respect to consumer perspectives, consumers were unaware they received care from an ACO. From the perspectives of ACO stakeholders, these ACOs noted that they prefer to focus on patients’ relationships with providers and typically do not emphasize the ACO name or entity. Critical success factors for private sector ACO development included provider engagement, strategic buy-in, prior experience managing risk, IT infrastructure, and leadership, all meant to shift the culture to a focus on value instead of volume. 

These organizations perceived that pursuing an accountable care strategy allowed them to respond to policy changes anticipated to impact the way healthcare is delivered and reimbursed. Increased understanding of factors that have been important for more mature private sector ACOs may help other healthcare organizations as they strive to enhance value and advance in their ACO journeys.

Improving the understanding of factors that have been important for more mature accountable care organizations (ACOs) can help organizations that are developing and continuing to evolve as ACOs. Our study is among the first to explore private sector ACO development, and we found the following: 

		An adequate information technology infrastructure enabling timely access to meaningful data was essential for ACOs to manage and monitor quality and outcomes. 

		Consumers generally lacked knowledge of ACOs and were unaware they were part of an ACO. 

		Physician engagement and strong organizational leadership to shift culture from a volume focus to a value focus were crucial to ACO development.

The introduction of the accountable care organization (ACO) as a form of healthcare delivery and payment system reform supported by the Affordable Care Act is hoped to improve quality of care and reduce healthcare costs by aligning the incentives of physicians, hospitals, and other clinicians and healthcare organizations.

 the ACO label has been defined as “groups of providers who are willing and able to take responsibility for improving the overall health status, care efficiency, and healthcare experience for a defined population.”

Although early adopters of the ACO model commonly operated under the CMS Medicare Shared Savings Program (MSSP) or the Pioneer ACO Program,

 recent market entrants have included ACOs in the private market.

 These private sector ACOs have goals similar to those of the MSSP and Pioneer models in that the contracts with payers include both cost and quality targets; however, they have more flexibility with respect to contract terms relative to Medicare ACOs.

 For example, private sector ACOs are able to develop multiple contracts with multiple payers to extend coverage across broader, commercially insured populations and those including children. In addition, private sector ACOs can have very different quality reporting requirements and financial relationships with, and incentives for, providers—within the boundaries of antitrust considerations&mdash;relative to Medicare ACOs.

Early reports have suggested that provider groups decide to become ACOs for a variety of reasons, including the desire to save money and improve care,

 and due to a sense of inevitability about the direction of health reform.

 However, few studies to date have focused on private sector ACOs

—particularly those that bear downside risk&mdash;and none have asked both ACO stakeholders and consumers affiliated with these organizations about their perspectives. Because private sector ACOs are not bound by the federal regulations and reporting requirements of Medicare ACOs, they have the potential to develop quickly and for different reasons and in different environments than Medicare ACOs. We undertook this study to improve our understanding of ACO development and implementation in the private sector, which is one of the most rapidly growing sectors of the ACO market.

 Our research objective was to explore the rationale for private sector ACO development, the perspectives consumers had about these emerging ACOs, and the critical success factors interviewees associated with private sector ACO development.

We conducted 4 comprehensive case studies of private sector ACOs that assumed full risk from different geographic regions of the United States. Unlike most studies that focus only on the perspectives of ACO providers and leaders, we also focused on the consumer perspective. We elected to use a multiple case study design

 because of the exploratory nature of our study and our desire to learn from multiple stakeholders to improve our understanding of a relatively new organizational form.

We selected ACOs for study that both operated in the private sector and assumed financial risk for substantial portions of the populations they served. Organizations had to self-identify as an ACO and, consistent with common definitions of ACOs,

 they had to involve groups or networks of providers that assume responsibility for the cost and quality of care for defined populations, including some level of downside financial risk. We also maximized the variability of our sample to include ACOs that differed along several important dimensions, including structure (ie, physician-owned vs physician-hospital organizations), size, geography, and population (ie, pediatric vs general). Summary information about our 4 ACOs is included in 

We collected data through site visits during the spring and summer of 2013. Across the 4 sites, we held a total of 68 in-person or telephone key informant interviews. In addition, we held 5 focus groups comprising 52 consumers (

). Our data collection process included a concomitant assessment of interview and focus group transcripts and discussion of preliminary findings to permit probing for new concepts and to ensure that we reached saturation in data collection consistent with standards for rigorous qualitative research.

Site visits and key informant interviews.

 During 2- to 3-day visits, we conducted semi-structured interviews with ACO stakeholders from 4 main areas: 1) organizational leaders (eg, chief executive officer [CEO], chief operations officer, chief marketing officer); 2) administrators and staff members who could provide insight about the ACO decision making and development process (eg, informants from business development, strategic planning, finance, contracting, marketing, external affairs, information systems, quality and performance improvement); 3) administrators, staff members, and clinicians involved in ACO operations (eg, ACO administrator, contracts manager, practice manager, medical director); and 4) physicians affiliated with the ACO.

 To ensure consistency in our data collection, we used 2 standard interview guides: 1 tailored for providers and 1 for administrators and staff. The interview guides consisted of multiple-question domains, including: 1) History and Context; 2) ACO Implementation; 3) Consumer Involvement; and 4) Critical Success Factors. These interview guides were tested in a pilot study of a different ACO to improve question clarity and refine the guides prior to our study site visits. Most interviews were conducted in person and lasted 30 to 60 minutes. All interviews were recorded and transcribed verbatim. 

Each focus group was conducted using a standard guide that included open-ended questions exploring consumers’ perspectives about ACOs and their own healthcare. A study investigator moderated each of the focus groups, with an additional investigator present as a co-moderator. One focus group was conducted at 3 of the ACO sites and 2 were conducted at the fourth site to capture consumer perspectives from both the urban and rural markets this ACO served. Each focus group comprised 7 to 16 participants who lived in the ACO market and interacted with the ACO as patients or parents of patients of ACO providers. Sessions lasted 90 minutes and participants were given a $50 gift card as a token of appreciation for their time. All sessions were recorded and transcribed verbatim.

 For each participating ACO, we also requested key documents related to ACO development. Examples of these documents included the ACO strategic plan, communication vehicles, ACO progress reports, board presentations, and annual reports. 

Our analytic approach included using both inductive and deductive methods to analyze the 1158 pages of transcripts from our interviews and focus groups in an iterative process. After each site visit, the research team first compiled a “Site Visit Summary” that included summary impressions. This initial analysis was helpful for conceptualizing findings and themes and provided insight into more focused inquiry.

Our second analytic step involved coding the interview transcripts to break this qualitative data into smaller, meaningful segments for analysis.

 We developed an initial set of codes based on our interview guides and emergent themes from the site visit summaries; we then summarized these codes in a “coding dictionary” that included detailed definitions to ensure consistent application of the codes.

 This preliminary coding dictionary was further refined as themes and patterns emerged in the data. Three members of the research team then coded the data and, throughout the coding process, conducted cross-checks of the coded data to ensure that codes were consistently applied.

 Data were coded and managed using a qualitative software program, Atlas.ti (Scientific Software Development GmbH; Berlin, Germany).

The third step of analysis we conducted for this paper involved examining the codes of “motivations” and “critical success factors” to inform our understanding of private sector ACO development. We used an inductive approach to identify themes and patterns in the coded data, first focusing on patterns within cases (ie, study sites) and then evaluating whether these were replicated across cases, paying particular attention to any differences in the contrasting cases (eg, in pediatric ACOs vs others).

 At each stage of this coding and analysis process, we also sought and considered disconfirming evidence

 to ensure the robustness of our findings. 

When asked about their motivation for developing a private sector ACO, interviewees across the study sites noted 3 reasons, explained here and with supporting evidence presented in 

The first was to lower costs while improving care quality. As one ACO executive summarized, “We came together with a fundamental mission of improving cost of care and improving quality of care.” Second was to improve coordination of care and population health management. This motivation was acknowledged even in the face of understanding that all incentives were not aligned. As one interviewee explained, “You can’t see either the hospital or the health plan or the physician group or whoever the players are as adversaries, even though your incentives might not always be completely aligned. You’ve got to find a way, a common ground to do what’s right for the patient.” The third common motivator had to do with a sense of inevitability given the direction of health reform. One interviewee reflected, “I think if you polled hospital CEOs, 90% of them are going to say, ‘We are on the path to an accountable care organization.’ …there is now a general belief and acceptance in that this is the right thing to do in that whether you’re an insurance company, a provider, a hospital, you are moving in this direction.” Although individual interviewees at the sites noted other reasons, such as the desire to consolidate market power so that the organization could “leverage the best payments” or to be “a market leader” in this new model of care, these comments did not reflect the collective sentiment across interviewees and institutions. 

Consumers’ Perspectives about ACO Development

Across the focus groups, none of the consumer participants was familiar with the terms “accountable care organization” or “ACO,” nor were any individuals aware they were part of an ACO. This became apparent during each session when no participants provided answers to the questions, “Prior to coming here [to this focus group session], did you know what an Accountable Care Organization was?” and “Did you know that [this organization] was an ACO?” As a result, participants’ answers to open-ended questions instead reflected their experience with their own physicians or a named hospital and not the ACO entity. Consumers’ lack of familiarity with the ACO model had been predicted by our sites’ key informants, who consistently indicated that their ACO outreach efforts purposely did not include branding the ACO to consumers, nor promoting the ACO as something new and different. 

Critical Success Factors for Private Sector ACO Development

When asked what interviewees believed to be critical success factors for development and implementation of an ACO model in the private sector, responses could be classified into 5 main categories: 1) physician involvement, 2) strategic buy-in to the ACO vision, 3) information technology (IT) infrastructure and data, 4) experience and understanding, and 5) leadership. These factors are explained below, with additional supporting evidence provided in 

Provider buy-in and engagement were mentioned often and across all 4 sites, and were perceived to be the key factors limiting whether the ACO model could be implemented. As one senior executive explained, “The providers actually have to buy into it…Because if they don’t, you can provide reports and have meetings and do everything that you want to, but if the providers haven’t actually bought into it, it’s an uphill battle.” Engagement and involvement of physicians in ACO governance were also important, with one interviewee summarizing this factor as “absolutely bringing physicians to every point in the table.”

 Strategic vision and buy-in at the organizational level (ie, moving beyond provider-level buy-in) involved commitment from and across the organization rather than only individuals (providers) engaging with the ACO concept. One senior executive reflected, “I think you really need that strategic buy-in … the fact that the healthcare system needs to take some responsibility to figuring out how to provide more effective, affordable, and efficient care that still meets the needs of the patients.” Interviewees at other sites made similar comments about the importance of buy-in to help ensure support for required investments and the effort required to support cultural changes for the organization. 

 IT was commonly recognized as critical. As summarized by one executive, “Well, an IT infrastructure is key. You can’t control what you can’t measure. You need to define your quality and utilization parameters. … You’ve got to build the structure right. You’ve got to have adequate data from your health plans so that you can figure out what you need to figure out.” Beyond infrastructure, interviewees also recognized the need for the data that the systems supported. As one ACO director noted, “You can have physicians who are bought into it and are engaged in it, but they need to know how they’re doing, and they want to know how they’re doing, right?” 

 At all 4 sites, interviewees explicitly noted that past experience with risk was contributing to their successful implementation of the ACO model. One director commented, “What we find is that with the experience that we’ve had previously, we’re sort of anticipating what’s coming down the road. And I think that’s positioning as well, as we move forward. Otherwise, you’re spending a lot of time scrambling.” This understanding was also valued at another site, as an interviewee explained, “We felt that we could take risk for a population that we knew very well and that we could manage the health of that population within the fiscal constraints.”

 Comments emphasizing leadership as a success factor reflected the importance of the leadership function overall, as well as the need for leadership of the cultural changes required to implement the ACO model. One manager commented, “The cultural shift is pretty huge to move from fee-for-service. And that whole mindset and getting people to really be able to embrace that.” Leadership was noted to be especially important as the ACO model evolved. As one executive explained, “And even when everybody’s on board it’s tough, because there are winners and losers, and people’s roles are going to change, how they’re perceived to perform, their incentives will change. It’s a tough cultural change even when everybody’s on board. So if there’s any hesitation by an important part of the organization, it’s going to be tough.” 

The ACO concept has spread beyond the Medicare ACO initiatives, as provider and payer organizations in the Medicaid and private markets are developing ACOs to serve these populations. Lacking clearly defined models for private sector ACOs, however, payers and providers in these spaces are left to develop their own models. Furthermore, because most research to date has focused on ACOs developed in the MSSP and Pioneer ACO programs, there is little information about how and why ACOs are developing to serve other populations. In this paper, we have begun addressing this gap by providing early insights from the development and implementation of 4 ACOs serving privately insured and Medicaid populations. 

Although private markets and populations may vary from those served by the MSSP and Pioneer programs, our findings suggest that many of the motivators for developing private sector ACOs are similar. The need to control the rising costs of care while simultaneously improving the quality of care, the need to develop capabilities to manage population health, and a sense of the inevitability of healthcare reform were the primary motivations for ACO development espoused by our interviewees. In a sense, providers want to “do the right thing,” and they perceive the ACO model as providing an opportunity to do that. This mix of motivators suggests both push and pull forces at work. The sites we studied are moving away from legacy approaches to healthcare financing and delivery, including fee-for-service and fragmented, siloed care, which they feel are no longer sustainable nor desirable. At the same time, these organizations are moving toward new contractual and organizational arrangements that they perceive both as inevitable and as allowing them to improve the health of populations.

 In short, healthcare reform efforts to better align incentives with quality and population health goals for traditional Medicare populations appear to be affecting transformations more broadly. 

Our findings that consumers lacked knowledge of ACOs and were unaware that they were part of an ACO are notable, especially given growing emphasis on the engagement of patients in their own care.

 Providers and payers involved in developing ACOs are investing considerable time, money, and effort to transform the healthcare system, and yet the parties with arguably the greatest stake in that transformation are neither informed of nor empowered to shape these efforts. Given the linkage between patient behaviors and many health outcomes, we expect there are numerous opportunities to involve consumers in ways that will improve the abilities of ACOs to accomplish their goals. For example, there may be opportunities to provide incentives, including financial ones, to engage patients or to provide physicians training in patient engagement.

 Other opportunities may be to include patient representatives in governance, as required in the MSSP and Pioneer ACO programs, but not required in any private sector ACO model of which we are aware. 

Private sector accountable care organization development has been motivated by perceived opportunities to improve quality, efficiency, and population health, and the belief that payment reform is inevitable.

March 2017

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