In this reply to the commentary, “A Call for a Statewide Medication Reconciliation Program,” published in the October 2016 issue of 

®, authors note that although they agree with the authors’ assessment of the problem, they believe there is a proven and scalable solution to improve medication reconciliation that is already available to, and used by, clinicians. 

In the commentary, “A Call for a Statewide Medication Reconciliation Program,” published in the October 2016 issue of 

, Drs Askin and Margolius present recommendations for statewide medication reconciliation programs (SMRPs) aimed at improving current medication reconciliation processes.

 Incomplete or inaccurate data found within patient medication histories can pose serious safety risks, and we agree with the authors’ assessment that improvements to medication reconciliation processes are necessary.

Nevertheless, the establishment of SMRPs, by expanding current prescription drug monitoring programs (PDMPs), may not be the most practical or expeditious solution. First, as noted by the authors, health systems might be disinclined to invest to make their electronic health records (EHRs) interoperable with SMRPs. Further, PDMP datasets have not been universally integrated in physician EHRs or pharmacy management systems, and this lack of seamless integration compromises physician accessibility and usability, thereby negatively affecting their utilization.

 Second, the absence of a single nationally adopted and accredited standard, common vocabularies, and uniform data formats by PDMPs will result in EHR and pharmacy vendors potentially needing to certify and implement 50 different SMRPs—a cost-prohibitive and resource-intensive endeavor.

 Finally, as noted by the commentary authors, prescribers and pharmacies who serve patients in multiple states may have to register with each SMRP separately to access its data, which would result in additional workflow burdens. EHR and pharmacy integration challenges across SMRPs can thus negatively impact clinician usability and subsequently reduce the efficiency of the overall medication reconciliation process.

An alternative to a mandated SMRP is to employ the existing national network of connected pharmacies, EHRs, and pharmacy benefit managers (PBMs) that enables providers to electronically access—through their EHRs&mdash;medication lists for their patients. This national network infrastructure utilizes the medication history transaction standard that is approved by the National Council for Prescription Drug Programs (NCPDP), adopted by CMS, and required under Meaningful Use stage 3 criteria.

 This solution has already been implemented and is operated by the Surescripts Health Information Exchange; it facilitates a single standard EHR certification and integration process, thereby avoiding the aforementioned state-by-state variations in portal layouts, data formats, and/or user experiences. In addition, this solution is seamlessly integrated and accessed by clinicians within existing EHR workflows and has been increasingly adopted and utilized by them over the past 10 years.

Indeed, pharmacies, PBMs, and prescriber EHRs successfully and securely communicated over 1.7 billion patient medication histories across the Surescripts network in 2015 alone.

 The NCPDP medication history solution provided by Surescripts includes both pharmacy-dispensed data and PBM claims data. When combined, these longitudinal data sets provide a more comprehensive nationwide picture of a patient’s medication history than is available in PDMPs.

In summary, although we agree with the authors’ assessment of the problem, we believe there is a proven and scalable solution to improve medication reconciliation that is already available to, and used by, clinicians.

 Surescripts LLC (AAD, SJ, YY, CD, KW), Arlington, VA.

 All authors are employees of Surescripts, which has a commercial product for medication history and would benefit financially from the success of the products mentioned in the letter. The authors report no other relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (AAD, CD, SJ, KW); acquisition of data (AAD); analysis and interpretation of data (AAD, YY); drafting of the manuscript (AAD, CD, SJ, KW, YY); critical revision of the manuscript for important intellectual content (AAD, CD, SJ, KW, YY); statistical analysis (AAD); provision of patients or study materials (AAD); administrative, technical, or logistic support (AAD, YY); and supervision (AAD).

 Seth Joseph, MBA, Surescripts LLC 2800 Crystal Dr, Arlington, VA 22202. E-mail: seth.joseph@surescripts.com.

1. Askin E, Margolius D. A call for a statewide medication reconciliation program. 

2. Cornish PL, Knowles SR, Marchesano R, et al. Unintended medication discrepancies at the time of hospital admission. 

3. Rutkow L, Turner L, Lucas E, Hwang C, Alexander GC. Most primary care physicians are aware of prescription drug monitoring systems, but many find the data difficult to access. 

. 2015;34(3):484-492. doi: 10.1377/hlthaff.2014.1085.

4. HHS. Prescription Drug Monitoring Program interoperability standards. HealthIT.gov website. https://www.healthit.gov/sites/default/files/fdasia1141report_final.pdf. Published September 2013. Accessed January 2017.

5. HHS. ONC Health IT Certification Program. HealthIT.gov website. https://www.healthit.gov/policy-researchers-implementers/standards-and-certification-regulations. Updated December 14, 2016. Accessed January 2017.

6. Gabriel MH, Smith JY, Sow M, Charles D, Joseph S, Wilkins TL. Dispatch from the non-HITECH-incented health IT world: electronic medication history adoption and utilization. 

. 2016:23(3)562-569. doi: 10.1093/jamia/ocv151.

7. Surescripts. 2015 National Progress Report. Surescripts website. http://surescripts.com/news-center/national-progress-report-2015/. Accessed August 2016.&ensp;

Articles

WEB EXCLUSIVE

In this reply to the commentary, “A Call for a Statewide Medication Reconciliation Program,” published in the October 2016 issue of The American Journal of Managed CareÂ®, authors discuss a proven and scalable solution to improve medication reconciliation that is already available to, and used by, clinicians.   

A Better Way: Leveraging a Proven and Utilized System for Improving Current Medication Reconciliation Processes

	Addressing the mounting primary care shortage in the United States has been a focus of educators and policy makers, especially with the passage of the Affordable Care Act in 2010 and the Medicare Access and CHIP Reauthorization Act in 2015, placing increased pressure on the system. The Association of American Medical Colleges recently projected a shortage of as many as 65,000 primary care physicians by 2025, in part because fewer than 20% of medical students are picking primary care for a career.

	We examined the issue of attracting medical students to primary care through the lens of organizational behavior theory. Assuming there are reasons other than lower income potential for why students are inclined against primary care, we applied various principles of the Herzberg 2-factor theory to reimagine the operational flow and design of primary care. We conclude by proposing several solutions to enrich the job, such as decreasing documentation requirements, reducing the emphasis on specialty consultations, and elevating physicians to a supervisory role.

	Although there is a heavy reliance on increasing financial remuneration to primary care physicians as a way of recruiting members to the field, we can look to other modalities—informed by organizational behavior theory&mdash;to enrich the field and make it a more attractive option for trainees. The added benefit of such an approach is significant cost savings, which can then be invested back into the primary care practice. These modalities include: 

		Decreasing documentation requirements. 

		Increasing the supervisory role of physicians in a multidisciplinary practice. 

		Reducing the reliance on specialists to manage the care of patients with chronic diseases.

From the outside, the view of a life in medicine usually involves meaningful patient contact, rewarding longitudinal relationships, and collaborative practice with nurses and specialists—in short, a vision that resembles the work of a primary care physician (PCP). At the outset of their training, almost half of all medical students indicate an interest in primary care.

 However, as students approach the beginning of their fourth year and begin to make defining choices for their future, that number drops to about 15%. With the Association of American Medical Colleges projecting a shortage of as many as 65,000 PCPs by 2025, the primary care workforce is in a deficit crisis.

With the passage of the Affordable Care Act in 2010 and the Medicare Access and CHIP Reauthorization Act in 2015, there is now even more stress on the system; these acts envision a healthcare system with the PCP squarely in the center, serving as both the linchpin and gatekeeper to complex medical coordination. Despite the election of a new administration that favors market-based solutions over mandates, the emphasis on care coordination and “value” is not likely to go away. The changing policy landscape provides opportunity: as reimbursement structures change and health systems compensatorily alter their workflow, we can consider solutions that require us to revisit these blueprints. In this paper, we consider how such operational improvements can motivate more talented medical learners to pursue primary care.

When considering how to motivate and retain talent in medicine, all too frequently we reach into our bag of financial rewards—increased base salary, loan forgiveness, or fringe benefits&mdash;for the answer. Nevertheless, although research does show that financial incentives tend to lead to increased output and productivity in the short term, such gains are usually modest and not sustained.

 Additionally, such financial rewards come with hidden costs, such as their propensity to mitigate the attractiveness of the task at hand and distract from the process of task activity to the product of getting the reward.

Behavioral theory, first proposed approximately 5 decades ago by Frederick Herzberg, alters this traditional management approach by dichotomizing the sources of employee satisfaction. Herzberg distinguished between the factors that make people satisfied and motivated on the job (motivators) from those that make them dissatisfied (hygiene factors) as separate entities altogether (

Herzberg demonstrated, through an examination of the work lives of engineers and accountants, that factors providing positive satisfaction arise from the intrinsic nature of the work itself, such as challenging goals, increasing responsibilities, and opportunities for personal growth. These intrinsic factors contain the stimuli necessary to address individuals’ deep-seated needs for growth and achievement. Hygiene factors tend to be environmentally situated and peripheral to the job itself, such as one’s relationships with peers and supervisors, salary, work conditions, and company policy; by themselves, these factors tend not to motivate an employee to enter a particular field and excel.

In considering how to make the field of primary care more attractive to students, we must avoid falling into the trap of optimizing solely for hygiene factors despite these often tending to be the lowest hanging fruit and easiest to execute. Students privately fret over the lack of prestige in primary care, believe the notion that one can be “too smart” for such a generalist career, or become concerned about the administrative workload and hassle of such a coordination-heavy role. In light of this information, it becomes clear that the charges laid against primary care are more fundamental than simply the financial piece.

 Improving hygiene factors alone, therefore, will likely not be as successful as considering how to make the job itself more compelling.

The push toward demonstrating value in healthcare is a fine starting point, with most of the population-based value metrics falling to primary care for both their delivery and documentation.

 This added burden comes with little substantive support for those most responsible for the activity, however. Moreover, the existing documentation and clerical burdens of primary care—&shy;requirements that grow ever more detailed and &shy;billing-centric&mdash;represent quintessential horizontal job enlargement. Instead of providing opportunity for an employee’s psychological growth, such overloading merely makes a job structurally bigger.

Understanding how to modify the job to make it more enriching leads to solutions for organizing work and expanding access to the physician, and it suggests a potential funding mechanism. Per Herzberg’s recommendations to enable employees to become experts at specialized tasks, we can configure systems that would allow physicians to practice at the top of their license by reassigning routine and clerical tasks to midlevel providers.

 This argument has been repeatedly used in an attempt to show commitment to deemphasizing the administrative burden on primary care, but, to date, has seldom been met with actual resources. A related principle calls for removing some controls while retaining accountability. We interpret this to mean elevating physicians to the more supervisory role of midlevel providers who handle the bulk of follow-up visits and maintenance care.

Herzberg also calls for introducing new and more difficult tasks not previously handled. This could translate to expanding the scope of problems that primary care offices can manage. Currently, more than half of specialist visits are for routine follow-up of chronic disease management—a real perversion of this expensive resource and a further weakening of the role and capacity of PCPs.

 Systems that allow for management of the vast majority of health problems to fall under the purview of PCPs for most diagnoses and care, with specialists only intermittently consulted when diagnosis or management dilemmas require special expertise, would automatically reap savings, a proportion of which would naturally need to flow toward PCPs. That they would also serve to enhance the intellectual challenge and satisfaction of primary care is a bonus.

Herzberg’s theory is sometimes criticized for generalizing employee behaviors without considering individual personality traits that respond differently to incentives and for conflating job satisfaction with increased productivity. Although the theory is imperfect, it draws its enduring value from being one of the most replicated studies in various corporate settings and across different populations.

Organizations that have begun implementing these principles have already reaped dividends. Two years after moving to an enhanced staffing model with a greater reliance on midlevel providers, the Seattle-based Group Health Cooperative noted a halving of reported rates of physician burnout, a 29% reduction in the use of specialty consultations, and overall cost savings of $14 per member per month.

 The SouthCentral Foundation, based in Anchorage, Alaska, designed its practice around a medical home that distributes work so that each team member operates at the highest level of their credentials: nurses focus on care coordination, medication refills, chronic disease monitoring, and test notifications; medical assistants handle point-of-care testing; and behavioral health consultants address mental health complaints, parenting and family dysfunction, and communication challenges.

 This gives physicians greater bandwidth to offer up to 50% to 80% of appointments to same-day visits for acute complaints. Over a 5-year period, from 2000 to 2005, the system saw a 60% drop in visits to specialists and a 40% decrease in hospital days and admissions.

If we are to address the challenges of cost, access, and sustainability, then the inevitable future of primary care will involve utilizing physicians in a supervisory capacity at the head of large, multidisciplinary teams, the members of which are trained and competent to be able to see patients on their own while still reporting back to the physician. These changes will not only foster greater cost savings, but will likely also benefit the issue of talent management. As a form of job enrichment, changing the very nature of the work will serve as an important recruiting and retention tool for PCPs.

The authors would like to acknowledge the advisory contributions of Laurence F. McMahon, Jr, MD, MPH, and Jason Mann, MSA.

 University of Michigan Health System (SM), Ann Arbor, MI; VA Ann Arbor Healthcare System (SS), Ann Arbor, MI.

 Dr Saint is on the medical advisory boards of Doximity and Jvion. Dr Mullangi 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 (SM, SS); acquisition of data (SM); analysis and interpretation of data (SM); drafting of the manuscript (SM); critical revision of the manuscript for important intellectual content (SM, SS); statistical analysis (SM); and supervision (SS).

 Samyukta Mullangi, Department of Internal Medicine, University of Michigan Health System, 1500 E Medical Center Drive, Ann Arbor, MI 48109. E-mail: smullang@med.umich.edu.

1. Novak S. Luring students into family medicine. 

website. http://www.nytimes.com/2012/09/10/us/10iht-educlede10.html. Published September 9, 2012. Accessed Aug 1, 2016.

2. Benabou R, Tirole J. Intrinsic and extrinsic motivation. 

3. Herzberg F. One more time: how do you motivate employees?

4. Warm EJ, Goetz C. Too smart for primary care? 

2013;159(10):709-710. doi: 10.7326/0003-4819-159-10-201311190-00009.

5. Porter ME, Pabo EA, Lee TH. Redesigning primary care: a strategic vision to improve value by organizing around patients’ needs. 

2013;32(3):516-525. doi: 10.1377/hlthaff.2012.0961.

6. Starfield B. The future of primary care: refocusing the system. 

2008;359(20):2087-2091. doi: 10.1056/NEJMp0805763.

7. Bassett-Jones N, Lloyd GC. Does Herzberg’s motivation theory have staying power? 

8. Reid RJ, Coleman K, Johnson EA, et al. The Group Health medical home at year two: cost savings, higher patient satisfaction, and less burnout for providers. 

2010;29(5):835-843. doi: 10.1377/hlthaff.2010.0158.

9. Klein S, McCarthy D. CareOregon: transforming the role of a Medicaid health plan from payer to partner. The Commonwealth Fund website. http://www.commonwealthfund.org/publications/case-studies/2010/jul/careoregon. Published July 22, 2010. Accessed August 1, 2016.&ensp;

COMMENTARY

Primary care physician recruitment and retention must rely on not only increased financial remuneration, but also on a redesign of workflow and processes.

Applying Organizational Behavior Theory to Primary Care

Avoidable readmissions of patients discharged from hospitals are a major concern. This study evaluates the impact of pharmacist-provided postdischarge services on hospital readmissions for members of a US managed Medicaid health plan.

 Synergy Pharmacy Solutions (SPS) initiated a transition of care (TOC) service for high-risk members of the Kern Health Systems (KHS) managed Medicaid plan. Over 1100 patients were referred to SPS between April 2013 and March 2015. KHS classified hospitalized members as high risk for readmission based on prior healthcare utilization, a health risk assessment questionnaire, and the use of the Johns Hopkins predictive modeler. This study compares SPS TOC recipients with a matched sample of KHS members discharged from nonintervention hospitals. Thirty-day and 180-day readmissions and time-to-readmission were defined as outcomes. Logistic regression and Cox model were estimated, controlling for demographics, diagnostic and drug profiles, and prior hospital utilization.

KHS identified 1763 high-risk discharges from nonintervention hospitals, of which 1005 and 669 were matched to 830 and 558 selected SPS patients in 30-day and 180-day populations, respectively. The SPS postdischarge intervention reduced the risk of readmission within 30 days by 28% (odds ratio [OR], 0.720; 95% confidence interval [CI], 0.526-0.985) and within 180 days by 31.9% (OR, 0.681; 95% CI, 0.507-0.914). The estimated effect of the SPS intervention from the Cox model was a reduction in risk of 25% (hazard ratio, 0.749; 95% CI, 0.566-0.992).

 A community pharmacy-based postdischarge TOC program can significantly reduce readmission rates at 30 and 180 days compared with usual discharge care.

An ambulatory care pharmacy-based transition of care program reduced 30-day and 180-day readmission rates by 28% and 31.9%, respectively, compared with usual discharge care. 

		The pharmacist interventions focused on patient education, resolving medication-related problems, and facilitating access to postdischarge appointments and medications. 

		Previous studies evaluating pharmacist-provided care transitions have focused on specific disease states, had shorter follow-up periods, and/or included only academic or integrated health systems. The standalone clinic evaluated herein may be more generalizable to a variety of different practice settings. 

		Future research may identify specific risk factors and interventions that affect readmissions.

Patients with complex medical histories and medication regimens who are admitted to the hospital are at risk for readmission due to a number of factors, including lapses in the continuity of care. The average 30-day readmission rate in the United States is about 16%.

 These high readmission rates have imposed a significant clinical and economic burden on the US healthcare system. As a result, the Hospital Readmissions Reduction Program, enacted in October 2012, directed that CMS reduce payments to hospitals with excess 30-day readmission rates for conditions like acute myocardial infarction (AMI), heart failure, pneumonia, chronic obstructive pulmonary disease (COPD), total hip arthroplasty, and total knee arthroplasty.

 Thus, it is critical to identify the reasons for readmissions and to implement programs to decrease the risk of readmissions. 

Suboptimal medication therapy during the transition of care (TOC) period following hospital discharge is a major contributory factor to hospital readmissions and increased healthcare utilization.

 Forster et al estimated that 11% of patients experienced an adverse drug event after discharge from inpatient services, and 27% of readmissions were considered to be preventable if the patient had received appropriate postdischarge medication monitoring.

 Additionally, several services have been shown to impact hospital readmissions, including patient education, medication adherence counseling, and medication reconciliation.

 Pharmacist involvement in discharge counseling, medication reconciliation, and telephone follow-up has resulted in a lower incidence of preventable adverse drug events.

 Consequently, it is reasonable to expect that pharmacist-led TOC services may decrease readmission rates.

Synergy Pharmacy Solutions (SPS) in Bakersfield, California, initiated an ambulatory care pharmacy-based TOC service in 2013 for recently discharged members of the Kern Health Systems (KHS) managed Medicaid health plan who were classified as high risk based on their healthcare utilization, medical history, and social history and the use of the Johns Hopkins predictive modeler. High-risk members admitted to a single local hospital were referred to SPS for postdischarge services. Over 1100 members were referred to SPS between April 2013 and March 2015. 

This study evaluated the effect of the ambulatory care pharmacy-based TOC services provided by SPS on 30-day and 180-day readmissions compared with a control group of matched KHS members discharged from neighboring hospitals.

Ambulatory Care Pharmacy-Based TOC Program

The risk of readmission for individuals enrolled in KHS's managed Medicaid plan was evaluated based on their history of hospitalizations, prescription medication utilization, and social history. Adult patients discharged from a local hospital who were at high risk for readmission were automatically referred to the SPS TOC program. Those who met the following criteria were then excluded by the SPS team: discharged to a skilled nursing facility, rehabilitation facility, or hospice; died in hospital; left hospital against medical advice; or hospitalized for an elective procedure, obstetrical complications, substance abuse, a urinary tract infection, or a suicide attempt.

Once the qualified patient agreed to participate in the SPS TOC program, medications were reconciled and any discrepancies between the patient’s self-reported medication use and the hospital discharge orders were noted. Over the 30 days following discharge, the pharmacists worked with the outpatient providers to resolve any medication-related problems, such as inappropriate therapy, therapeutic duplications, and potential drug interactions. In addition, the pharmacists counseled patients to improve medication adherence. The pharmacy staff reinforced the discharge care plan, including postdischarge appointments, facilitating authorizations for specialist care, arranging transportation for appointments, and working with each patient’s dispensing pharmacy to resolve insurance-related issues. Patients requiring additional assistance were invited for a face-to-face visit, which included more intensive counseling and assistance with organizing medications. 

Medication management services were documented directly into the existing electronic health record (EHR) system of the ambulatory care pharmacy, which included customized reporting capabilities. Daily reports were generated by the pharmacy team, and follow-up tasks were assigned accordingly. The clinical pharmacy team acted as a liaison to bridge the communication gaps between the patients, their prescribers, and their dispensing pharmacy, thereby facilitating improvements in the continuity of care between the inpatient and outpatient settings. 

The primary source of data for this study was the KHS paid claims database, which covered all enrolled beneficiaries’ inpatient records, outpatient services, emergency department visits, and prescription claims for services rendered within the United States. 

International Classification of Diseases, Ninth Revision, Clinical Modification 

codes were used to identify diagnoses. Medications were identified using specific therapeutic class codes. The data related to TOC services were collected from the EHR system at SPS. 

The intervention and control patient populations were selected from the pool of adult Medicaid managed care members of KHS's health plan who were discharged from either the study hospital or the control hospital (other neighboring hospitals contracted with KHS in Kern County, California) and met the following inclusion criteria at the time of discharge: active members of KHS with an inpatient stay at participating hospitals and at least 1 of the following: 1) high risk, as determined by KHS’s algorithm, including prior healthcare utilization and social history; 2) discharged with prescription claims for 5 or more medications; 3) admitted to any local hospital within the last 45 days. 

For the intervention patients referred to SPS, an index date was defined as the discharge date of their hospitalization immediately preceding referral. All referred patients were screened for 6 months of continuous health plan enrollment prior to the index hospitalization and 30 days of postdischarge data. A second screen requiring 180 days of postdischarge data was applied for hospital admissions used in the analysis of readmission at 6 months.

The control group was identified retrospectively by applying the KHS risk-screening algorithm to its members discharged from neighboring hospitals between October 2012 and March 2015. In order to identify an index hospitalization for each control group patient, all hospitalizations for each patient were converted into episodes of hospital care and matched to the index episode in an intervention group member based on the number of prior hospitalizations and length of stay (LOS) (± 1 day). In order to maximize the power of the analysis, we included all the patients from the control group who were matched to at least 1 patient in the intervention group. Thus, intervention patients could have more than 1 matched control hospitalization in the final study population.

A total of 1123 patients were referred to SPS, of which 830 met the enrollment criteria for the 30-day analysis and were matched to 1005 patients receiving usual care. A total of 558 SPS patients met the enrollment criteria for the 180-day analysis and were matched to 669 usual care patients (

). In the intervention group, all patients referred to SPS were included in this intent-to-treat (ITT) analysis, including those who did not qualify for services, who could not be contacted, and who declined services. 

The primary outcome measures were all-cause 30-day and 180-day hospital readmissions, which were defined as inpatient stays within 30 or 180 days after the index hospitalization discharge date. The 180-day window was analyzed in order to evaluate the persistence of the intervention beyond 30 days. Time to readmission was also calculated and used as a secondary outcome measure. As a sensitivity analysis, the total count of hospital readmissions within 30 days and 180 days after the index date were compared between the intervention and control groups.

Descriptive statistics were applied to test for differences in demographics and clinical characteristics between the intervention and control groups. A χ

 test was used to test for baseline differences between intervention and control patients in the distribution of gender, race, age, and indicator of prior hospitalizations. Student’s 

 test was utilized to compare the mean index hospitalization LOS and number of medications.

Logistic regression was used to estimate the impact of the TOC intervention on the likelihood of a 30-day or 180-day readmission. These models controlled for age, gender, race, prior hospitalizations (yes/no), LOS of the index hospitalization, inpatient diagnoses prior to and including the index hospitalization, and the mix of medication classes used by the patient over the 6 months prior to admission. Time to readmission was analyzed using a Cox proportional hazards model, controlling for the same covariates used in the logistic analyses. The study population for the Cox analysis was the same population used in the 30-day readmission analysis. For the sensitivity analysis, ordinary least squares (OLS) regression and Poisson regression models were used to estimate the effects of the TOC intervention on the count of hospitalizations within 30 days and 180 days after the index hospitalization controlling for demographic information, prior healthcare utilization, and comorbidities. Data analyses were performed using SAS version 9.4 (SAS Institute Inc, Cary, North Carolina) and STATA version 12 (StataCorp, College Station, Texas). 

A total of 1123 patients were referred to the SPS TOC program during the study period, of whom 841 were continuously enrolled in the KHS Medicaid plan 6 months prior to and 30 days after the index hospitalization. After matching, 830 intervention patients and 1005 control observations were included in the logistic regression model for 30-day readmission. For the analysis of 180-day readmission, 564 referred patients were continuously enrolled in the KHS plan 6 months prior to and 30 days after the index hospitalization, of which 558 from the intervention group and 669 from the control group were matched. 

 compares the baseline characteristics of the intervention and control groups. In the 30-day readmission population, the age, gender, and number of medications from the intervention and control groups were not statistically different, whereas the intervention group tended to have a higher proportion of prior inpatient admissions and longer index hospitalization LOS. In the 180-day matched population, the age, race, and gender of the 2 groups were similar. As in the 30-day population, the intervention group had a higher proportion of patients with prior inpatient stays and longer LOS. In order to control for the differences between groups, these demographics and clinical characteristics were used as independent variables in both the logistic and Cox models. 

After controlling for confounders, the multivariate logistic regression analysis on 30-day readmissions showed that the SPS TOC intervention was associated with a statistically significant 28% reduction in 30-day readmissions (odds ratio [OR], 0.720; 95% confidence interval [CI], 0.526-0.985) (

). Other factors associated with an increased risk of 30-day readmissions were prior inpatient stays (OR, 1.930; 95% CI, 1.255-2.969) and longer LOS (OR, 1.054; 95% CI, 1.018-1.091). Patients who were hospitalized for AMI, COPD, digestive diseases, infectious and parasitic diseases, and neoplasms had a higher likelihood of 30-day readmissions. In addition, patients with prescription claims for antiepileptic drugs, dialysis solutions, and dietary supplements (including intravenous nutrition) also had higher 30-day readmission rates. 

The 180-day analysis indicated that the TOC intervention reduced readmissions at 6 months by 31.9% (OR, 0.681; 95% CI, 0.507-0.914) (

). Patients with a prior hospitalization, COPD, and infectious and parasitic diseases were more likely to be readmitted within 180 days. Patients hospitalized for blood disorders and diabetes were more likely to be readmitted within 180 days, but not within 30 days. Use of dialysis solutions and dietary supplements, including intravenous nutrition, was associated with higher risk for 180-day readmissions, consistent with the 30-day outcomes.

The sensitivity analyses demonstrated that the SPS TOC service was associated with a reduction in the number of hospitalizations. The TOC program reduced the number of readmissions by 6 per 100 patients within 30 days and 19 per 100 patients within 180 days compared with patients receiving usual care (

Time to readmission was analyzed by the Cox proportional hazards model on the 30-day ITT population. After adjusting for all demographics and clinical characteristics, the model illustrated that patients receiving TOC services had a 25% lower hazard of readmission compared with patients receiving usual care (hazard ratio, 0.749; 95% CI, 0.566-0.992) (

This study estimated the impact of a community pharmacist-based TOC initiative on 30-day and 180-day readmission rates in a managed Medicaid population. Both the logistic regression and Cox proportional hazard models found that the TOC services at SPS were associated with significantly lower all-cause readmissions at 30 days and 180 days compared with usual discharge care. Accordingly, this study adds to the body of literature on the effects of TOC and the role of the pharmacist in TOC. Whereas previous studies have largely focused on TOC services within academic centers or closed systems, such as the Veterans’ Health Administration, this study evaluates the impact of a stand-alone ambulatory care pharmacy-based TOC service. In addition, prior research on the impact of the pharmacist has focused on medication reconciliation prior to discharge. The SPS TOC program has demonstrated that medication-related problems often persist after discharge, which requires further interventions by pharmacists for the 30-day period after discharge. More details on these interventions will be described in future publications. 

This study also adds to the body of literature on the impact of pharmacist-based TOC services. Many TOC interventions have shown the benefits of close postdischarge care coordination on readmission rates and healthcare utilization.

 However, these studies either focused only on specific disease conditions or only evaluated the effects over a short period of time. O’Dell et al reported that clinical pharmacist services for cardiac patients with unstable angina were associated with lower readmission rates compared with usual care. However, the results were not significant in the larger pool of all cardiac patients.

 Koehler et al designed a randomized clinical trial and showed that pharmacist-led interventions reduced 30-day readmissions but did not affect 60-day readmissions.

 Kirkman et al also found that telephonic follow-up by pharmacists reduced 30-day readmissions; their regression analysis demonstrated that the 30-day readmission OR for patients who received usual care was 1.53.

 When the reference category was reversed, the OR was comparable to our result of 0.72. To our knowledge, our 180-day analysis exceeds the follow-up period of existing studies. 

All patients referred to the TOC services (the ITT population)—including those who did not qualify for services (6.7%), patients who could not be contacted (4.7%), and patients who declined services (2.4%)&mdash;were analyzed in this study. Thus, the results estimate the effects of TOC services on the entire referred population. The corresponding percentages in the 180-day population were 3.4%, 2.2% and 5%, respectively. There were many possible reasons for patients refusing services or being unreachable by phone, including the “cold call” nature of the phone call from SPS, the perception that the TOC services were unnecessary, disconnected phone numbers, homelessness, or refusal to discuss their healthcare with a professional other than their own physician. Despite the inclusion of all of these patients in the analysis, the results demonstrated a significant reduction in readmissions associated with the intervention&mdash;an effect that may have been even larger had we excluded them from the analysis. 

In addition to evaluating the impact of the SPS TOC program on readmission rates, this study explored the potential factors associated with higher readmission rates. In both the 30-day and 180-day analyses, prior hospitalizations within 6 months, COPD and infectious and parasitic diseases were significantly associated with increased risk of readmission. Similarly, using medications for electrolyte imbalance and dietary supplementation was shown to be related to higher readmission rates. It is possible that use of these prescriptions may be indicators for chronic illnesses, such as renal failure and/or gastrointestinal disorders. Although the current results described many factors that could potentially affect readmissions, future stand-alone studies focusing on causal relationships with readmissions would be warranted. Once those factors are successfully identified, future TOC services may be designed to target the appropriate patient groups for whom they would be most effective. 

Our study was limited by several factors. First, the study used a nonrandomized design, selecting patients discharged from intervention and control hospitals. Although we matched the intervention and control populations using the number of prior hospitalizations and the LOS of the index hospitalization, the healthcare utilization in the intervention group was still higher than that of the control group, possibly indicating that the intervention group’s health status was worse than that of the control group (Table 1). After controlling for these imbalances between the groups in the multivariate regression models, the intervention was still shown to have a significant impact on readmission rates. Second, the generalizability of our results may be limited because this study focused on the rural population of Bakersfield, California. Finally, an observational study cannot establish the causality of factors affecting the risk of readmission investigated in the current study. Future studies would be necessary to answer this research question. 

Compared with usual discharge care, the ambulatory care pharmacy-based TOC program significantly reduced readmission rates by 28% at 30 days and 31.9% at 180 days. These are likely conservative estimates of the treatment effect, as all referred patients were included in the intent-to-treat analysis.

The authors would like to thank the staff at SPS, including Cristina Aldaco, Lirio Medina, Stephanie Pimentel, Paula Reyes, Dr Brian Komoto, PharmD, CEO at SPS, and Dr Carolyn Dam, PharmD, former clinical pharmacist at SPS. In addition, the authors thank their colleagues at Kern Health Systems (KHS), including Jeff Pollock, Utilization Management Program manager; Pavan Tirumalasetty, data analyst; Deborah Murr, RN, administrative director of Health Services; Douglas Hayward, chief executive officer; and Dr John Fisher, MD, MBA, former chief medical officer at KHS.

 Department of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California (WN, JM), Los Angeles, CA; Synergy Pharmacy Solutions (DC, JH, KK), Bakersfield, CA; Kern Health Systems (CG, BW), Bakersfield, CA.

 Drs Colayco, Hashimoto, and Komoto are employed by Synergy Pharmacy Solutions, which provides the Transition of Care program described in this paper. Dr Komoto also owns stock in Synergy Pharmacy Solutions. Dr McCombs is a board member of the Komoto Family Foundation and supervises the Komoto Fellowship. 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 (WN, DC, BW, JM); acquisition of data (WN, DC); analysis and interpretation of data (WN, DC, CG, BW, JM); drafting of the manuscript (WN, DC, JH, KK, CG, JM); critical revision of the manuscript for important intellectual content (WN, DC, JH, KK, CG, BW, JM); statistical analysis (WN, DC, JM); provision of patients or study materials (DC, JH, KK); obtaining funding (DC, JM); administrative, technical, or logistic support (DC, BW); and supervision (DC, JM).

Weiyi Ni, PhD, University of Southern California, 635 Downey Way, VPD Ste 210, Los Angeles, CA 90089-3333. E-mail: weiyini@usc.edu. 

1. Christy S, Sin B, Gim S. Impact of an Integrated Pharmacy Transitions of Care pilot program in an urban hospital

. 2015;29(5):490-494. doi: 10.1177/0897190014568674.

CLINICAL

Patients receiving postdischarge care from pharmacists had a 28% lower risk of readmission at 30 days and a 31.9% lower risk at 180 days compared with usual care.

Impact of a Pharmacy-Based Transitional Care Program on Hospital Readmissions

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. 

Our findings suggest the importance of having strong leadership and physician engagement as part of ACO development, thus highlighting the importance of physician engagement reported in prior studies.

 Moving beyond physician engagement, however, all of the ACOs in our study had physicians in formal leadership positions and 3 of the ACOs had strong physician representation on their governing boards. The ACOs in our study included employed and community physicians in various aspects of decision making, ranging from compensation and incentive committees to quality improvement initiatives. Our findings about the importance of physician leadership are consistent with a recent national survey of public and private ACOs, which found that more than half of all ACOs are physician-led and that physicians make up the majority of governing boards in over 75% of the ACOs surveyed.

 Strong physician leadership could also help facilitate high levels of communication across providers, thereby improving patient care.

These findings also underscore the need for adequate IT infrastructure for ACO development. Policies, such as Meaningful Use, helped motivate providers to implement electronic health records, which is a necessary first step. However, ACOs need the ability to access timely and meaningful data to monitor and improve outcomes and efficiency. The ACOs we studied are making significant capital investments in data and reporting capabilities; meanwhile, the ACOs that are unable to make the necessary capital investments in IT infrastructure will likely lag in terms of development. The Medicare Advance Payment Model attempts to address this capital need for smaller or rural Medicare ACOs; however, there do not appear to be similar arrangements in the private sector ACO markets.

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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Applying Organizational Behavior Theory to Primary Care

Impact of a Pharmacy-Based Transitional Care Program on Hospital Readmissions

Private Sector Accountable Care Organization Development: A Qualitative Study

Scaling Lean in Primary Care: Impacts on System Performance

Patient Experience Midway Through a Large Primary Care Practice Transformation Initiative

Effects of an Enhanced Primary Care Program on Diabetes Outcomes

Consumer-Directed Health Plans: Do Doctors and Nurses Buy In?

Improvements in Access and Care Through the Affordable Care Act

Does Paid Versus Unpaid Supplementary Caregiving Matter in Preventable Readmissions?