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The American Journal of Accountable Care September 2017
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Population Health in Primary Care: Cost, Quality, and Experience Impact
Tracy L. Johnson, PhD, MA; Mary van der Heijde, FSA, MAAA; Stoddard Davenport, BA; Carlos Irwin Oronce, MD, MPH; Daniel Brewer, BA; Rachel Everhart, PhD, MS; Patricia Gabow, MD; Simon J. Hambidge, MD; Adam Atherly, PhD; and Holly Batal, MD, MBA
To Err is Avoidable: The Automation of Knowledge and the Clinical Decision Support Revolution
Ezra Mehlman, MBA
Building Partnerships Between a College of Pharmacy and ACOs: Development of the ACORN SEED
Stephanie A. Gernant, PharmD, MS; Genevieve M. Hale, PharmD, BCPS; Tina Joseph, PharmD, BCACP; Renee S. Jones, PharmD, CPh; Sarah Alameddine, PharmD; Stacey Maravent, PharmD; Sara M. Eltaki, PharmD, B
Postacute Care Partnerships and Patient Progression
Penny Gilbert, MSN, MBA, RN, NE-BC, CPHQ; Nancy J. Maggard, MSN, RN, NE-BC; Cheryl Talbert, MSW, LCSW; and Donna Vela, MSN, RN, NE-BC, CPM
Patient Participation in Care Management: Are They Aware?
Jodi Summers Holtrop, PhD, MCHES; Qiaoling Chen, MS; Gretchen Piatt, PhD, MPH; Zhehui Luo, PhD; Jean Malouin, MD; Ruth Clark, RN, BSN, MPA; and Cecilia Sauter, MS, RD, CDE, FAADE

Population Health in Primary Care: Cost, Quality, and Experience Impact

Tracy L. Johnson, PhD, MA; Mary van der Heijde, FSA, MAAA; Stoddard Davenport, BA; Carlos Irwin Oronce, MD, MPH; Daniel Brewer, BA; Rachel Everhart, PhD, MS; Patricia Gabow, MD; Simon J. Hambidge, MD; Adam Atherly, PhD; and Holly Batal, MD, MBA
An evaluation of the use of predictive modeling for primary care resource allocation demonstrated reduced spending and improved quality and patient experience for publicly insured adults.

Objectives: To evaluate whether a primary care practice transformation that used population health strategies and predictive modeling to match clinical resources to patient needs reduced inpatient and total spending, while maintaining or improving quality and patient experience for adult Medicaid and Medicare patients. 

Study Design: Quasi-experimental analysis using an adjusted historical control. 

Methods: Measures included a quality composite metric, patient experience indicators, and total cost of care, as assessed by an independent actuary. 

Results: Payers saved a cumulative $15.8 million (1.7%) across a 26-month program implementation period, which was substantially larger than the approximately $3.9 million in program staffing expenses. Driven by reduced inpatient spending, the total cost of care for high-risk adults was reduced across all lines of business, ranging from –$40.88 per member per month (PMPM) to –$737.20 PMPM. Payer savings were larger for Medicare (5.5%) than for Medicaid patients (0.7%). Patient experience metrics improved during this time. Quality findings were mixed, likely confounded by the 2014 Medicaid expansion. 

Conclusions: These findings suggest that risk-stratified primary care delivery models can achieve the Triple Aim and could be self-sustaining through alternative payment models that allow reinvestment of savings into program costs. These results are consistent with literature that finds that short-term return-on-investment requires carefully targeting patients at risk of hospitalization, that reducing Medicare hospitalizations may be more easily achieved than for Medicaid, and that opportunities may be greater among unmanaged fee-for-service populations. Differences in savings by payer and by year underscore the importance of all-payer approaches to program financial sustainability. 

The American Journal of Accountable Care. 2017;5(3):10-20
Constraining healthcare cost growth has been a focus of health policy since the enactment of Medicaid and Medicare in the 1960s.1 Despite substantial efforts, high medical inflation persists, governmental health spending continues to crowd out other priorities, and health outcomes remain poor compared with those of international peers.2-4 Post Affordable Care Act, efforts to reverse these trends have focused on patient-centered medical homes (PCMHs), complex care management, transitions of care, and accountable care organizations (ACOs). Results to date have been inconsistent, with a limited number of multifaceted programs reducing healthcare expenditures.5-8 

Programs that have lowered costs and improved quality and patient experience (the Triple Aim) often share features of Wagner’s Chronic Care Model.9 Most have implemented comprehensive team-based care, enabling proactive provider-patient interactions and tailoring care models to better support the needs of high-risk patients.10-15 Clinical information systems to facilitate population health management have also been foundational, including high-risk patient identification, decision support, and clinical performance feedback.16,17 

Most cost-saving initiatives have targeted Medicare or commercial populations and have achieved savings by reducing hospitalization.10,12,13,18-22 Among the few well-designed Medicaid programs, evaluations assessing costs or hospitalizations have shown mixed findings.23-25 This suggests the need to adapt successful interventions to Medicaid populations, which will likely require attending to social determinants of health.22,26,27

Finding effective Medicaid approaches takes on increased salience as Medicaid/Children’s Health Insurance Program (CHIP) enrollment has outpaced that of Medicare.28,29 To this end, the Center for Medicare & Medicaid Innovation (CMMI) funded Health Care Innovation Challenge Awards (HCIAs) to accelerate at-scale, delivery system reform.30 We describe the reach and Triple Aim outcomes of an HCIA-funded primary care practice transformation at a large urban safety-net institution. This intervention employed predictive risk modeling to segment patients according to clinical and financial criteria, and sought to target higher-risk patients for more, and more intensive, services. This evaluation tested whether a risk-stratified, enhanced primary care delivery model demonstrated savings through reduced inpatient spending, while improving quality and patient experience for Medicaid and Medicare adults. 



Denver Health (DH) is an integrated, academic safety-net delivery system and the largest provider of Medicaid and uninsured services in Colorado, serving approximately 200,000 patients annually. DH provides comprehensive outpatient and inpatient services and operates a managed care plan. This integration enables data capture across the care continuum.

Target Population

Although the program targeted both adults and children in need of primary care services, this evaluation focused on Medicaid and Medicare beneficiaries older than 19 years, specifically: DH primary care users (≥1 primary care visits in the prior 18 months); DH managed care members; or frequent users of DH’s emergency department, urgent care, or hospital services (≥3 services in a year). The target population was dynamically redefined monthly, according to a validated population attribution and risk-stratification algorithm, described briefly below and detailed elsewhere.31 Excluded were individuals who could not be risk-stratified due to no claims history or short enrollment periods.

DH adapted commercial predictive risk modeling software to assign patients to 1 of 4 tiers of care needs. The algorithm relied primarily on age, gender, diagnosis, clinical procedure, and medication history. Through a multidisciplinary process, DH-defined rules were developed and integrated into the algorithm. These rules considered additional diagnostic information, clinical registries, and utilization patterns signaling unmet social or behavioral health needs. Algorithm development for high-risk tiers focused on defining patient groups at risk of hospitalization with distinctive care support needs, not solely on high costs. As noted, the algorithm was rerun monthly to capture new patients and changes in health status. 

Intervention: Tiered Population Health Approach to Primary Care

This population health intervention built on DH’s National Committee for Quality Assurance–certified PCMHs and used Wagner Chronic Care Model principles to implement team-based care for complex populations.32-36 Consistent with Wagner’s vision of health information technology–enabled, proactive, prepared teams, DH provided real-time patient risk-tier information for care planning at the point of care. We specified a graduated set of enhanced clinical and electronic services appropriate to each risk tier, with more higher-intensity services targeted to higher-tier patients. Standard work that considers both tier and individual needs guided care teams’ service provision. 

All patients (tiers 1-4) were provided “usual care” medical home services, complemented by new, optional electronic messaging reminder services. DH also expanded primary care staffing to include new team members to provide disease management, care transition, and patient navigation services (tiers 2-4). This enhanced-care team included nurse care coordinators, clinical pharmacists, behavioral health consultants, and patient navigators. More comprehensive multidisciplinary care management support was available to complex patients (tiers 3-4) during and between visits. 

For tier 4, separate high-intensity clinics were established for targeted subpopulations: children with special healthcare needs, medically complex adults with multiple admissions, and adults with severe mental health diagnoses. All high-risk teams sought to empower patients through multidisciplinary care planning, goal setting, and problem-solving approaches. Patients also received referrals for specialty care, substance abuse treatment, housing, and other community resources (Figure).

Process and Outcome Measures

Primary outcomes were program reach, patient experience, quality, and total cost of care. Patient reach was quantified as the number of patients receiving any face-to-face or phone-based patient interaction by the HCIA-funded enhanced-care or high-intensity teams. Usual care clinical contacts were not included, nor were low-touch services, such as phone messages, letters, and text messages. These exclusions sought to avoid overstating program reach by focusing on new, intensive HCIA services. 

To assess overall quality, DH uses an internal composite quality metric comprising individual indicators largely adapted from the Health Resources and Services Administration’s Health Center Program Uniform Data System. DH used the Consumer Assessment of Healthcare Providers and Systems’ PCMH-aligned items to assess patient experience. 

Assessed from the payer perspective, the total cost of care measure was calculated by aggregating medical expenditures across all inpatient, outpatient, professional, and other services, and was expressed on a per-member per-month (PMPM) basis to account for differing lengths of enrollment. DH had nearly complete data capture for its managed care members because the DH health plan pays members’ medical claims. For those enrolled in fee-for-service (FFS) programs, spending estimates for non-DH services were derived from public sources (listed in the eAppendix [eAppendices available at]). Savings net of program expenses were also calculated. 

Program expenses included salary and benefits of HCIA-funded staff, including the enhanced-care team, information technology, and evaluation personnel. Clinical staffing associated with reimbursable clinical care for program participants was not counted, nor were program development costs. This represented DH’s annual, unreimbursed costs to continue the program post award (eAppendix).

Analytical Approach

This quasi-experimental analysis compared trends in payer spending between a baseline performance period and 2 intervention performance periods. Because DH enrolls a majority of the Denver Medicaid market and the program was implemented at scale, no concurrent DH or non-DH control population exists. Therefore, it was necessary to develop an appropriate historical comparative population to estimate what the baseline costs would have been without the intervention. Observed costs were then compared with this historical cost benchmark. CMS uses a similar methodology to assess the financial performance of Medicare ACOs.37 Consistent with CMS’ approach, population-level cost outcomes were assessed whether or not targeted individuals were reached by the intervention. In the actuarial literature, this is approach is known as quasi-experimental analysis using an adjusted historical control.38

To construct performance populations for analysis, DH used its patient attribution and tiering algorithm to identify the target population during each month of baseline and intervention performance periods, resulting in 38 cross-sections, each with different member month counts and tier distributions. These 38 cross-sections were subsequently aggregated into 3 performance periods: a 12-month baseline period, a 14-month early program period, and a 12-month mature program period. The decision to create 2 intervention periods balanced several considerations: distinguishing early and mature program effects, minimizing seasonal effects by selecting measurement periods of approximately equal length, and isolating the potentially confounding effect of the Medicaid expansion in 2014 into a separate performance period. 

Changes in medical costs were calculated by subtracting observed costs during the 2 intervention periods from the historical cost benchmark. In contrast to a pre-post analysis that focuses on a single population cross-section that is reached by the intervention, we refresh the population (38 cross-sections). This addresses regression by accounting for population dynamics such as changes in health status. To the extent that high-risk patients became lower-risk, died, or left the population during the intervention periods, this phenomenon also occurred during the baseline period, enabling detection of performance improvement net of these effects. 

During the intervention periods, observed member months (which differ by time period) were the denominator used to calculate PMPM costs, but were not the multiplier to calculate total costs. To ensure the intervention period costs were comparable to those of the baseline period, the PMPMs were applied to the baseline population’s tier distribution. This “tier mix” adjustment ensures that patient health status was comparable across performance periods, effectively holding tier mix constant (see eAppendix for details).

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