Sandeep Palakodeti, MD, MPH

Central to the Porter-Lee value agenda, integrated practice unit (IPU) pilots have multiplied over time, striving to fundamentally reorganize the delivery of care via multidisciplinary teams while improving outcome measurement and driving competitive volumes. As these pilots emerge to form bridges of coordinated care, critics continue to question the value proposition of these IPUs: Do they achieve the Quadruple Aim of modern health care by improving cost of care, health outcomes, patient experience, and provider experience? Noting that value realization has eluded IPU pilots globally over the past 15 years, the authors examine 6 critical challenges and propose recommendations to consistently deploy effective IPUs, leading to a win-win proposition for all key stakeholders.

Central to value-based health care, integrated practice units (IPUs) colocate clinicians, support specialists, and requisite services to effectively manage end-to-end care for complex conditions. Although several IPU pilots have emerged, critics question the value proposition of these so-called bridges of coordinated care. Overall, we recommend 6 means for IPUs to realize their value and meet the Quadruple Aim of health care:

leverage competitive market-based approaches to increase adoption;

incorporate preauthorized, dynamic care pathways for scheduling;

develop standardized outcome measurement processes;

administer training programs to improve provider collaboration competencies;

establish suitable technology and partners to continuously adjust care delivery; and

integrate risk-reward sharing between payers and IPU teams.

In his landmark framework on value-based health care (VBC), Michael Porter proposed the organization of condition-based care delivery through integrated practice units (IPUs).

 Conceptually, IPUs colocate clinicians, support specialists, and requisite care delivery services to effectively manage the end-to-end spectrum of medical conditions and garner large volumes by becoming a de facto center of excellence and multidisciplinary competency for chronic conditions, such as diabetes, hypertension, and breast cancer. The colocated team meets regularly to develop care plans, process improvements, and metrics for outcomes. IPU pilots with these characteristics have multiplied over time into many community specialty clinics, outpatient clinics, patient-centered medical homes, primary care transformations, ambulatory intensive care units, and infusion centers, bringing a flair of retail and competition to health care, but the critical question remains: Do these units achieve the Quadruple Aim of modern health care by improving the cost of care, health outcomes, patient experience, and provider experience?

A patient with multiple conditions can benefit from properly structured IPUs instead of experiencing multiple referrals and hand-offs at single-specialty clinics. For example, 20% of knee replacements in the United States are performed at low-volume specialty centers or hospitals with infrequent opportunities to build competency, leading to poorer patient outcomes.

 IPUs also save patients the pain and cost of visiting large hospitals, provide additional safety from exposure to iatrogenic diseases, and offer personalized care directly in communities. Both advocates who call these IPUs bridges of coordinated care and critics who call them ineffective archipelagos differ on their value propositions.

 With widespread VBC pilots around the world, however, there appears to be a general consensus that the IPU is a core care delivery model that can innovate health care, even if it is not a panacea for all its ills.

We examine 6 critical challenges and propose recommendations to improve the efficacy of and value realization by IPUs: (1) scalability, (2) optimal workflow design and effective scheduling, (3) standardized metrics for performance and outcomes, (4) new competencies and behaviors, (5) patient-centered health care technology, and (6) fair value-based risk sharing (

Despite several successful IPU pilots across the world, their performance and return on investment are far from being consistent, as IPUs need large customer bases to offset setup and operational costs. Patients with pancreatic cancer have a 25% greater hazard of long-term mortality at low-volume centers than at high-volume centers. MD Anderson Cancer Center has successfully organized care into 14 disease-specific centers that improved collaboration among multidisciplinary teams handling large volumes of patients. Similarly, the Diabeter, a Dutch practice, treats more than 2000 patients per year at 5 IPUs, achieving the lowest rate of hospital admissions and cost of care for type 1 diabetes nationally.

 Although a shift from single-specialty centers and large hospitals to IPUs is promising, a recent systematic review by Cattel and Eijkenaar indicates that the evidence regarding cost-effectiveness and improved outcomes remains inconclusive.

 Because IPUs are relatively unestablished entities in the health care ecosystem, they face the challenges of building credibility and avoiding perceptions of competition with mainstream hospitals. Moreover, increasing adoption and attracting break-even volumes at IPUs will continue to challenge proponents to explore novel partnership models such as clinically integrated networks (CINs) and market-based strategies from retail industries while frustrating those without an adequate understanding of critical success factors. To mitigate the risks associated with scalability, we recommend that IPUs (1) use market-based strategies to increase patient adoption, (2) develop novel partnership models such as CINs, and (3) account for differing patient choices to win over rising competition. Increased volumes will support multidisciplinary staff, their competency development, and efficient operations to optimize cost.

Another critical challenge facing IPUs is ensuring an effective workflow design through new care models. Suboptimal pathways can result in poor patient scheduling with unacceptably long waitlists and underutilized care teams, resulting in patient dissatisfaction and cost overruns.

 For effective care scheduling, dynamic workflow templates for patient appointments can minimize waiting time by accounting for system randomness, lead time before and during visits, disruptions due to late arrivals, workflow changes, and unplanned absences by patients or providers. Growing personalization and overrunning sessions can increase the workload at IPUs, leading to increased congestion at clinics, patient dissatisfaction, and erosion of staff morale. Advanced scheduling technology, such as artificial intelligence (AI)–based machine learning algorithms, can train and dynamically adjust scheduling and operational systems to account for capacity and randomness with longitudinal data. Additionally, preauthorized care pathways at IPUs can improve speed and convenience for patients.

Standardized Metrics for Performance and Outcomes

IPUs need to develop standardized metrics for benchmarking operational performance and patients’ health outcomes. The International Consortium for Health Outcomes Measurement has developed evidence-based indicators to measure quality and health outcomes, but they lack wide adoption. If standardized metrics were to be established across the industry, IPUs could benefit by benchmarking and improving their quality and operational performance while transparently communicating their competitive advantages to stakeholders. High-performing IPUs could thus negotiate better contracts and claim improved reimbursements from payers while passing cost savings directly to patients.

New Skills and Behaviors for Integrated Teams

Traditional clinical practices do not require clinicians to operate in teams over the full treatment cycle.

 This presents a potential danger: IPUs may end up reorganizing the existing workforce and workflows without providers acquiring the prerequisite competencies. Highly functioning IPUs need clinicians to acquire new collaboration and facilitation skills alongside their medical expertise. Interdisciplinary teams need to be accountable for holistic patient case management by focusing on relationships, patient scheduling, care coordination, medication adherence, routine follow-ups, and broader social determinants of health (SDOH). These new competencies require the development and administration of thorough training programs for IPU teams. As such, medical schools and residency programs should prepare clinical staff for these critical skills at the outset; for example, instead of assigning a set number of patients to each resident physician, a single patient should be assigned to a multidisciplinary team of residents who collaboratively provide holistic care for condition-based diseases.

Patient-Centered Health Care Technology for IPUs

Health care technology plays a critical role in various touchpoints between patients and IPU teams. Patient-centered platforms enable bidirectional communications, from scheduling care and sharing care pathways to recording treatment data and dashboarding outcomes in real time. Indeed, COVID-19 has catalyzed digital health as integral to care delivery. To contain the significant fixed costs of technology, most vendors are offering per-member per-month usage costs and partnering with payers and providers to enhance their platform capabilities at no charge.

 Advanced IPUs are leveraging predictive analytics to project which patients are most likely to achieve desired health outcomes based on the medical condition or a group of conditions, patient history, patient-reported outcomes, and clinical progress made. We recommend that IPU leaders perform a deep assessment of their technological needs and establish suitable technology and partners to develop AI-based algorithms that continuously adjust care models, dynamic scheduling, and operational performance to improve patient outcomes. Moreover, technology needs to support comprehensive patient management and strong electronic health record solutions.

Value-Based Contracting and Risk Sharing for IPUs

The IPU model requires fair risk-reward sharing across the value chain. Payers need to own accountability for insurance risk and providers for the performance risk for IPUs to achieve their intended objectives of improved business performance and patient health outcomes. The challenges, however, have been to sustain this value matrix longitudinally, extend incentives to care delivery teams, and ultimately pass cost savings to patients. Although a plethora of reimbursement methods and contracting, performance, and outcome metrics have been explored by the industry, there is a lack of consistency in their implementation among IPUs, and stakeholders’ lack of confidence in the repeatability of performance stalls progress.

 As a result, patients continue to be subjected to the fee-for-service model with little incentive for care management teams at IPUs. Support services addressing behavioral health and SDOH are not typically covered under fee-for-service or value-based contracting, thereby constraining the intended packaging of care at IPUs. To be cost-effective, these support services can be shared across multiple IPUs. Our recommendation is to expand value-based contracts to cover both shared and IPU-specific services required for managing end-to-end care. Taking a leaf from the CMS Oncology Care Model and the Comprehensive End-Stage Renal Disease Care Model, it will be beneficial to develop flexible bundled and alternate payment models, adjusted for longitudinal patient outcomes and IPU performance.

In the short term, we predict that IPUs will continue to emerge due to the intuitive VBC phenomenon. However, without thoughtful implementation of all critical success factors (

), value realization will continue to elude most IPUs, as has been the case for the past 15 years. Stakeholders should understand the prerequisites and assess the impacts of effective IPU operations before venturing into this space to avoid undesirable outcomes and financial loss. To accelerate value realization, IPUs need to robustly implement the key value drivers by leveraging market-based approaches to increase adoption and optimize cost, incorporating preauthorized care pathways for scheduling, developing standardized metrics for outcomes, improving collaboration competencies in care teams, utilizing the right health care technologies, and integrating risk-reward sharing between payers and IPU teams.


 Highmark Health (PJ), Pittsburgh, PA; Indiana University of Pennsylvania (PJ), Indiana, PA; Massachusetts Institute of Technology (BJ), Cambridge, MA; University of Pittsburgh (UJ), Pittsburgh, PA; Case Western Reserve University (SP), Cleveland, OH.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (PJ, BJ, UJ, SP); drafting of the manuscript (PJ, BJ, UJ, SP); and critical revision of the manuscript for important intellectual content (PJ, BJ, UJ, SP).

 Sandeep Palakodeti, MD, MPH, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106. Email: sandeep.palakodeti1@gmail.com.


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Articles

As value realization eludes integrated practice units (IPUs), the authors examine 6 key value drivers for IPU teams to competitively drive volumes and hone their multidisciplinary competencies.

Value Realization: An Unattained Challenge for Integrated Practice Units

 Complex care management programs have emerged as a promising model to better care for high-need, high-cost patients. Despite their widespread use, relatively little is known about the impact of these programs in Medicaid populations. This study evaluated the impact of a complex care management program on spending and utilization for high-need, high-cost Medicaid patients.

 Randomized quality improvement trial conducted at CareMore Health in Memphis, Tennessee. A total of 253 high-need, high-cost Medicaid patients were randomized in a 1:2 ratio to complex care management or usual care.

 Intention-to-treat analysis compared regression-adjusted rates of spending and utilization between patients randomized to the complex care program (n = 71) and those randomized to usual care (n = 127) over the 12 months following randomization. Primary outcomes included total medical expenditures (TME) and inpatient (IP) days. Secondary outcomes included IP admission, emergency department (ED) visits, care center visits, and specialist visits.

 Compared with patients randomized to usual care, patients randomized to complex care management had lower TME (adjusted difference, —$7732 per member per year [PMPY]; 95% CI, –$14,914 to –$550; 

 = .036), fewer IP bed days (adjusted difference, —3.46 PMPY; 95% CI, –4.03 to –2.89; 

 <.001), fewer IP admissions (adjusted difference, —0.32 PMPY; 95% CI, –0.54 to –0.11; 

 = .014), and fewer specialist visits (adjusted difference, —1.35 PMPY; 95% CI, –1.98 to –0.73; 

 <.001). There was no significant impact on care center or ED visits.

 Carefully designed and targeted complex care management programs may be an effective approach to caring for high-need, high-cost Medicaid patients.

A complex care management program for high-need, high-cost Medicaid patients reduced total medical expenditures by 37% and inpatient utilization by 59%. Based on the design of the program, these results suggest that:

Carefully designed and targeted complex care management can be effective among high-need, high-cost Medicaid patients.

Community health workers and other nontraditional healthcare workers can help engage and activate patients, build trust, and better understand and manage the nonmedical drivers of poor health and avoidable spending.

Targeted interventions focused on modifiable risk factors are an effective and efficient approach for reducing unnecessary utilization.

High-need, high-cost patients—those with chronic medical conditions and co-occurring social and behavioral complexity—experience poor health outcomes and high rates of potentially preventable spending.

 Improving care quality and lowering spending for this population have become a focus for policy makers, clinicians, payers, and health systems.

Complex care management programs—broadly defined as efforts to coordinate medical and social services, assist patients and caregivers in managing medical and behavioral health conditions, and address the psychosocial drivers of poor health—have emerged as a promising model to improve care for high-need, high-cost patients.

 In recent years, complex care management programs have been widely adopted by health systems and delivery organizations entering into value-based payment arrangements. A 2018 survey found that 96% of accountable care organizations had implemented complex care management programs for high-need, high-cost patients.

The impact of complex care management has been uneven. Systematic reviews and large-scale program evaluations have not demonstrated a consistent impact on health outcomes, spending, or acute care utilization.

 Some individual programs, however, have led to improvements in patient-reported health and decreases in acute care utilization and total spending.

 Program heterogeneity (eg, practice setting, patient characteristics, care models, staffing) may explain these varied results.

Complex care management programs have traditionally been developed and implemented among Medicare populations.

 Increasingly, these models are being adopted in Medicaid.

 Similar to the Medicare population, high-need, high-cost Medicaid patients have high rates of chronic conditions

 both attributes well suited to complex care management.

 High-need, high-cost Medicaid patients also have high rates of behavioral health disorders and unmet social needs,

 attributes less common among Medicare patients and areas in which the impact of complex care management is uncertain.

Despite the proliferation of complex care management for Medicaid patients, there have been very few rigorous program evaluations, and the overall effect of these programs is unclear. This paper presents the results of a randomized quality improvement trial to evaluate the impact of complex care management on spending and utilization for high-need, high-cost Medicaid patients with complex health and social needs.

This was a randomized quality improvement trial to evaluate the impact of a complex care management program on spending and utilization for high-need, high-cost Medicaid patients. The design and implementation of the program, detailed in the following paragraphs, incorporated attributes of successful complex care management programs in other populations.

 The program was implemented at CareMore Health (CareMore) in Memphis, Tennessee, where CareMore provides comprehensive primary care services for Medicaid patients enrolled in TennCare plans administered by Amerigroup. The program was operated out of a single CareMore care center.

Participants were drawn from adult Medicaid patients attributed to CareMore primary care physicians (PCPs). Program eligibility criteria were aimed at identifying patients at risk for poor outcomes and unnecessary spending, as well as those most likely to benefit from complex care management. The criteria drew from analyses suggesting that combining predictive models, historical claims, and clinician judgment is the most effective approach to identifying patients for complex care management.

Eligible patients were first required to meet at least 1 of the following criteria: top 5% of total medical expenditures (TME) in the prior 12 months, top 5% of Chronic Illness Intensity Index (CI3) score, or care team member nomination. The CI3 score is a predictive model developed by Amerigroup that uses demographic, clinical, and pharmacy claims data to estimate a patient’s predicted cost in the subsequent 12 months compared with the average Amerigroup Medicaid patient.

Patients meeting 1 of the above criteria were then required to meet at least 1 of the following criteria: 2 or more inpatient (IP) admissions in the prior 12 months, 3 or more emergency department (ED) visits in the prior 12 months, or 2 or more chronic conditions.

Patients less likely to benefit from complex care management because of specific comorbidities (cognitive impairment, severe mental illness without medical comorbidity, active malignancy, pregnancy) or current residence in a long-term care facility were excluded.

Given limited resources, eligible patients were randomized in a 1:2 ratio to complex care management or usual care. Patients not continuously attributed to CareMore over the 12-month period following randomization were considered lost to follow-up. This could occur for several reasons: loss of Medicaid eligibility, enrollment in another TennCare plan, or transfer of care to a non-CareMore PCP.

The program was staffed by a multidisciplinary care team consisting of a community health worker (CHW), a social worker (SW), and a PCP. CHWs and other nontraditional healthcare workers have become increasingly common components of complex care management programs.

 CHWs, drawn from local communities, build longitudinal relationships with patients, provide context-specific activation and engagement, assist with care coordination, and help patients navigate the complexities of health systems and social programs.

 CHW interventions have been shown to improve health outcomes and reduce spending, especially among urban, underserved populations.

With guidance and support from the SW and the PCP, the CHW was responsible for patient outreach, engagement, activation, and accompaniment. The SW was responsible for counseling and brief interventions for patients with behavioral health needs and for coordinating referrals to social service agencies and other medical providers. The PCP was responsible for providing comprehensive care for acute and chronic conditions and for coordinating with specialists and inpatient providers.

Patients randomized to the complex care management program were contacted by the CHW via telephone and invited to enroll in the program. The CHW attempted to contact patients on 3 separate occasions, after which no additional attempts at contact were made.

Program activities were designed around a conceptual model of the drivers of poor outcomes and avoidable spending for high-need, high-cost Medicaid patients. Drawing on existing frameworks,

 these drivers were grouped into 4 intersectional domains: (1) medical complexity and health trajectory, (2) care coordination and engagement, (3) self-management and behavioral health, and (4) environmental and social supports. Within each domain, specific drivers of poor health and high costs were identified. For example, unstable housing was a driver within the environmental and social supports domain, and medication management was a driver within the medical complexity and health trajectory domain (see 

 for a complete list of domains and drivers).

Patients who enrolled in the program were scheduled for a 60-minute in-person intake visit within 7 days of enrollment. During the intake visit, the entire care team (ie, CHW, SW, and PCP) conducted a comprehensive assessment of the patient’s medical, behavioral, and social risk factors. The results of this assessment were used to identify and prioritize the most impactful and impactable drivers of poor outcomes and avoidable spending for each patient. Finally, the patient and the care team codeveloped a tailored care plan that outlined interventions, roles, and responsibilities for addressing each of the prioritized drivers. For example, the SW may be responsible for providing counseling and brief interventions for depression and for coordinating referrals to psychiatry. The PCP may be responsible for rapidly addressing poorly controlled diabetes or congestive heart failure. The CHW may be responsible for contacting patients to identify barriers to adherence and self-management. The patient may be responsible for applying for temporary housing assistance, logging physical activity, or checking in daily with the CHW/PCP to provide blood pressure or blood sugar readings.

After the intake visit, there was frequent, structured follow-up. The CHW called patients at least weekly to assess progress and identify and troubleshoot barriers. The care team met formally every week to review and update care plans. Patients were scheduled for monthly, in-person follow-up visits to review and revise the care plan. Patients admitted to an IP or skilled nursing facility were visited by a member of the care team, if possible. The CHW accompanied patients to specialist, social service, and other appointments as needed.

The program launched on March 1, 2017, and ran until February 28, 2018, at which point patients returned to usual care.

The primary outcomes were TME and IP bed days. Secondary outcomes included ED visits, IP admissions, care center visits, and specialist visits. The ED visit outcome measure excluded visits that resulted in an IP admission. The care center visit measure included any in-person visit for medical or behavioral health services at one of CareMore’s outpatient facilities. All outcomes were assessed over the 12-month period following randomization (March 1, 2017, to February 28, 2018).

The following covariates were assessed for each patient: age at randomization, gender, CI3 score at randomization, and rates of each outcome measure over the 12 months prior to randomization (March 1, 2016, to February 28, 2017). Spending and utilization rates were calculated from claims data provided by Amerigroup. CI3 scores were provided by Amerigroup. Demographic data were extracted from the CareMore electronic data warehouse.

Baseline characteristics for patients randomized to complex care management and those randomized to usual care were compared using standardized mean differences (SMDs).

 Proposed cutoffs vary, but SMDs greater than 0.1 or 0.25 are generally considered to represent an important difference between groups.

The effect of the program on the primary and secondary outcomes was analyzed through an intention-to-treat approach based on random assignment. Count-based utilization outcomes (IP admissions, IP bed days, ED visits, care center visits, and specialist visits) were modeled with Poisson regression, and the spending outcome (TME) was modeled with ordinary least squares regression. Both adjusted and unadjusted analyses were performed. Adjusted models included age at randomization, gender, CI3 score at randomization, and the corresponding outcome measure over the 12-month period prior to randomization (eg, the model for IP days included IP days from March 1, 2016, to February 28, 2017, as a covariate). Adjusted and unadjusted results were similar. Results from the adjusted models are presented below, with unadjusted results available in the 

 value of .05 was considered to indicate statistical significance. 

 values were adjusted for multiple comparison using the Holm-Bonferroni method.

 All analyses were performed using R version 3.5.1. The evaluation was implemented as a randomized quality improvement trial without institutional review board approval or waiver.

 depicts the flow of patients through the trial. A total of 253 patients met eligibility criteria and were randomized in a 1:2 ratio to complex care management (n = 93) or usual care (n = 160). More than half (56.9%) of the patients randomized to complex care management ultimately enrolled in the program. The most common reason for not enrolling was an inability for the CHW to reach the patient via telephone (37 of 40 patients). A small number of patients were reached but declined to participate in the program (3 of 40 patients). Across both groups, roughly 20% of patients were lost to follow-up in the 12 months after randomization. The intention-to-treat analysis included 71 patients in the complex care management group and 127 patients in the usual care group. Information on appointment-kept rates is provided in the eAppendix.

Baseline rates of spending and utilization were high. The average patient incurred TME of $22,714, spent 6.81 days in an IP facility, and visited the ED 3.30 times during the 12-month period prior to randomization. 

 compares baseline characteristics between patients randomized to complex care management and those randomized to usual care. Compared with patients in the usual care group, patients in the complex care management group were slightly younger (mean age of 43.8 vs 45.9 years), had lower predicted future costs (mean CI3 score of 8.56 vs 9.99), and visited the ED more frequently (3.82 vs 3.01 mean visits).

 compares adjusted rates of utilization and spending between patients randomized to complex care management and those randomized to usual care over the 12 months following randomization. The complex care management program was associated with lower TME (adjusted difference, —$7732 per member per year [PMPY]; 95% CI, –$14,914 to –$550; 

 = .014), and fewer specialist visits (adjusted difference, —1.35 PMPY; 95% CI, –1.98 to 0.73; 

 <.001). There was no significant impact on ED or care center visits.

The eAppendix includes summary vignettes for selected patients enrolled in complex care management. These are intended to supplement the quantitative findings from the primary analysis and offer additional detail on the implementation of complex care planning.

A complex care management program reduced spending and inpatient utilization among high-need, high-cost Medicaid patients. Patients randomized to complex care management had TME that were 37% lower than those randomized to usual care, an absolute reduction of $7732 per patient per year. This spending reduction appeared to be driven primarily by decreases in inpatient utilization—bed days were reduced by 59% and admissions by 44%.

Several potential mechanisms could explain the observed decreases in utilization and spending. Improved management of medical, social, and behavioral risk factors may have prevented acute exacerbations of chronic disease. Patient engagement and activation may have led to improved self-management and adherence. Finally, social and behavioral stabilization may have facilitated safe discharge planning, reducing the need for, or duration of, inpatient admissions.

Complex care management led to a small but significant decrease in specialist visits. Although prior studies of complex care management have not examined specialist use, data from primary care improvement initiatives suggest that enhanced primary care services can reduce downstream specialist utilization.

 In this study, specialist utilization could have been decreased by increased engagement with primary care, better management of chronic medical conditions in the primary care setting, or better communication and coordination between the PCP and specialists. Because there were no additional financial or nonfinancial incentives to reduce utilization in the complex care management group, it is less likely that the reduction in specialist utilization was due to restricted access.

Complex care management for high-need, high-cost Medicaid patients significantly reduced total medical expenditures and inpatient utilization in a randomized quality improvement trial.

Sandeep Palakodeti, MD, MPH

Articles

Value Realization: An Unattained Challenge for Integrated Practice Units

Impact of Complex Care Management on Spending and Utilization for High-Need, High-Cost Medicaid Patients