Archive for the ‘Risk Stratification’ Category

Providers and ACO Data Analytics: Too Much Information Is Not Helpful

November 22nd, 2016 by Patricia Donovan
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Collaborative Health Systems believes the health data it distributes to its physicians should speak to the challenges providers see in the market.

As the largest sponsor of Medicare Shared Savings Program (MSSP) accountable care organizations (ACOs), Collaborative Health Systems (CHS) has learned a number of lessons about the integration of data analytics and technology. Here, Elena Tkachev, CHS director of ACO analytics, outlines three challenges her organization has faced in the rollout of health analytics to its provider base, and some CHS approaches to these hurdles.

What are some of the challenges we have identified, and some solutions? Number one is the availability and access to timely and accurate data. This has been a challenge for us. As an insurance company, we have a very strong expertise and access to the claims information Medicare provides to us, but we did face the challenge of incorporating electronic medical records (EMRs) into our data. We have been taking a phased approach, where we continue only adding and enhancing our data. If you are not at a point where you’re ready to consume everything, it doesn’t mean you should not do it until you have all the pieces together. It’s better to start with something and then you can grow from that point and improve it.

The second is related to the technology and capability—the ability to aggregate all this different data from different resources and have it be meaningful. For us, it’s really an investment in having strong technology data architect subject matter experts as well as the tools that can help us with that.

The third is display of meaningful results. This has been a challenge and we’ve reiterated it. Since I first started at CHS, the reports have drastically changed, because we learned from our providers that too much information is not helpful; just giving someone a spreadsheet with a lot of columns is not very useful.

Providers would rather see information summarized, and less is more. It’s really important to have information be very clear. The data needs to speak to the challenges the providers see in the market.

Source: Health Analytics in Accountable Care: Leveraging Data to Transform ACO Performance and Results

http://hin.3dcartstores.com/Health-Analytics-in-Accountable-Care-Leveraging-Data-to-Transform-ACO-Performance-and-Results-_p_5185.html

Health Analytics in Accountable Care: Leveraging Data to Transform ACO Performance and Results documents the accomplishments of CHS’s 24 ACOs under the MSSP program, the crucial role of data analytics in CHS operations, and the many lessons learned as an early trailblazer in value-based care delivery.

HINfographic: The Rising Risk: Harvesting Population Health’s Low-Hanging Fruit

October 5th, 2016 by Melanie Matthews

Paramount to population health management success under risk-based contracts is strategic oversight of the ‘rising risk’—individuals with two or more unmanaged health conditions. One quarter of respondents to the 2016 Population Health Management survey by the Healthcare Intelligence Network zero in on their own ‘rising risk’ populations.

A new infographic by HIN examines the health risks served by population health management programs and how population health management services are delivered.

2016 Healthcare Benchmarks: Population Health Management2016 Healthcare Benchmarks: Population Health Management analyzes responses of more than 100 healthcare organizations to HIN’s third comprehensive industry survey on PHM trends administered in spring 2016. It delivers the latest metrics on current and future PHM initiatives, providing actionable data on the most effective PHM tools and workflows, risk identification strategies, communication and engagement tools, program delivery modalities, results and challenges, and much, much more.

2016 Healthcare Benchmarks: Population Health Management is supported with more than 50 graphs and tables and describes many successes respondents have achieved with a PHM approach. Participating organizations also weigh in on the sustainability of a population health management approach. Click here for more information.

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Home Visits Validate Predictive Analytics and 10 More 2016 Risk Stratification Trends

August 30th, 2016 by Patricia Donovan

Assuring data integrity is the top challenge to health risk stratification, according to a July 2016 healthcare benchmarks survey.


Two key trends emerging from a July 2016 survey on Stratifying High-Risk Patients highlight the need to occasionally eschew sophisticated tools in favor of basic, face-to-face care coordination.

As one survey respondent noted, “A key element [of stratifying high-risk patients] is building a trusting face-to-face relationship with each patient, knowing what they want to work on, coaching them and activating them.”

The first learning gleaned from the survey’s 112 respondents is that, despite the prevalence of high-end risk predictors, algorithms and monitoring tools, clinicians must occasionally step into the patient’s world—that is, literally enter their home—in order to capture the individual’s total health picture.

Fifty-six percent of respondents make home visits to risk-stratified patients; a half dozen identified the home visit as its most successful intervention for risk-stratified populations.

That inside look at the patient environment illuminates data points an electronic health records (EHRs) might never bring to light, including socioeconomic factors like limited mobility that could prevent a patient from keeping a follow-up appointment.

“I never know until the moment I enter the home and actually see what the environment is like whether we correctly predicted the need for high intervention (and get a return on it),” commented one respondent.

The second trend in risk stratification is the emerging laser focus on ‘rising risk’ patients, an activity reported by 72 percent of respondents. This scrutiny of rising risk populations helps to prevention their migration to high-risk status, where complex and costly health episodes prevail.

Other data points identified by the 2016 Stratifying High-Risk Patients survey include the following:

  • Almost four-fifths of 2016 respondents have programs to stratify high-risk patients, and the infrastructures of more than half of these initiatives utilize clinical analytics, predictive algorithms, EHRs and other IT tools to manage care for high-risk patients.
  • The reigning health risk calculator continues to be the LACE tool (Length of stay, Acute admission, Charleston Comorbidity score, ED visits), used by 45 percent in 2016, versus 33 percent two years ago.
  • For more than a quarter of 2016 respondents, assuring data integrity remains a key challenge to risk prediction.
  • A case manager typically has primary responsibility for risk stratification, say 52 percent of respondents.
  • Diabetes is the most prevalent clinical condition among high-risk patients, say 47 percent.
  • At least 70 percent report reductions in hospitalizations and ER visits related to risk stratification efforts.
  • Improvement in the highly desirable metric of patient engagement is reported by 74 percent of respondents.

Click here to download an executive summary of survey results: Stratifying High-Risk Patients in 2016: As Risk Prediction Prevails, Industry Eyes Social Determinants, Rising Risk.

Health Coaching Success Metrics and 8 More Behavior Change Benchmarks

July 7th, 2016 by Patricia Donovan

Satisfied clients and participants on track for goal attainment are two hallmarks of a can't-lose coaching initiative.

Satisfied clients and participants on track for goal attainment are two hallmarks of a can’t-lose coaching initiative.

What are the hallmarks of a winning health coaching strategy? The answer depends on what’s being measured: the effectiveness of the individual coach, the participant’s progress, or overall program success.

That’s the feedback from 111 healthcare organizations responding to the 2016 Health Coaching Survey by the Healthcare Intelligence Network.

If you’re looking to measure the health coach’s success, then client satisfaction is the best indicator, say 27 percent of these respondents.

On the other hand, for a gauge of an individual’s progress, look to the participant’s goal attainment, report 78 percent.

This same metric—goal achievement—is also the best indicator of program success as a whole, agree 64 percent.

The May 2016 survey documented a number of other health coaching benchmarks, including the following:

  • Motivational interviewing is a coach’s top tactic to effect behavior change, say 83 percent.
  • All-important ‘face time’ with coaches is plentiful: 47 percent embed or co-locate health coaches at points of care, with most onsite coaching occurring in primary care offices (50 percent) or at employer work sites (50 percent).
  • Nine percent even embed health coaches in hospital emergency rooms.
  • While a majority focuses on coaching high-risk individuals with multiple chronic illnesses, 51 percent now extend eligibility for health coaching to individuals stratified as ‘rising risk.’
  • Nearly half of respondents—48 percent—offer health coaching to patients and health plan members with behavioral health diagnoses.
  • Reflecting the surge in telehealth, 12 percent of respondents offer video health coaching sessions to clients.

Download an executive summary of the 2016 Health Coaching survey.

2016 Population Health Management Snapshot: Most Interventions Telephonic and 9 More PHM Trends

May 19th, 2016 by Patricia Donovan

Most population health management interventions are conducted telephonically, according to HIN's latest PHM metrics.

The majority of outreach in the burgeoning field of population health management is delivered telephonically, according to 84 percent of respondents to an April 2016 Population Health Management (PHM) survey by the Healthcare Intelligence Network.

This third comprehensive PHM assessment also determined that data analytics use in population health management continues to rise, though more slowly than it did from 2012 to 2014, when EHR and registry use tripled.

Additionally, the survey found that 70 percent of respondents have committed to population health management, up from 56 percent in 2012. At the same time, many lament payor reluctance to cover essential PHM services like health coaching and group visits they see as critical to PHM success.

To accrue clinical and financial gains from PHM’s data-driven, risk-stratified care coordination approach, 90 percent provide chronic care management (CCM) services, a strategy that results in PHM ROI between 2:1 and 3:1 for 12 percent of these CCM adopters.

In condition-specific PHM metrics new for 2016, diabetes tops the list of health targets for PHM interventions, say 88 percent.

A health risk assessment (HRA) remains the primary instrument for identifying individuals for PHM interventions, say 70 percent, up from 64 percent in 2014.

Also paramount to PHM success under value-based healthcare reimbursement is strategic oversight of the ‘rising risk’— individuals with two or more unmanaged health conditions. One quarter of 2016 respondents focus PHM attention on their ‘rising risk’ populations, the April 2016 survey determined.

In recent years, population health management (PHM) has ranked as the healthcare space richest with opportunity, according to HIN’s annual industry trends snapshots.

Download an executive summary of 2016 Population Health Management survey results.

Horizon Episodes of Care Program Prototype for Value-Based Specialty Care and Reimbursement

April 21st, 2016 by Patricia Donovan

Horizon BCBS-NJ's Episodes of Care program engages specialists across a suite of nine episodes.

Imagine a value-based healthcare payment model in which the sole financial hazard to specialist providers is the risk of amassing additional revenue.

Further, envision a scenario in which these specialists are invited to design their payment program, from the model’s intent to key quality metrics.

Those are some highlights of Horizon Blue Cross Blue Shield of New Jersey’s Episodes of Care (EOC) program, a value-based model designed to focus specialists on the provision of quality- and value-based care across nine separate episodes, from joint replacement to hysterectomy to oncology.

Hailed as a national leader in advancing the episodes model as a prototype for value-based specialty care, Horizon is careful to distinguish its EOC program from a bundled payment initiative, for two key reasons.

“First, our EOC program is a quality-based program; it’s not only about the payment or payment structure,” explained Lili Brillstein, director of the Horizon Episodes of Care program during a recent webinar, Episodes of Care: Improving Clinical Outcomes and Reducing Total Cost of Care Through a Collaborative Payor-Provider Relationship.

Secondly, bundled payments typically refer to a prospective model in which a bundled amount of money is paid to a provider or group of providers in advance of services being delivered, while Horizon’s retrospective model pays providers after services have been provided.

The upside-only nature of Horizon’s retrospective model contributes to the program’s collaborative nature, Ms. Brillstein added. “If the metrics are met, savings are shared. If the metrics are not met, we’re not punishing our partners.”

There is other evidence of collaboration and of Horizon’s desire to see the providers succeed in the EOC program. One example is the payor’s use of case mix-adjusted budgets at the practice level rather than the prevalent member-specific risk-adjusted budgets. “This budgeting allows Horizon to create an opportunity for providers to move the needle [on a metric], and benefit from that. The opportunity for cost savings and shared savings also is dramatically improved.”

Another case in point is Horizon’s invitation to prospective providers to talk through the episode’s construct, intent and design prior to its launch.

Horizon’s engagement of providers in the EOC program has “changed the spirit of the relationships between the payor and the provider,” Ms. Brillstein noted. “It’s like nothing I’ve ever seen before. Our provider partners have become our ambassadors for the program.”

Select EOC results presented during the webinar indicated that outcomes are better for EOC partners—in the area of reduced readmissions, for example—than they are for physicians not in the EOC program.

Horizon expects to launch at least three more episodes in 2016, including a Crohn’s Disease episode that will take into account behavioral health services for those members. While the payor fully expects to move to a prospective model, it believes its current EOC model is preparing them for that eventuality, softening the transition from fee for service to prospective payments.

“[That transition] doesn’t just happen. You don’t sign the paper, and suddenly know what to do. It is an evolutionary transformative process,” concluded Ms. Brillstein.

Click here to listen to an interview with Lili Brillstein: Horizon BCBSNJ Episodes of Care: No-Risk Retrospective Model Paves Way for Value-Based Migration

CHS on Data Analytics in Accountable Care: “No Matter What Happens, This Change is Coming”

February 11th, 2016 by Patricia Donovan

Collaborative Health Systems, the largest sponsor of Medicare ACOs in the United States, recently rolled out an analytics and dashboard portal for its 3,200 providers.

Attention, please. Two aggressive milestones to migrate Medicare providers to value-based healthcare are on the horizon:

  • In 2016, CMS expects 30 percent of Medicare fee-for-service (FFS) reimbursement to be tied to alternative payment models such as accountable care and bundled payments.
  • Also this year, the federal payor wants 85 percent of Medicare FFS payments to be based upon quality metrics.

“If you are a provider, or working with providers who accept Medicare beneficiaries, it’s really important to know these changes are coming,” advises Elena Tkachev, director of ACO analytics for Collaborative Health Systems (CHS). “It will be the responsibility of physicians to participate in these payments because no matter what happens, this change is coming.”

Ms. Tkachev detailed the power of data analytics to drive CHS’s success in accountable care during Data Analytics in Accountable Care: Strategies and Case Studies, a January 2016 webinar from the Healthcare Intelligence Network now available for replay.

As the largest sponsor of Medicare Shared Savings Program (MSSP) ACOs in the United States, CHS has a firm handle on HHS’s value-based agenda. The organization manages 24 MSSP ACOs, nine of which generated savings of nearly $27 million in 2014, and one that has been accepted as a Next Generation ACO, the newest Medicare accountable care model.

And with CMS expectations for value-based reimbursement slated to rise over the next two years, expectations for data analytics to improve care and costs related to Medicare beneficiaries have never been higher.

“Today, physicians are being measured through claims and the clinical metrics on the population they serve. We see the main responsibility of analytics as providing simple access to actionable, timely and relevant information to help clinicians make better decisions, improve quality of care and enhance the patient experience.”

Despite the magnitude of its enterprise, CHS believes its future in accountable care rests upon its primary care physicians (PCPs), which it views as “quarterbacks of care” for its more than 280,000 Medicare beneficiaries.

To foster quality improvement, CHS equips PCPs with an arsenal of analytics capabilities. So that its 3,200 providers can tap into CHS’s massive storehouse of CMS, claims, lab, risk stratification and care coordination data collected on its 24 Medicare Shared Savings Program (MSSP) ACOs, the health system recently rolled out an analytics and dashboard portal.

These tools enable providers to monitor the aggregate health of their populations as well as their own performance, even giving providers the ability to track their own performance over time and contrast it with other clinicians’—a capability that pleases CHS’s more competitive physicians, Ms. Tkachev notes.

Frequent webinar training keeps provider analytics’ use sharp, and dashboard-generated reports and scorecards help physicians to monitor and enhance quality performance and improve patient outreach, Ms. Tkachev explained.

Despite its significant success, CHS still encounters the perennial challenges of access to timely and accurate data, aggregation abilities, and the display of meaningful results. Ms. Tkachev shared some CHS tactics to resolve these issues, including soliciting feedback on the tools from providers who use them.

Listen to an interview with Elena Tkachev on data analytic’s potential to drive annual wellness visits and boost beneficiary attribution.

Longitudinal Care Plans, Risk Scores Raise Patient Engagement for MSSP ACO’s Complex Population

October 6th, 2015 by Patricia Donovan

A top-performing MSSP in 2014, the Memorial Hermann ACO has successfully engaged its Complex Care population via a collaborative care coordination approach.

The Memorial Hermann ACO may have been one of 2014’s top-performing Medicare Shared Savings Programs (MSSPs), but the health system’s commitment to achieving quality outcomes was solidified more than eight years ago, when its own physicians asked for a clinically integrated physician network.

Memorial Hermann complied, developing a set of tools, training and care models to not only support the physicians but also reflect payors’ needs: chief among them, initiatives that could boost patient engagement.

Today, the Memorial Hermann ACO has a patient-centered care delivery strategy built on teamwork and collaboration. The Texas ACO is proud to point to a patient engagement rate of 74 percent for individuals enrolled in Complex Care, an initiative for individuals with long-term, multiple chronic conditions that has significantly reduced cost and hospital lengths of stay for participants.

This patient engagement measure represents members who consent to participate in the program and remain engaged for 30 days, explained Mary Folladori, RN, MSN, FACM, CMAC, system director of care management at Memorial Hermann Physician Network and ACO, during Care Coordination in an ACO: Managing the Population Health Continuum from Wellness to End-of-Life, a September 2015 webinar from the Healthcare Intelligence Network now available for replay.

Ms. Folladori provided an overview of the ACO’s care coordination strategy that in 2014 generated savings of nearly $53 million in the MSSP program, resulting in a health system payout of almost $23 million. The ACO’s performance earned Memorial Hermann a MSSP quality score of 88 percent.

Some high points from Memorial Hermann’s ACO strategy include the following:

  • Embedding of care coordinators into the ‘micro culture’ of a physician practice, its community and the members served by the practice;
  • Strategic use of a data warehouse to identify vulnerable members early and link them with needed health services;
  • Development of comprehensive risk scores derived from multiple sources for Complex Care patients; and
  • Creation of longitudinal care plans that follow Complex Care patients for up to 18 months and help to transition them back to a baseline level of functioning.

In wrapping up observations on Memorial Hermann’s quality-driven approach, Ms. Folladori quoted its CEO, Chris Lloyd: “The success that has been found within our ACO is deeply based on a collaborative approach to care. It has been cultivated over eight years with our commitment to clinical integration. We all strongly believe that without that strong clinically integrated physician network, without our physicians driving those quality outcomes, we would not have been as successful as we have.”

With so much emphasis on quality and outcomes, it’s no wonder participation today in the Memorial Hermann ACO is by invitation only—and only after a practice has passed an assessment.

New Population Health Management Strategy for ‘Emergent-Risk:’ Arrest Trajectory of Compounding Conditions, Cost

June 11th, 2015 by Adam Kaufman, PhD, President & CEO, Canary Health

Adam Kaufman, PhD, president and CEO, Canary Health


For many years, healthcare organizations have invested in two approaches to population health management: First, wellness management for healthy populations who want to prevent illness, and second, disease/case management for the very sick who must adhere to physicians’ prescribed medical care.

Now, organizations are filling the gap between the healthy and very sick by investing in the ’emergent-risk’ population—adults with one or more pre-chronic or early-stage chronic conditions. Population goal: arrest the trajectory of compounding chronic conditions that compound declines in quality of life and compound increases in cost of care.

Consumer Engagement with Digital Health Self-Management

For many years, the natural worsening of chronic illness has been the focus of academic institutions, a few digital health innovators, and pioneering health plans. As a result of these efforts, a new class of digital health programs is now proven to arrest the trajectory of chronic illness. It started when Stanford’s Patient Education Research Center discovered that self-efficacy (a person’s belief in his or her own ability to achieve goals) had the strongest correlation to improved health outcomes. With this discovery, the concept of health self-management was born. Thus began the drive to engage and impact consumers.

Over the years, healthcare organizations across the United States began offering Stanford’s in-person workshops with notable improvements in health and with measurable reductions in cost of care. According to Stanford research, participants reduced pain, fatigue, depression, and A1C—and reduced ER visits and days in the hospital for up to one year.1 While results were impressive, the programs had one drawback for managers of population health: due to the cost and complexity of in-person delivery, the programs were hard to scale.

As web-based technologies advanced, the research community began to search for ways to harness digital innovation to scale evidence-based programs. In 2006, researchers at the University of Pittsburgh tested the first digital self-management program aimed at diabetes prevention. This digital translation2 of the NIH’s Diabetes Prevention Program3 (DPP) delivered outcomes that mirrored those achieved with the DPP’s in-person, self-management intervention—but at a fraction of the cost.

Data-Driven Insight: The Compounding Effect

As health plans began to adopt digital health self-management, data revealed deeper insights into individuals with prechronic and early-stage chronic conditions. Data from years of Canary Health research with a pioneering health plan shows that chronically ill patients add, on average, a new chronic condition every two to three years. These compounding conditions drive compounding increases in the cost of care—specifically in the areas of pharmacy, medical equipment, and outpatient care.

And without intervention, according to Advisory Board, each year 15 to 20 percent transition to the high-risk population of very sick individuals who require high-cost medical care. This trajectory of chronic illness translates into an additional $1,000-$3,500 in expenses per person, per year. With 80 million adults in this population, that’s an additional $80-$280 billion in costs each year to the U.S. healthcare system. If healthcare leaders don’t prevent this compounding effect, both health plans and providers will hit a financial tipping point where the cost of care puts both margins and mission at risk.

Proven Outcomes: Arresting the Trajectory of Chronic Illness

As health plans began to measure the ROI of digital interventions, a deeper look at results revealed the broader and longer-term impact4 of digital health self-management programs. For emergent-risk populations, the interventions accomplished the following:

  • Halted the progression of individuals’ preconditions to diabetes, heart disease and other conditions;
  • Slowed the progression of existing conditions, and;
  • Prevented compounding conditions and compounding costs of care.

On the heels of this research, the goal became “trajectory impact” at a population level: programs for the emergent-risk population are now designed to arrest the trajectory of compounding conditions and compounding costs of care. With digital technology’s ability to scale, entire emergent-risk populations can be targeted immediately for outreach and intervention.

And with the lower cost structure of digital technology, health self-management interventions can generate a return beginning one year after the intervention and continuing over the lifetime of each individual.

Citations:
1 Lorig K, Sobel DS, Stewart AL, Brown BW, Bandura A, Ritter P, González VM, Laurent DD, Holman HR. Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: a randomized trial. Med Care 1999; 37(1):5-14. View the abstract at http://www.ncbi.nlm.nih.gov/pubmed/10413387.

2 The digital translation of the DPP was described in the journal article from McTigue, et al. Using the Internet to Translate and Evidenced Based Lifestyle Intervention into Practice Telemedicine and e-Health Vol 15#9 November 2009. Read more at http://www.ncbi.nlm.nih.gov/pubmed/19919191.

3 The Diabetes Prevention Program (DPP), a major, multicenter clinical research study, discovered that modest weight loss through dietary changes and increased physical activity sharply delayed the onset of type 2 diabetes among pre-diabetic patients. The study showed that taking metformin also reduced risk, although less dramatically. Read more at http://diabetes.niddk.nih.gov/dm/pubs/preventionprogram.

4 A two-year, controlled matched study by Canary Health for GEHA, a self-insured, not-for-profit association providing health and dental plans to federal employees and retirees and their families through the Federal Employees Health Benefits Plan. For a briefing on case study results, contact akaufman.canaryhealth.com

About the Author: Adam Kaufman is a health economist and the president and CEO of Canary Health. He speaks to audiences nationwide on the accelerating trend of chronic illness and the financial tipping point that threatens the margins and mission of American healthcare organizations and advises healthcare senior management teams on making strategic investments in their emergent-risk populations. Prior to serving Canary Health as President and CEO, Adam served as general manager of dLife’s Healthcare Solutions division. Kaufman has served as adjunct assistant professor in the economics department at the University of Southern California, and he is the author of a data analytics patent that predicts consumer engagement.

HIN Disclaimer: The opinions, representations and statements made within this guest article are those of the author and not of the Healthcare Intelligence Network as a whole. Any copyright remains with the author and any liability with regard to infringement of intellectual property rights remain with them. The company accepts no liability for any errors, omissions or representations.

AltaMed Constructs Business Case for Care Coordination Team

May 19th, 2015 by Patricia Donovan

The AltaMed multidisciplinary care team targets dual eligibles with multiple chronic conditions and functional and cognitive impairments.

When the largest FQHC in the country set out to quantify the contributions of its multidisciplinary care team, it found the concept didn’t fit neatly into return on investment models.

So at budget time this year, leaders of AltaMed Health Services Corporation’s care coordination model for its highest risk patients identified seven performance metrics to present to its CFO, explained Shameka Coles, AltaMed’s associate vice president of medical management, during A Comprehensive Care Management Model: Care Coordination for Complex Patients, a May 2015 webinar now available for replay.

The evidence that ultimately secured funding for the care coordination project’s next phase included the model’s impact on specialty costs, emergency room visits, and HEDIS® measures, among other factors.

These were all areas examined early on, back in phase one, when the care coordination team set a number of strategic goals that aligned with the corporation’s five pillars: service, quality, people, community and finance.

Rolled out in four phases beginning in July 2014, the model is aimed at AltaMed’s dually eligible population— Medicare-Medicaid beneficiaries with high utilization, multiple chronic conditions, and multiple functional and cognitive impairments, Ms. Coles explained.

Phase one of the project was devoted to understanding and engaging the duals population via telephonic and print outreach, then developing a care management model reflecting both Triple Aim and patient-centered medical home goals. (The 23-site multi-specialty physician organization in Southern California has earned Joint Commission primary care medical home designation.)

At the heart of the model is a multidisciplinary care team, which counts a care coordinator, clinic patient navigator and care transitions coach among its eleven roles. Patients are stratified as high, moderate or low risk and matched to risk-appropriate interventions.

“Each member is activated based on where the patient is at in the continuum of care,” noted Ms. Coles, who also reviewed team member roles and responsibilities and a host of complementary programs supporting care coordination during the May 2015 program sponsored by the Healthcare Intelligence Network.

In phase two, focused on development of end-to-end workflows, staff assessments and ratios, and team training, AltaMed hired an educator, fleshed out the patient navigator role, and examined integration of behavioral health and long-term services and supports (LTSS).

Phase three triggered a deeper dive into case manager caseloads and utilization patterns as well as several quality improvement activities.

Now in phase four, the goal of AltaMed’s care coordination model is to ensure it can reflect a financial impact. “We’ll look very closely at our per member per month cost and our inpatient metrics,” Ms. Coles concluded.