Managing Managed Care

Our inequitable, inefficient, oftentimes uncaring health care "system," revealed. -- Jeffrey G. Kaplan, M.D., M.S.

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Data Science Strategy; the Managed Care Method

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Healthcare reform is really about managing the care so that it is optimally accessible, reasonable and reliable quality (esp. less variation), and is more efficient and cost-effective.  Here's what a fellow Medical Director and I wrote about this in 1994, still very relevant:

"Managed care is not a panacea for at least two reasons: (1) it lacks a proven method of achieving results, and (2) it lacks a firm foundation of information about customers and service."  Nevertheless, it is a fundamental for health care quality and cost-efficiency. And let me be clear, "successful" means "accountable," or "health maintenance" or "preferred provider," etc. Unfortunately, managed care "tells little about the continuity of care, less about clinical efficacy, and nothing about the prediction of illness…Furthermore, when managed care reviews illness management, it does so without paying much attention to severity of illness or the quality of life… [Also,] variation in medical practice remains unexplainable.” 

“Managed care’s critical analytic technique involves, in a word, ‘outliers.’ Can management be effective when it focuses on the tails of a quality/cost population distribution curve, instead of shifting the entire curve toward better outcomes?"

In order to shift the current payment system from procedure volume to rewarding quality, “Consumers should have choices about which health care provider to use, but they should be required to pay significantly more if they choose lower-value providers when higher-value providers are available.”*

* It should be noted that a “'high-value' provider is one who delivers the same or better-quality services at lower charge than their peers,"

Kaplan JG, Sneider JS. “Managed health care plans: sheep in wolves’ clothing.” Medical Interface (pub. Medicom Int'l) Feb., 1994:7(2):61-4, 66,84.

Data are critical

To improve the quality of care, reduce unwarranted variation. To learn about that variation, however, define what is of value and how to emulate (i.e., the best practices).  Be careful to avoid having a 'silo' mentality meaning have a longitudinal picture and wherever possible, acuity-adjust all process and outcome data. Technically, normative and comparative statistics requires all that, but simply put, all I am taking about is being able to make 'apples to apples' comparisons and to learn what works and what does not (and pay better when we are doing well by the patient, by the way); Finally, the schwerpunkt of all of this is to measure and manage/manage and measure. As Dr. Sneider and I said in our 1994 paper: "To accomplish these objectives, proper data must be collected from the beginning. If not, in the end we will not know if we had done well, except by measurement of costs against budget. This, however, is precisely the trap managed care programs, government planners, and regulators should assiduously avoid.” 

Data Science Strategy

"Harnessing the full potential of data requires developing an organization-wide data science strategy, which] will help achieve both precision medicine (helping to tailor treatments to patients) and the creation of learning health systems (helping to predict outcomes and identifying specific areas for improvement [See Oxford Academic Journals {; last access failed 1/04/2017}).

Ideally, every decision a provider makes about a patient should be informed by the data of both that specific patient and other similar patients. In a learning health system, prior experiences improve future choices."

Cresswell KM, Bates DW, Sheikh A. Usher [Institute of Population Health Sciences and Informatics, University of Edinburgh; Harvard Medical School.]  "Why Every Health Care Organization Needs a Data Science Strategy" March 22, 2017

All good managers know the importance of the centrality of data, how it is mined and its granularity.  Dr. Norbert Goldfield, Medical Director of 3M adds that more detailed categorization of data is vitally necessary–one way to do this is to have a risk adjustment classification system that has a robust predictive power.  In "When patients can’t read: Accounting for socioeconomic disparities in risk adjustment," he writes that chronically ill patients often experience greater severity of illness if they are impacted by, for example, lack of confidence [1], illiteracy, low socio-economic status or homelessness.  Case-mix adjustment models should therefore be expanded to capture additional data elements such as socioeconomic disparities in order to equitably adjust the cost of care, especially in prospective payment and monitor or comparing patient outcomes.  

Note:  posted "When patients can't read: Accounting for socioeconomic disparities in risk adjustment" on Jan. 2, 2014.

[1] Wasson, J. A patient-reported spectrum of adverse health care experiences: harms, unnecessary care, medication illness, and low health confidence. J Ambul Care Manage. 2013 Jul-Sep;36(3):245-250.

[2"Clinical Categorical vs. Regression Based: Understanding classification system fundamentals," September 23, 2013. Blog by Norbert Goldfield, MD, Richard Fuller.

[3] "The success of any payment system that is predicated on providing incentives for cost control is almost totally dependent on the effectiveness with which the incentives are communicated… Central to the success of the Medicare inpatient hospital prospective payment system is that DRGs have remained a clinical description of why the patient required hospitalization." (Federal Register, Vol. 66, No. 174; 9/7/2001/Rules and Regulations) p 46904;

Using "Predictive Analytics to Determine Next Year’s Highest-Cost Patients"

"There are very real challenges for providers and health plans to maintain a viable business model while caring for patients with complex needs. Kaiser Permanente is leaning in, by seeking opportunities to deliver more of the high-value care that patients need and want — care that is coordinated, empathetic, and patient-centered, and that allows patients to stay at home."

Care Redesign, blog post by Shah NR, Davis AC, Gould MK, Kanter MH, Dec. 1, 2016 for N Eng J Med's NEJM Catalyst

"At the end of the day, healthcare reform can and will lead to health improvement for individual human beings (ibid). Everyone hopes that the number of uninsured continues to decrease. Building on the progress in coverage that has recently occurred and the recommendations of the recently published Academies report, we are confident that this can occur if CMS can firmly grasp the outcomes mantle in a transparent manner with incentives that are fair to all healthcare providers, especially safety net institutions."

Fuller R, Goldfield N [3M] "An essential next step for healthcare reform: Ensuring the future of safety net institutions." Pub. Aug. 17, 2016


Monetizing Data

Alex Macneil (MuleSoft) whose responsibilities include application program interface (API) Integration & Enablement states: "Unlocking the value of ..... data requires a deliberate API strategy, coordinated across lines of business and legacy data silos. The amount of data, devices, and applications will continue to grow" – [he asks] "Will your connectivity strategy keep pace?"

See the link, Treating Information as an Asset and/or follow on Twitter using #GartnerEIM to learn why in the Gartner report, they predict that "90% of companies will have a dedicated Chief Data Officer (CDO) by 2019 dedicated to achieving direct monetization of data."

Measuring outcomes is the inevitable way to prove you or your practice are doing right by patients.  It, therefore, should come at no surprise that you will need data.  Here follows a short list of primary sources of data that are available by hand or by computer.  You say it seems like a lot of work?  Yes, but you must be prepared to justify, if not maximize what they–the insurer, the patient, the business arm of your enterprise, etc. pays you, speaks to your performance quality, builds your reputation.

  1. Insurance beneficiary enrollment data, which is commonly used to identify the demographics of your patients, vitally important for acuity-adjustment as in comparative analysis and normative statistics.
  2. Administrative claims data tells something about accessibility, utilization and costs. It is the easiest, most accessible data, but be careful, it may be up-coded to justify referrals, tests, treatments or lack of progress managing a health condition.  I'm not pointing fingers, but up-coding does maximize revenue and it makes patients seem sicker than they really are (again, at higher acuity or risk than they really are).
  3. Electronic Health Record (EHR) – can provide clinical detail, not found in claims data, such as "lab findings (e.g., HbA1c and LDL), physical measures (e.g., blood pressure or BMI) or prescriptions written (to assess patient compliance)."
  4. Patient surveys – can provide "qualitative data such as patient-reported satisfaction with care or health-related attitudes and behaviors." .... These data help in the assessment of if not improvement of the patient experience. Surveys useful for this type of evaluation include the Consumer Assessment of Healthcare Providers and Systems (CAHPS), How’s Your Health and Patient-Reported Outcome Measures (PROMs) [Example: The Fact B+4 Questionnaire, FACIT Measurement System–David Cella, PhD] 

Paul LaBrec. "How can we tell if patient-centered medical homes are working?"  3M Health Care / Health Information Systems / Inside Angle / Blog / How can we tell if patient-centered medical homes are working? 2/24/2016


Please see the end of the full post, above—"Health Care Coverage under the Affordable Care Act – A Progress Report" by  David Blumenthal, M.D., M.P.P., and Sara R. Collins, Ph.D. in the N Eng J Med; July 2, 2014DOI: 10.1056/NEJMhpr1405667

P.S. I had hoped to see more outcome measurement and management in this Progress Report.

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