A patient-centric perspective on population health The psychosocial - - PDF document

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A patient-centric perspective on population health The psychosocial - - PDF document

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5/31/2017 An Optum Company Alejandro Reti, MD Chief Medical Officer; Optum Analytics Social determinants of health and population health analytics Alejandro Reti, MD Chief Medical Officer, Optum Analytics A patient-centric perspective on

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An Optum Company

Alejandro Reti, MD Chief Medical Officer; Optum Analytics

Social determinants of health and population health analytics Alejandro Reti, MD Chief Medical Officer, Optum Analytics

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An Optum Company

  • The psychosocial foundations for good (or poor) health
  • Evolution and maturation of analytics in service of population health

management

  • Patient reported and collected data in service of patient engagement and

patient directed care

A patient-centric perspective on population health

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Images courtesy of Harry Burns, MD Scotland NHS

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An Optum Company

Scotland saw a substantial relative decrease in life expectancy beginning in the 1950s

Life expectancy: Scotland & other Western European Countries, 1851-2005

Source: Human Mortality Database

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Images courtesy of Harry Burns, MD Scotland NHS

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An Optum Company

The coherence theory of human health

For health to exist, life must be:

  • Comprehensible
  • Manageable
  • Meaningful

This leads to motivation & engagement When any of these do not exist, it leads to chronic stress

Aaron Antonovsky

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An Optum Company

Evidence from all corners – environmental and psychological stress translates into physical stress

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10 20 30 40 50

Months of Orphanage Rearing

*linear trendline

Evening Cortisol Levels Increase with Months of Orphanage Rearing *

The Founders’ Network 2 4 6 8 10 12 14 16 18 20

08-8.30 10-10.30 12-12.30 14-14.30 16-16.30 18-18.30 20-20.30 22-22.30

nmol/l Higher Grade Lower Grade

Stress and grade of employment: men

Time of day Salivary cortisol levels

Images courtesy of Harry Burns, MD Scotland NHS

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Engaging patient reported data helps us move upstream

A window into the health, wellness, and preparedness of the whole person to not only deal with, but thrive within, their life circumstance will help us better address that person’s health needs

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What are ACOs focusing on today?

Transitions in Care Managemen t Gaps In Care Managemen t Complex Care Managemen t

Reduction of Preventable Hospitalizations

Readmission Prevention Scorecards & Dashboards Identification

  • f High-Cost

Patients HCC Code Accuracy Patients Missing Basic Care

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Risk characterization and stratification are common building blocks for prospective patient care

Who is highest risk? Why are they this way – what is driving that risk? What can I do to lower that risk? For how long have they had this risk profile?

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A brief retrospective of risk stratification techniques reveals a focus

  • n increasing precision, timeliness, and relevance

High (3-5%) Rising (10-15%) Well

A typical methodology – 12 point scale

  • Age
  • Utilization (IP, ED, ambulatory)
  • Current active meds
  • Chronic conditions
  • Some include concepts like disease

control, care gap counts and other measures for “actionability” or relevance to improve the relevance of the derived patient list

While this methodology is simple to implement and understand, it has disadvantages:

  • It is backward looking and not particularly predictive
  • “Risk” or outcome not clearly defined – thus people tend to

mean different things when they talk about it

  • Metrics designed to capture patients likely to need, and

respond to, intervention can end up filtering out patients who still have very high risk of bad outcomes

Rising risk – single scale

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A real world patient population selected using these criteria will have meaningful utilization and chronic disease

Average age of 71

3 ED visits and 2 hospitalizations in the last year

Saw an outpatient provider 10 times in the last year Taking 14 different medications Has 2 chronic conditions

Costs about 1.5-2x the average beneficiary

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Predictive models introduce a prospective view on outcomes of inherent interest

Prediction has introduced a number of benefits to the risk stratification process:

  • Far superior precision in identifying target groups (capture

>50% of events in top 10-15% of patients)

  • Model event-based risk profiles with higher fidelity

Future cost Likelihood

  • f

hospital- ization

20 40 60 80 100 0-79% 80-89% 90-95% >95%

Patients by risk of CHF-related hospitalization

Patient count

Initial hospitalization Time Readmission Period of heightened vulnerability & management needs

High cost with contribution from hospitalizations

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Clinical data greatly improves predictive model precision

What drives a claims model for heart failure admission?

  • Historical heart failure admissions
  • Historical non-heart failure admissions
  • General utilization profile
  • Presence of heart failure diagnosis
  • Presence of comorbidities (atrial fibrillation, mitral

valve disease, conduction disorders)

What drives a clinical model for heart failure admission?

  • Historical heart failure admissions
  • Historical non-heart failure admissions
  • General utilization profile
  • Time of first heart failure diagnosis
  • Most recent ejection fraction
  • Recent BNP value
  • Relative change in BNP over time
  • Patterns of treatment (i.e. beta blockers, diuretics

and nitrates)

  • Markers of kidney and pulmonary function

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Active Patients with CHF

  • Pts. w/ CHF-Related

IP Admissions Total CHF-Related IP Admissions

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An Optum Company

A predictive methodology will yield a population that is reasonably similar by age, utilization history, and comorbidity profile

A typical methodology – 12 point scale

  • Age
  • Utilization (IP, ED, ambulatory)
  • Current active meds
  • Chronic conditions

Methodology driven by likelihood of admission

  • Age
  • Utilization (IP, ED, ambulatory)
  • Current active meds
  • Chronic conditions
  • Laboratory values

Average age ED visits Hospitalizations Outpatient visits Charlson comorbidity index

74 3 2 11 2.0 71 3 2 10 1.8

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…The previously described historically-based methodology captures only 40% of the patients at highest risk for admission

Highest admission risk patients captured by the non-predictive methodology Captured Not captured

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Five key social determinants of health – not well documented in clinical data

Economic stability

  • Poverty
  • Employment
  • Food security
  • Housing stability

Education

  • High school graduation
  • Enrollment in higher education
  • Language and literacy
  • Early childhood education and development

Social and community context

  • Social cohesion
  • Civic participation
  • Discrimination
  • Incarceration

Health and health care

  • Access to health care
  • Access to primary care
  • Health literacy

Neighborhood and built environment

  • Access to healthy food
  • Quality of housing
  • Crime and violence
  • Environmental conditions

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IOM recommendations for collection of social data

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Patient reported measures add dimensions that can better address psychosocial factors and outcomes from the patient perspective

PHQ9

A depression screen with high positive and negative predictive values (88%)*

*Source: Kroenke K, Spitzer RL, Williams JB; The PHQ-9: validity of a brief depression severity measure. J Gen Intern

  • Med. 2001 Sep;16(9):606-13.

PROMIS10

Global health assessment focused on symptoms, functioning, and healthcare related quality of life

SF12 and 36

General assessments used to measure functional health and well being

General health inventories Oswestry disability index

A tool to quantify disability in low back pain

RAPID3

A combination global assessment and symptom severity assessment tool for rheumatoid arthritis

Seattle angina questionnaire

Health related quality of life assessment for people with coronary artery disease

Disease specific inventories

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  • In general, would you say your health is:
  • In general, would you say your quality of life is:
  • In general, how would you rate your physical health?
  • In general, how would you rate your mental health, including your mood and your ability to think?
  • In general, how would you rate your satisfaction with your social activities and relationships?
  • In general, please rate how you carry out your usual social activities and goals?
  • To what extend are you able to carry out your everyday physical activities such as walking, climbing

stairs, carrying groceries, or moving a chair?

  • In the past 7 days, how often have you been bothered by emotional problems such as feeling anxious,

depressed, or irritable?

  • In the past 7 days, how would you rate your fatigue on average?
  • In the past 7 days, how would you rate your pain on average?

The PROMIS10 offers a health outcome assessment that is highly descriptive

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Comorbid depression is Associated with Increased Health Care Use and Expenditures in Individuals with Diabetes

Diabetes Care. Leonard E. Egede, MD, Deyi Zheng, MB, PHD, and Kit Simpson, DRPH.

Depression and Cardiovascular Healthcare Costs among Women with Suspected Myocardial Ischemia: Prospective Results from the Women’s Ischemia Syndrome Evaluation (WISE)

Journal of the American College of Cardiology The relationship between modifiable health risks and health care expenditures: an analysis of the multi- employer HERO and cost database Journal of Occupational Medicine.

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  • Early detection of frailty enables proactive patient support, focused on individuals with

more complex co-morbidities and less access to family support

Using patient reported information to better detect frailty in the elderly

Symptoms and functional status assessments that capture (deterioration) in:

  • Alterations of consciousness
  • Dizziness & vertigo
  • Malaise or fatigue
  • Respiratory capacity
  • Digestion
  • Senility
  • Activities of daily living

Primary care provider registries to alert them about emerging risks in their high risk patients Care management registry (enhancing lists already driven by historical hospitalization utilization and comorbidity), +/- a specially targeted program

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Toward a demand and reserve construct for defining clinical risk

This construct can be made practical by focusing on the few things that most meaningfully drive outcomes and can be measured directly or inquired upon Biological factors

  • Co-morbidities
  • Flares or decompensation
  • Genetics
  • General physical resilience

Psychosocial factors

  • Depression and other mental illness
  • Motivation and self-efficacy
  • Intelligence and health literacy

Environmental factors

  • Absence of toxins or pollution
  • Minimal exposure to infectious

disease

  • Excessive financial costs

Most definitions of risk today rely heavily on surrogates:

  • Outcomes to be avoided (hospitalization risk,

excessive utilization or cost)

  • Excessive utilization or cost
  • Mortality
  • Gaps in care (surrogate for patient or environmental

factors that lead to non-adherence) Foundational risks affecting health outcomes

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A state of health is maintained when patient reserve exceeds the demands imposed by the health challenge

The demand imposed by these risks changes over time, as does an individual’s capacity to overcome the demands. Here I am borrowing from useful constructs that have been described in the developmental psychological and psychiatric literature.

Resilience: The ability to overcome and to avoid negative health consequences despite the risk

factors present in the environment.

Healthy equilibrium

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The Health Deficit visualization adds incremental insight to a more traditional risk profile

Traditional patient risk profile

67 year old female smoker COPD, chronic bronchitis, CAD, hypertension, atrial fibrillation, and pre-diabetes Medications:

  • Fluticasone/salmeterol
  • Lisinopril
  • Coumadin

COPD admission risk: 85% percentile Future cost score: 3.9 (4 times as costly as the average beneficiary) Gaps in care:

  • Blood pressure >140/90 30% of the last year
  • CAD without statin
  • INR time in therapeutic range of 55%
  • No evidence of spirometry testing

Ability for self care Ability to pay for care and related expenses Social and family support General state of physical health Motivation

Health Deficit profile

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Questions and discussion

alejandro.reti@optum.com