Using ACGs in Health Management Programs at Johns Hopkins. Linda Dunbar PhD RN Vice-President, Care Management Johns Hopkins HealthCare

Transcription

1 Using ACGs in Health Management Programs at Johns Hopkins Linda Dunbar PhD RN Vice-President, Care Management Johns Hopkins HealthCare

2 Johns Hopkins HealthCare A member of the Johns Hopkins family Owned jointly by Johns Hopkins University and Johns Hopkins Health System Johns Hopkins HealthCare develops and manages contractual relationships with the national and state governments, employers, hospitals, and physicians Our dedication to care management strategies using a population health approach provide the core of our business We conduct sophisticated analyses of population s health risk factors and provide targeted interventions that help prevent, manage and improve chronic conditions. 2

3 What makes our approach different Health risk screening marries claims and self-reported data Interventions are tailored based on algorithms which include the ACG predictive model (Dx and Rx) Reporting is robust and meaningful for employers, the Department of Defense and the State of Maryland Our world-class researchers craft current, evidence-based interventions and program At Johns Hopkins HealthCare, we work with the gold standard in health management and medical research. We provide a most comprehensive clinical data profile of our population and access to the best care to improve the population s health over time. 3

4 The US Department of Defense (DOD) and Johns Hopkins DOD is responsible for providing health care to eligible staff and their families for life through a government-funded program called TRICARE. Johns Hopkins HealthCare contracts with the DOD and receives a monthly capitation for 28,000 eligible DOD employees in the State of Maryland. The Johns Hopkins US Family Health Plan provides all health care and health management programs for the 28,000 enrollees. Johns Hopkins HealthCare contracts with hospitals, primary care clinics, and specialty providers and finances all health care for 28,000. 4

5 Johns Hopkins Community Physicians Primary care is provided in 17 clinics throughout the State of Maryland. 50% of specialty care, hospital care, and other services are provided within Johns Hopkins Health System, and 50% of care is provided by other private physician groups and hospitals. 5

6 Population Assessment Goals To compare and contrast allocation of resources across Johns Hopkins Community Physicians To summarize performance assessment for the entire USFHP population 6

7 Sicker Mean ACG Risk Score by Primary Care Site USFHP Mean Cranberry Station Green Spring Station Hager Park Laurel Monocacy Valley Montgomery Grove Odenton Meade Riverside Signature White Marsh Wyman Park 7 Annapolis Healthier

8 Mean Number of Chronic Conditions by Primary Care Site USFHP Mean Annapolis Cranberry Station Green Spring Station Hager Park Laurel Monocacy Valley Montgomery Grove Odenton Meade Riverside Signature White Marsh Wyman Park 8 Mean # Chronic Conditions

9 Prevalence per 1,000 Asthma Diabetes Depression Site Asthma Diabetes Depression USFHP population Annapolis Cranberry Station Green Spring Station Hager Park Laurel Monocacy Valley Montgomery Odenton Meade Riverside Signature White Marsh Wyman Park

10 Sicker Morbidity Ratio by Primary Care Site CY2006 CY2007 Cranberry Station Green Spring Station Hager Park Laurel Monocacy Valley Montgomery Grove Odenton Meade Riverside Signature White Marsh Wyman Park 10 Annapolis Morbidity Healthier

11 Poorer Efficiency Ratio by Primary Care Site CY2006 CY2007 Cranberry Station Green Spring Station Hager Park Laurel Monocacy Valley Montgomery Grove Odenton Meade Riverside Signature White Marsh Wyman Park 11 Annapolis Efficiency Greater

12 Primary Care Clinic Assessment Goals: To summarize morbidity for a specific primary care clinic and further for a group of patients with specific conditions or co-morbidity To develop a care management action plan for the population of patients in a particular clinic 12

13 Population Assessment: Demographics of Population/Clinic Demographic Characteristic Wyman Park Clinic (n=2,545) Total USFHP Population (n=29,124) Age in years, mean 58.2* 43.3 % female Months of enrollment during CY 2007, mean % of patients who were nonusers in 2007 % of patients who were new members in 2007 % of members accounting for 70% of total healthcare expenditures 11.3* * * *

14 Morbidity Profile, Summary Measures for Wyman Park Clinic Morbidity Measures Wyman Park Clinic Total USFHP Population Mean ACG risk score 1.29* 1.00 % with 1 or more chronic conditions, unadjusted 76.1* 54.0 Expected % with 1 or more chronic condition with age-sex adjustment 69.6 n/a No. chronic conditions, %* 9.9%* 11.1%* 55.1%* 46.0% 17.0% 10.7% 26.3% *Difference between sample clinic and total population is statistically significant. 14

15 Age-Sex Adjusted Comparison of Disease Distribution for Wyman Park Clinic (1.00 = prevalence ratio is not different from total population, adjusting for differences in the age-sex distribution) Condition Actual Prevalence per 1,000 Age-Sex Standardized Prevalence Ratio Asthma * Hypertension * Diabetes * Congestive Heart Failure Ischemic Heart Disease Depression Anxiety Disorders COPD Low Back Pain Headache * *Difference between sample clinic and total population is statistically significant. 15

16 Interpretation: Morbidity Profile for Wyman Park Clinic Morbidity is high Mean ACG risk score = 1.29 Over 75% of patients have 1+ chronic conditions 66% have multiple chronic conditions Hypertension and diabetes prevalence very high More resources needed for this practice Focus Quality Improvement efforts on hypertension and diabetes Care Management Program for diabetes and hypertension Remote monitoring devices (TeleWatch) Guided Care for older multi-morbid adults 16

17 Case Finding for Care Management Programs Cases for Care Management Programs are identified using ACG Predictive Modeling Application and Population Health Database 17

18 Risk Factors in the ACG Predictive Modeling Risk Score Age Gender Overall Disease Burden Frailty Risk Score Complicated Pregnancy Marker Selected Medical Conditions Hospital Dominant Conditions Pharmacy (Rx) Information (optional) 18

19 ACG Predictive Model Outputs ACG-PM produces two types of predictive risk indicators: Probability Score: represents the likelihood that a member will be among those persons using extraordinary health care resources Scores range from 0 to 1. Score of 0.4 means the individual has a 40 out of 100 chance of being in the high-risk cohort next year Predicted Resource Index: can be readily converted to a predicted dollar amount Scores range from 0 to roughly 40 with a population mean of 1.0. Johns Hopkins produces risk scores every month for every person and stores in the Population Health Database 19

20 Population Health Database Inputs Claims, Rx, Enrollment, Self-reported Health Risk Data Lab Results ACG Output: EDC, Risk Scores Scientific Knowledge: clinical guidelines Update Monthly Update Monthly Update Monthly Review Annually Claims/Operations Tables Lab Results Tables ACG Tables Scientific Tables Database Update monitored patient and clinical events tables Monitored Patient Tables Clinical Events Tables Create Reports Routinely and on Demand Outputs Patient Care Reports Provider Profile Population Statistics Population Groups Users Case Manager Medical Directors Clinical Management Team 20

21 Identify, Stratify, Intervene with Persons with Diabetes Our goal: High Complexity Level 1 Moderate Complexity Level 2 Low Complexity Level 3 1. Identify all persons with diabetes, and 2. Stratify them into three levels of complexity, and 3. Intervene appropriately. Each level of complexity has an appropriate level of care management intervention applied by our staff: (outreach, nursing/case management assistants, nurses, physicians and social workers) 21

22 Identify Persons with Diabetes: Algorithm for Level 3 Select patients who meet any of the following criteria: From ACG Diagnosis Tables, (EDC) for diabetes From ICD-9 codes on medical claims (including encounter data), search all available fields for diabetes: 250, 250.0, , , , 250.1, , , , , 250.2, , , , , , , , , 250.4, , , , , 250.5, , , , , 250.6, , , , , 250.7, , , , , 250.8, , , , , 250.9, , , , , v53.91, v65.46, v45.85, v58.67 From ACG Rx-MG tables: ENDx030 or ENDx040 From lab services tables: Select patients where CPT is ('83036','83037') From Health Risk Assessment (HRA) table: Select patients who self report Personal History of Diabetes Type I or II 22

23 Stratify: Algorithm for Level 2 From the Diabetes Level 3 DM Screening Criteria Algorithm Output Data Set: DKA (250.1x) OR Hyperosmolarity (250.2x) OR Coma (250.3x) OR Hemoglobin A1C: Most recent Hgb A1C >7 and <9% (3046F) OR Most recent probability_high_total_cost > 0.2 AND Renal manifestations (250.4x) OR Ophthalmic manifestations (250.5x) OR Neurological manifestations (250.6x) OR Peripheral circulatory disorders (250.7x) OR Other specified manifestations (250.8x) OR Unspecified complication (250.9x) OR LDL elevation OR Urine protein: Positive macroalbuminuria (3062F) OR CAD OR CHF OR Kidney Disease OR COPD. 23

24 Stratify: Algorithm for Level 1 From the Diabetes Level 2 Moderate Severity DM Screening Criteria Algorithm Output Data Set: DKA (250.1x) OR Hyperosmolarity (250.2x) OR Coma (250.3x) OR Hemoglobin A1C: Most recent Hgb A1C >9% (3046F) OR Most recent probability_high_total_cost > 0.6 AND Renal manifestations (250.4x) OR Ophthalmic manifestations (250.5x) OR Neurological manifestations (250.6x) OR Peripheral circulatory disorders (250.7x) OR Other specified manifestations (250.8x) OR Unspecified complication (250.9x) OR LDL elevation OR Urine protein: Positive macroalbuminuria (3062F) OR CAD OR CHF OR Kidney Disease OR COPD. 24

25 Stratification: The Simplified Version High Complexity n = 65 Diabetes Diagnosis HbA1C > 9 ACG-pm >.6 Co-morbidity (Cardiovascular, COPD, Depression) Moderate Complexity n = 352 Diabetes Diagnosis HbA1C >7 and < 9 ACG-pm >.2 and <.6 Low Complexity n = 3,332 Diabetes Diagnosis HbA1C <7 ACG pm <=0.2 25

26 Diabetes in the USFHP Population Total USFHP Diabetes Population = $32,982,208 or $10,356 annual expense (n = 3,739 or 13.8% of population) High Complexity n = 65 $6,120,825 or $94,166 annual expense per person Moderate Complexity n = 352 $9,805,816 or $28,848 annual expense per person Low Complexity n = 3,332 $17,055,567 or $5,124 annual expense per person 26

27 Quality and Utilization of Services: Highly Complex Group High Complexity n = 65 Moderate Complexity n = 352 Low Complexity n = 3,332 Inpatient Utilization Inpatient Admissions: 264 admissions Inpatient days: 2,137 Average length of stay per admissions: 8.0 Primary care visits: PCP visits 758 Specialty care visits : Specialty care visits 3,400 Poor Quality of Care HbA1C measured annually 86.6% HbA1C in good control 45.50% Annual eye exam 73.72% LDL-C level < % BP < 130/ % 27

28 Clinical Screening 28

29 Clinical Screener Role Registered Nurse with experience in Inpatient, outpatient clinic or primary care office, utilization management or telephone triage Clinical predictive modeling expert Data-based decision making Streamlined referral process Results in. Efficient and effective targeting of high-risk patients 29

30 Clinical Screening by RN Case Identification: Patients Identified as needing Care Management Services Level 1, 2, or 3 Assigned Electronically Referral Process for Physicians: Address Dedicated Phone Hotline Clinical Screening Software ACG Predictive Modeling Diagnoses Utilization Clinical Indicators Lab and radiology Clinical Assessment: Telephone contact Amenability Assessment Clinical Screener (RN) Level of Intervention Assigned Level 1, 2, or 3 Case Manager Assigned if Level 1 30

31 Clinical Screening Software: General Information 31

32 Clinical Screening Software: Utilization Information 32

33 View Detailed Information: Pharmacy 33

34 View Detailed Information: Emergency Room Visits 34

35 Clinical Screening Software: Screening Status 35

36 Interventions for People in need of Care Management Services 36

37 Summary: Our services for Level 1, 2, and 3 Level 1 High risk with multiple chronic illness Intensive Case Management: Guided Care RN or Social Work Case Manager Individualized Assessment Care Plan Self-Management Plan Level 2 Moderate risk patients with single chronic illness or risk factors Level 3 Low risk Disease Management: Health Coaching and Lifestyle Management Remote monitoring with TeleWatch Programs to modify diet, increase exercise, smoking cessation, weight loss Health Education and Promotion Healthwise information online and in print, handbooks and mailing Direct messaging via mail and web Healthy lifestyle program promotions 37

38 Level 3: Health Education and Promotion In Print On Web and in Personal Health Record To browse content, go to and search A-Z Health Topics 38

39 Level 2: Disease Management and Lifestyle Management Health coaches available via telephone Review of health risks Offer health coaching services Goal setting Incorporating individuals desired goals within context of identified health risks Contracting for meeting goals with identified indicators of progress Personal Action Plan Setting measurable outcomes with timelines Resource identification Monitoring and follow-up 39

40 Level 2: TeleWatch telemonitoring by RNs TeleWatch Remote telemonitoring System created by: JHU- School of Medicine, Division of Cardiology JHU- Applied Physics Laboratory Goals of the telemedicine system: Easy to use Inexpensive Employ widely available technology 40

41 Level 2: TeleWatch Monitoring Automated, telephone-based telemonitoring system Patients self-report physiologic parameters (using durable medical equipment) and answer pre- recorded, individualized, disease-specific questions Algorithms designed to detect worrisome trends or responses which automatically alert the nurse Validation algorithms ensure data integrity 41

42 Level 2: TeleWatch Monitoring POTS line 1. Patient selfcollects data 2. Data selftransmitted 3. TeleWatch server collects data 4. Automated Alert generation 5. Patient feedback Laptop computer Patient Healthcare Provider 42

43 Level 2: TeleWatch Monitoring Hospital Admission 43

44 Guided Care: Intensive Case Management for Level 1 44

45 Level 1: Intensive Case Management/Guided Care Guided Care is a vehicle that combines successful innovations and provides them through the primary care system Innovations have already demonstrated effectiveness alone: Transitional Care Chronic Disease Self-Management Geriatric Evaluation by an interdisciplinary team Caregiver Support and Training Case Management Principle Investigator: Chad Boult MD MPH Funded by NIA, Hartford Foundation, Langeloth Foundation 45

46 Level 1: The Guided Care Model Guided Care Improve Quality Of Life Improve Quality Of Care Improve Self-care Outcomes Patients: Function Health Quality of care Mortality Caregivers: Strain Affect Satisfaction Physicians: PCP satisfaction GCN satisfaction Payors: Volume srvcs Cost of services Quality of Care 46

47 Level 1: Guided Care Nurse-Physician Team Assesses needs and preferences Creates an evidence-based care guide and a patient-friendly action plan Monitors the patient proactively Supports chronic disease self-management Smoothes transitions between care sites Communicates with providers in EDs, hospitals, specialty clinics, rehab facilities, home care agencies, hospice programs, and social service agencies in the community Educates and supports caregivers Facilitates access to community services 47

48 Guided Care Randomized Clinical Trial High-risk older patients (n=904) of 49 community-based primary care physicians practicing in 14 teams Physician/patient teams randomly assigned to receive Guided Care or usual care Outcomes measured at 8, 20 and 32 months Randomized Trial ends June 30,

49 Guided Care: Effects on Physician Satisfaction Communicating with patients Guided Care (n=18) Usual Care (n=20) Communicating with caregivers Educating caregivers Motivating patients Know all pt s meds P (20-month results) 49

50 Guided Care: Effects on Quality of Care PACIC scales: GC UC aor* 95% CI P Goal setting <0.001 Coordination Decision support Problem solving Patient activation Aggregate Adjusted for baseline socio-demographics, health, function, PACIC scores, site PACIC = Patient Assessment of Chronic Illness Care instrument 50

51 Effects on Caregiver Strain 51

52 Effects on Costs of Care (per caseload, 55 patients) GC UC Difference Average Expenditure Cost Difference in 000s $ Hospital days $1,519/day SNF days $305/day Home health episodes $1331/episode Physician visits 40.0 $41/visit 1.7 Gross savings Cost of GCN 95.9 NET SAVINGS

53 Publications Boyd C et al. Gerontologist Nov 2007 Sylvia M et al. Dis Manag Feb 2008 Boyd C et al. J Gen Intern Med Feb 2008 Boult C et al. J Gerontology Mar 2008 Leff B et al. Am J Managed Care 2009 (in press) Wolff et al. J Gerontology 2009 (in press) Guided Care: a New Nurse-Physician Partnership for Chronic Care. Springer Publishing Co ( 53

54 In Summary. Our goal: Improve Population Health Population Morbidity Assessment Identify opportunities to intervene Identify persons in need of intervention Identify through algorithms with multiples data sources Stratify to appropriate level of morbidity and complexity Intervene with appropriate level of practitioner Outreach staff for prevention/case management assistants Health Coaches for disease and lifestyle management RNs and Social Workers for intensive case management Measure outcomes 54

55 The necessary tools. Population Health database Health Risk and other self-reported data Healthcare claims Pharmacy data Laboratory data ACGs Predictive modeling and risk adjustment, both diagnostic and pharmacy Electronic Health Record Decision Support functionality such as Guided Care 55

56 Contact Information Linda Dunbar PhD RN Vice President, Care Management Johns Hopkins HealthCare 6701 Curtis Court Glen Burnie, Maryland Telephone: