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Generating Information for Medicines Benefit Management: A Systems Framework Dennis Ross-Degnan, ScD Harvard Medical School and Harvard Pilgrim Health.

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Presentation on theme: "Generating Information for Medicines Benefit Management: A Systems Framework Dennis Ross-Degnan, ScD Harvard Medical School and Harvard Pilgrim Health."— Presentation transcript:

1 Generating Information for Medicines Benefit Management: A Systems Framework Dennis Ross-Degnan, ScD Harvard Medical School and Harvard Pilgrim Health Care Institute Universal Health Coverage: Considerations in Designing Medicines Benefits Policies and Programs Cape Town, South Africa, 29-30 September 2014

2 Overview  Pharmaceutical system and data sources  Assessing policy performance Routine data Ad hoc data

3 International manufacturers Drug importers Domestic manufacturers SUPPLY Manufacture & import Other key stakeholders: Drug regulatory agency Manufacturers associations ` Wholesalers and distributors Private and NGO facilities Private physicians/ other providers Pharmacies and retail outlets Private sector supply Other key stakeholders: Wholesale & pharmacy orgs Professional associations Health delivery systems Government procurement systems Government health facilities Public sector supply Consumers and patients Insurance and risk carriers Consumer demand DEMAND Other key stakeholders: Consumer & patient orgs Third party payers Employers Benefit design Enrollment Utilization (volume and value) Attitudes and opinions Supply chain performance Sales volume & value Price and mark-ups Attitudes and opinions Procurement Supply chain performance Utilization volume & value Price and mark-ups Treatment patterns Attitudes and opinions Patterns of illness Care seeking and utilization OOP payment and affordability Medicine availability Attitudes and opinions Manufacture Importation Distribution Pharmaceutical System Data to Inform Policy Decisions

4 Domains for Assessing Medicines Policy Performance  Availability Productive local and research-based industry Efficient delivery systems  Cost and affordability Health system – financial sustainability Patients – risk protection  Equitable access Vulnerable populations (SES, gender, disease)  Appropriate use Guideline-based choice Underuse, overuse Adherence to treatment  Improved outcomes Clinical measures Use of expensive services QALYs/DALYs Mortality  Satisfaction Providers and patients 4

5 Types of Routine Data Available for Measuring Performance  Member/patient data Age, gender, employment status, insurance  Utilization and clinical data Hospital inpatient Outpatient Medication dispensing Preventive services  Cost data Hospital, physician services, procedures, lab tests Medicines 5

6  Administrative data Derived from payment system with defined patients, providers, services, and payments  Clinical data Generated during process of care Increasingly from electronic medical records Richer clinical detail More difficult to collect and standardize Administrative vs. Clinical Data?

7 Uses of Performance Indicators  Routine monitoring Measures crucial for program management Regular collection, summary, reporting, feedback Targeting poor performers  Performance-based contracting Achieving objective standards linked to incentives  Policy evaluation Before and after an intervention or policy change Measure both anticipated and unintended effects 7

8 Investigating Drug Use in Health Facilities  Developed by INRUD and WHO in early 1990s  To measure specific drug use indicators reliably in any health facility Defined indicators Sampling facilities Sampling medical and pharmacy records Convenience samples of current patients 8

9 Routine Pharmaceutical Monitoring  Performance measures Cost Utilization Quality of care Adherence  Levels of aggregation Overall, by region, medical practice, prescriber By patient type  Frequency Monthly for factors needing frequent decisions (cost, high cost medicines, fraud) Quarterly or annually for higher level tracking 9

10 Examples of Monitoring and Profiling Indicators  Cost Avg. cost per member per month (PMPM) Avg. net cost per dispensing per month  Utilization Avg. no. of dispensings PMPM Total no. of dispensings per therapeutic class  Quality of care % of patients with ARI receiving antibiotics % of patients discharged from hospital with acute myocardial infarction receiving beta blockers  Fraud, abuse No. of prescriptions of opioids per provider No. of dispensings per member 10

11 Using Routine Health System Data to Inform Policy Decisions  Benefits Data exist – time and money savings Reflect real-world practice Potentially covering large populations  Challenges Many settings, providers, treatments Shifting populations Data ownership & confidentiality Missing populations & services Data quality, completeness Data integration across settings

12 Selected Issues in Data Quality, Completeness, and Integration  Availability Missing data Capitation, bundled payment, and data loss  Consistency Inconsistent member identifiers Inconsistent drug, diagnosis, procedure coding Inconsistent units for different dosage forms (especially injections, liquids, inhalers)  Reliability Incorrectly entered data, upcoding Denied or duplicate claims Inconsistent time windows

13 Ad Hoc Data for Assessing Pharmaceutical Sector Performance  Exit/post-visit surveys Quality of care Understanding Satisfaction  Observation System efficiency Process of care Quality improvement opportunities  Population surveys Access to treatment Medicines in home Attitudes and opinions Economic situation  Focus groups MDs, patients with specific illnesses Attitudes towards system changes 13

14 Examples of Indicators from WHO Jordan National Household Medicines Survey WHO Jordan National Household Medicines Survey, 201014

15 Summary Points  Policies have many objectives  Evidence is crucial to inform decisions  Many sources of medicines data  Routine data can be used to assess performance in relation to objectives  Ad hoc data are needed for key population-based measures 15

16 Extra Slides

17 Importance of Data Quality in the Policy Information Process Data Performance measures Policy evaluations Routine monitoring Data analysis and results Policy change Data quality problems can lead to poor measures, incorrect analyses, and bad policies

18 Harvard Pilgrim Health Care Pharmacy Monitoring System  Pharmacy Trend Monitoring Report Summary pharmacy trends, year on year Top drug classes, year to date (YTD) and change from last year Utilization trend graphs, last 4 years Detailed summary graphs, last 12 months Trends for key individual drugs 18

19  Centralized Data owners send data to central location Broad scope, large populations Limited depth of clinical information Patients often deidentified to ensure privacy No link to source clinical data  Distributed Data owners maintain physical control of data Known populations Meet security and privacy obligations Transfer only what is needed and when necessary Can link to richer clinical data Centralized vs. Distributed Data?

20 “Big Data” and Health Analytics 20

21 Informatics: Timely and Actionable Information to Guide Organizational Decisions Value Capture  Lower trend  Demonstrate value  Targeted action  Timely decisions  High impact interventions  Transform care delivery  On-demand information  Proof of value Value Creation Provider Network Management Clinical Analytics Financial, Actuarial & Operational HPHC Informatic s Employer & Member 21Source: Tariq Abu-Jaber, HPHC VP Informatics

22 Turkey’s National Health Information System (Saĝlik-Net) 22

23 Local Databases Standardized Study Datasets Coordinating Center Local Databases Standardized Study Datasets Local Databases Standardized Study Datasets Local Databases Standardized Study Datasets Structure of Distributed Research Network with Common Data Model  No central data warehouse  Sites create standard datasets  Process management and quality checking by Coordinating Center in concert with local data managers and analysts  Distribute programs, return results or limited datasets Institutional Firewalls

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