Generating Information for Medicines Benefit Management: A Systems Framework

Dennis Ross-Degnan, ScDHarvard 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

Overview

Pharmaceutical system and data sources

Assessing policy performance Routine data Ad hoc data

International manufacturer

s

Drug importers

Domestic manufacturers

SUPPLYManufacture & importOther key stakeholders:• Drug regulatory

agency• Manufacturers

associations

`

Wholesalers and distributors

Private and NGO facilities

Private physicians/

other providers

Pharmacies and retail

outlets

Private sector supplyOther 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

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

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

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

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

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

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

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

14

Examples of Indicators from WHO Jordan National Household Medicines Survey

4- week spending/person (JOD)

A. Key Indicators - All households All < 50 50 to 69

70 to

100

101 to

150

> 150

A.1. Geographic access to medicines

% respondents who agree that they would use public health care facilities more if opening hours were convenient 75% 83% 77% 81% 78% 63%

A.2. Availability of medicines

% respondents who agree that medicines are usually available at their public health care facility 32% 35% 26% 35% 32% 31%

A.3. Affordability of medicines

% households whose monthly medicines expenditures represent > 40% of discretionary spending 7% 13% 9% 4% 3% 5%

% respondents who agree that they can get free medicines at their public health care facility 32% 32% 28% 31% 37% 32%

% respondents who agree that they can usually afford to buy the medicines they need 77% 59% 68% 72% 91% 95%

A.4. Access to medicines - Mixed indicators

% respondents who agree that the quality of services delivered in public health care facilities is good 59% 57% 58% 59% 66% 55%

WHO Jordan National Household Medicines Survey, 2010

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

Extra Slides

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

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

21

Informatics: Timely and Actionable Information to Guide Organizational Decisions

Value Capture

Lower trend Demonstrat

e value Targeted

action Timely

decisions

High impact interventions

Transform care delivery

On-demand information

Proof of value

Value Creation

ProviderNetwork

Management

ClinicalAnalytics

Financial ,Actuarial

& Operation

al

HPHC Informati

cs

Employer & Member

Source: Tariq Abu-Jaber, HPHC VP Informatics

22

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

http://www.sagliknet.saglik.gov.tr/giris.htm

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