Post on 17-Dec-2015
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
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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
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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
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?
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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
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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