# Presentation: 723929

## Kaiser Permanente:The Intersection of Data and Quality

**Scott Young, M.D.**
**June 22, 2007**

## Discussion Agenda

- Background
- Drivers for data aggregation and measurement
- KP data sources
- Types of data collected
- Essential data for clinical performance
- Future directions

## America’s Largest Non-Profit Health Care Program

- Integrated health care  delivery system
- 8.6 million members
- 13,000 physicians
- 151,000 employees
- 8 regions in 9 states and D.C.
- 37 hospitals and medical centers
- 431	medical offices
- 35+	billion annual revenues

## Data Strategy – Key Drivers

- Growing chronically ill population
- Demographic shifts with Baby-Boomers and elderly
- Advancing medical science / technology – more and more evidenced-based practices to implement
- Increasing need for performance information
- Transitions between care settings

## Chronic Illness: A Key Driver of Medical Care Costs

- Source:  Kaiser Permanente Northern California commercial membership, DxCG methodology, 2001.

## 10 Clinical Priority Areas

## How do we measure quality?

- KP designs and maintains a robust analytic capacity
- Utilize common set of metrics (HEDIS, JCAHO, etc)
- Mix of KP data generated from:
- Internal Sources
- Members (KP.org)
- External Entities
- Key challenge is standardization and prioritization

## Examples of Key Metrics

- Common Measures
- Clinical Effectiveness
- Safety
- HEDIS Index
- JCAHO
- Safety & Risk Management
- Service
- Resource Utilization
- Core Value Metrics
- Developed to provide actionable metrics for operational and functional leaders
- Examples include care handoffs, clinical outcomes, screening and preventative services

## Internal Data Sources

- KP HealthConnect (KPHC)
- Integrated EHR across all eight KP regions
- Powerful tool for data collection and aggregation
- Legacy data systems
- Conversion essential and ongoing
- Mix of clinical and business / financial data
- Population Care Information System / Registries
- Panel Management
- Essential component to manage chronically ill populations

## HealthConnect Application 

- Degree of comprehensive data and integration

**Charting/Documentation**

- Improve effectiveness throughaccess to patient history

**Automated Reminders**(Preventative & Monitoring Tasks)

- Improve compliance with practice guidelines

**Internet Access**Email Communications, Online Health Information, etc.

- Improved patient access and convenience

**Medication Order Entry**

- Reduce prescribing errors

**Remote Access**

- Increased physician
- timeliness & efficiency

**Clinical Decision Support**

- Improved diagnosis & disease management

**Integrated EMR**
- Inpatient, Outpatient, Lab, Pharmacy.

- Quality measurement,
- improvement & research

## Expanded online Access for Members

## Risk of Acute Myocardial Infarction and Sudden Cardiac Death with Use of COX-2 Selective and Non-Selective NSAIDs: Nested Case Control StudyLancet 2005; 365: 475–81

**Risk of Acute Myocardial Infarction and Sudden Cardiac Death with Use of COX-2 Selective and Non-Selective NSAIDs: ****Nested Case Control Study*****Lancet *****2005; 365: 475–81**

- DJ Graham,1 DH Campen,2 R Hui,2 M Spence,2
- C Cheetham,2 S Shoor,2 G Levy,2 and WA Ray3
- 1Office of Drug Safety, US Food and Drug Administration
- 2Kaiser Permanente, California
- 3Vanderbilt University School of Medicine

## How did our COX-2 study come about?

- Started with efficacy, safety, and cost concerns with entire class of COX-2 drugs
- Tight P&T process led by, and trusted by, clinicians led to overall use of COX-2 drugs at 4% of NSAID market share within KP
- Highly dependent on multiple and disparate data sources

## NSAIDs vs. COX-2 Rx Market Share

**Source:  MedImpact Health Care System, Inc**

## How did our COX-2 study come about?

- Existence of our own pharmacy outcomes research group (PORG) and h/o epi studies
- Longitudinal data on millions of members

## How did it happen?

- In 2001, one of our physicians contacted an officer at the FDA, Dr. David Graham
- Out of the PORG operating budget (within Pharmacy ops) and with a small grant from the FDA for a phone survey, the authors designed and executed a case control study resulting in the Lancet article

## Did it have an impact?

- Stimulated many other analyses of Rofecoxib and other COX-2 agents
- Illustrated gap between marketing promises and real world outcomes
- Contributed to FDA scrutiny and to the company decision to withdraw the drug from the marketplace

## Data Aggregation – Multiple Sources

- Issues and Concerns:
- Critical need to standardize
- HL7/SNOMED-CT including CDA/CCD for data transmission between systems and aggregation of data
- Expansion of currently collected data
- Include health risk and health status data
- Incorporation of data from chart notes
- Currently difficult to achieve
- Resolve issues of attribution
- Provider, care team or system

## Example:Data Aggregation – Clinical Performance

- Aggressive identification of members with DM beginning in mid 90’s
- Case identification of adults with DM between 1997 – 2004
- Increased from 4.4% to 10.5%
- KP members with DM on ACE-I or ARB
- Accelerated from 48.4 to 57.6 % (2000-2004)

## Clinical Performance (cont.)

- LDL-C control in KP members with DM
- Increased from 25 to 61.1% from 1998-2004)
- Lipid lowering drug use among DM
- Increased from 38.6 to 52.1% (2002-2004)

## Improved Care CVD:Aspirin-Lovastatin-Lisinopril

- Noted region by region improvement in care of patients with DM and at risk for CAD
- Need to identify critical treatment components to predictably reduce cardiac events
- Identified a combination of aspirin, ACE inhibitor and lipid lowering agent
- Tested within a KP region and model validated by Archimedes
- Noted significant reduction in CVD and subsequent cost
- Currently being rolled out in all eight regions

## Data Aggregation:Issues for consideration 

- Applicable across virtually all medical conditions
- Particularly useful for chronic or complex conditions
- Critical barrier regarding source and quality of data
- Information is widely dispersed, in paper format, proprietary or in non-clinical (billing) systems
- Aligned systems strive to mitigate this issue
- Ability to organize data into care episodes remains difficult. Many technologies exist but remain proprietary, making comparability difficult.

## Episodes of Care – Methodology and Mechanisms

- Key issue – what outcomes and processes are not supported by episode methodology.
- Appropriate if population denominator missing.
- Many times utilizing person or population is more appropriate
- Episode approach fails to assess the appropriateness or volume of episodes.

## Data Feedback Mechanisms

- Key attribute of KP quality and system improvement
- Currently virtually all information is retrospective in nature
- Data sometimes too old to be or feel actionable
- Data must be actionable at the appropriate level, be that national, regional, medical center or provider
- The performance improvement domain requires the most current (and accurate) data

## The Future

- Widespread adoption of EHRs (KP HealthConnect) will enhance the availability of accurate and actionable data.
- Data collection and aggregation will increasingly become a byproduct of routine care.
- Integrated systems (virtual or real) will have the ability to “learn” from population outcomes.
- Data driven feedback will become more and more real time.

## Conclusions

- Currently needed data is not completely available
- Integrated delivery systems are currently aligning and utilizing data from multiple sources
- Standardization is critical for sharing between systems and providers
- EHRs remain the key tool for data collection and provision of evidence based care

## Thank You