Marshaling Data to Improve Patient Safety

1. Marshaling Data to Improve Patient Safety Michelle Mello, JD, PhD Harvard School of Public Health

2. Data-Driven Patient Safety Improvement

3. Major Private SectorData Collection Efforts • University HealthSystem Consortium’s Patient Safety Net • 14 academic medical centers active, +5 by year end • ~ 250 reports/site/month across a broad range of incidents (total n≈22,000) • Online reports submitted by clinical staff, risk managers • DoctorQuality, Inc.’s Risk Prevention & Management System • Several dozen participating institutions • ~ 70,000 reports to date • Online reports submitted by clinical staff, risk managers

4. Private Sector Data Collection, continued • Harvard’s Malpractice Insurers Medical Error Prevention and Surveillance Study • Funded by AHRQ (David Studdert, Principal Investigator) • 6 multi-hospital insurers nationwide, including CRICO • “Reports” are closed malpractice claims (n≈2,040) in 4 clinical areas • Record reviews conducted by specialist physicians

5. 1. Adverse Event Reporting • Reporters: • Risk managers (difficult) • Nurses (good – 60% in UHC) • Pharmacists (good – 29% in UHC) • Physicians (very difficult – 2% in UHC) • What to collect? • Medical injuries • Near-misses and unsafe conditions • Other “adverse events” – falls, fires, suicides, etc. • Contributing factors

6. Barriers to Reporting • Legal: • Tort fears – confidentiality of report data • HIPAA • Practical: • Cultural norms • Time / hassle factor • Reporting overload: JCAHO, FDA, Department of Health, Board of Medicine, risk management, insurer, peer review committee, UHC or DoctorQuality

7. 2. Report Aggregation • Reporting systems vary in: • Vocabulary and definition • Typologies of adverse events and contributing factors • Range of data collected • Private-sector systems collect comprehensive data, but have limited membership • State systems have • Theoretically universal reporting, but substantial underreporting • Limited range of data fields

8. 3. Data Analysis • Most multi-institutional systems have limited capacity to conduct data analysis • States: lack of human resources, money • UHC: “like that UPS commercial” • Partnerships with researchers emerging, but still limited • OK to share data with researchers? • Who will pay?

9. Data Analysis,continued • Moving beyond descriptive analysis is difficult • Heterogeneity of adverse outcomes, errors, clinical conditions, institutions, and patients • Small sample sizes • Case/control designs are expensive, difficult to power, and pose HIPAA issues

10. 4. Intervention Design • Reporting institutions must receive feedback to maintain a stake in reporting • Comparative data and benchmarking are of interest • Types of interventions: (1) educational, (2) systems change • Clinical leadership / buy-in are essential • Should include an evaluation component • Key issue: How tailored should the intervention be to particular institutions?

11. 5. Intervention Implementation • Barriers: • Identifying clinical leaders • Gaining buy-in from busy clinicians who lack a strong stake in QI • Demonstrating the value of claims & report data • Crowding-out from other QI initiatives • Outside of captives, no organizational structure to implement interventions through the insurer, or otherwise coordinate institutions/practice groups

12. Next Steps in Building an Infrastructure for Data-Driven Patient Safety Improvement • Standardization of reporting fields and linkage of data from multiple systems (reporting systems + quality datasets) • Stronger partnerships for data analysis • Merging of institutional risk management and patient safety units • Coordinated leadership from insurers, institutional management, and clinical staff • Better financial incentives for patient safety improvement (individual- and institution- level)