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

Data-Driven Patient Safety Improvement Report Aggregation Data Analysis Intervention Design Intervention Implementation Adverse Event Reporting

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

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

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

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

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

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

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

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

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

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) Standardization of reporting fields and linkage of data from multiple systems (reporting systems + quality datasets) Stronger partnerships for data analysis Stronger partnerships for data analysis Merging of institutional risk management and patient safety units Merging of institutional risk management and patient safety units Coordinated leadership from insurers, institutional management, and clinical staff Coordinated leadership from insurers, institutional management, and clinical staff Better financial incentives for patient safety improvement (individual- and institution- level) Better financial incentives for patient safety improvement (individual- and institution- level)