Marshaling Data to Improve Patient Safety

Michelle Mello, JD, PhD

Harvard School of Public Health

Data-Driven Patient Safety Improvement

ReportAggregation

ReportAggregation

InterventionDesign

InterventionDesign

Adverse Event Reporting

Adverse Event Reporting

Data Analysis

Data Analysis

InterventionImplementation

InterventionImplementation

Major Private SectorData Collection Efforts

University HealthSystem Consortium’s Patient Safety Net 14 academic medical centers active, +5 by year end14 academic medical centers active, +5 by year end ~~ 250 reports/site/month across a broad range of incidents 250 reports/site/month across a broad range of incidents

(total n(total n≈22,000)≈22,000) Online reports submitted by clinical staff, risk managersOnline reports submitted by clinical staff, risk managers

DoctorQuality, Inc.’s Risk Prevention & Management System Several dozen participating institutionsSeveral dozen participating institutions ~ 70,000 reports to date~ 70,000 reports to date Online reports submitted by clinical staff, risk managersOnline 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 Funded by AHRQ (David Studdert, Principal

Investigator)Investigator) 6 multi-hospital insurers nationwide, including CRICO6 multi-hospital insurers nationwide, including CRICO ““Reports” are closed malpractice claims (nReports” are closed malpractice claims (n≈2,040) in ≈2,040) in

4 clinical areas 4 clinical areas Record reviews conducted by specialist physiciansRecord 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 injuriesMedical injuriesNear-misses and unsafe conditionsNear-misses and unsafe conditionsOther “adverse events” – falls, fires, suicides, etc.Other “adverse events” – falls, fires, suicides, etc.Contributing factorsContributing factors

Barriers to Reporting

Legal:Legal:Tort fears – confidentiality of report dataTort fears – confidentiality of report dataHIPAAHIPAA

Practical:Practical:Cultural normsCultural normsTime / hassle factorTime / hassle factorReporting overload: Reporting overload: JCAHO, FDA, Department of JCAHO, FDA, Department of

Health, Board of Medicine, risk management, insurer, Health, Board of Medicine, risk management, insurer, peer review committee, UHC or DoctorQualitypeer review committee, UHC or DoctorQuality

2. Report Aggregation

Reporting systems vary in:Reporting systems vary in: Vocabulary and definitionVocabulary and definition Typologies of adverse events and contributing factorsTypologies of adverse events and contributing factors Range of data collectedRange of data collected

Private-sector systems collect comprehensive Private-sector systems collect comprehensive data, but have limited membershipdata, but have limited membership

State systems have State systems have Theoretically universal reporting, but substantial Theoretically universal reporting, but substantial

underreportingunderreporting Limited range of data fieldsLimited range of data fields

3. Data Analysis

Most multi-institutional systems have Most multi-institutional systems have limited capacity to conduct data analysislimited capacity to conduct data analysisStates: lack of human resources, moneyStates: lack of human resources, moneyUHC: “like that UPS commercial”UHC: “like that UPS commercial”

Partnerships with researchers emerging, Partnerships with researchers emerging, but still limitedbut still limitedOK 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 Moving beyond descriptive analysis is difficultdifficultHeterogeneity of adverse outcomes, errors, Heterogeneity of adverse outcomes, errors,

clinical conditions, institutions, and patientsclinical conditions, institutions, and patientsSmall sample sizesSmall sample sizesCase/control designs are expensive, difficult Case/control designs are expensive, difficult

to power, and pose HIPAA issues to power, and pose HIPAA issues

4. Intervention Design

Reporting institutions must receive feedback to Reporting institutions must receive feedback to maintain a stake in reportingmaintain a stake in reporting Comparative data and benchmarking are of interestComparative data and benchmarking are of interest

Types of interventions: (1) educational, (2) Types of interventions: (1) educational, (2) systems changesystems change

Clinical leadership / buy-in are essentialClinical leadership / buy-in are essential Should include an evaluation componentShould include an evaluation component Key issue: How tailored should the intervention Key issue: How tailored should the intervention

be to particular institutions?be to particular institutions?

5. Intervention Implementation

Barriers:Barriers: Identifying clinical leadersIdentifying clinical leadersGaining buy-in from busy clinicians who lack a Gaining buy-in from busy clinicians who lack a

strong stake in QIstrong stake in QIDemonstrating the value of claims & report data Demonstrating the value of claims & report data Crowding-out from other QI initiativesCrowding-out from other QI initiativesOutside of captives, no organizational structure Outside of captives, no organizational structure

to implement interventions through the insurer, to implement interventions through the insurer, or otherwise coordinate institutions/practice or otherwise coordinate institutions/practice groupsgroups

Next Steps in Building an Infrastructure for Data-Driven Patient Safety Improvement

Standardization of reporting fields and linkage of Standardization of reporting fields and linkage of data from multiple systems (reporting systems + data from multiple systems (reporting systems + quality datasets)quality datasets)

Stronger partnerships for data analysisStronger partnerships for data analysis Merging of institutional risk management and Merging of institutional risk management and

patient safety unitspatient safety units Coordinated leadership from insurers, Coordinated leadership from insurers,

institutional management, and clinical staffinstitutional management, and clinical staff Better financial incentives for patient safety Better financial incentives for patient safety

improvement (individual- and institution- level)improvement (individual- and institution- level)