Remote Auditing of Reporting Facilities by the Central Registry: Challenging but Rewarding.

OBJECTIVE: Discuss the experience of the New Jersey State Cancer Registry's (NJSCR's) transition to remote auditing of reporting facilities. METHODS: We conducted remote audits from 2016-2019 for reporting years 2014-2017. Facilities were selected for audit if they (1) were <90% complete for the year; (2) had ≥10 electronic pathology records (HL7) without a corresponding hospital abstract; or (3) had not been audited in the past 5 years. HL7 records and disease index data were used to determine which cases were potentially unreported. Disease index data were linked to data from the NJSCR Surveillance, Epidemiology, and End Results Data Management System (SEER*DMS) via Match*Pro software. We describe the number of facilities audited and the number of unreported cases identified as a result of the audit process by reporting year and audit type. We also calculate the percent increase in cases reported by reporting year and describe salient challenges in the process. RESULTS: During 4 years of data collection for the reporting years 2014-2017, 101 audits were completed and 10,546 cases were identified as unreported, representing a 7.1% increase in the number of reportable cases among those facilities audited. Challenges for the central registry involved organizing and reviewing large volumes of electronic data and Excel worksheets, and communications with facilities in the process of changing affiliations, personnel, or encryption policies. CONCLUSIONS: The new process has improved the audit experience for central registry staff and increased the capture of cases being reported to NJSCR. Facilities also made improvements to casefinding, reporting, and communications to the NJSCR.

Risk models to improve safety of dispensing high-alert medications in community pharmacies.

OBJECTIVES: To determine whether sociotechnical probabilistic risk assessment can create accurate approximations of detailed risk models that describe error pathways, estimate the incidence of preventable adverse drug events (PADEs) with high-alert medications, rank the effectiveness of interventions, and provide a more informative picture of risk in the community pharmacy setting than is available currently. DESIGN: Developmental study. SETTING: 22 community pharmacies representing three U.S. regions. PARTICIPANTS: Model-building group: six pharmacists and three technicians. Model validation group: 11 pharmacists; staff at two pharmacies observed. INTERVENTION: A model-building team built 10 event trees that estimated the incidence of PADEs for four high-alert medications: warfarin, fentanyl transdermal systems, oral methotrexate, and insulin analogs. MAIN OUTCOME MEASURES: Validation of event tree structure and incidence of defined PADEs with targeted medications. RESULTS: PADEs with the highest incidence included dispensing the wrong dose/strength of warfarin as a result of data entry error (1.83/1,000 prescriptions), dispensing warfarin to the wrong patient (1.22/1,000 prescriptions), and dispensing an inappropriate fentanyl system dose due to a prescribing error (7.30/10,000 prescriptions). PADEs with the lowest incidence included dispensing the wrong drug when filling a warfarin prescription (9.43/1 billion prescriptions). The largest quantifiable reductions in risk were provided by increasing patient counseling (27-68% reduction), conducting a second data entry verification process during product verification (50-87% reduction), computer alerts that can't be bypassed easily (up to 100% reduction), opening the bag at the point of sale (56% reduction), and use of barcoding technology (almost a 100,000% increase in risk if technology not used). Combining two or more interventions resulted in further overall reduction in risk. CONCLUSION: The risk models define thousands of ways process failures and behavioral elements combine to lead to PADEs. This level of detail is unavailable from any other source.