Impact of incorporating pharmacy claims data into electronic medication reconciliation.

PURPOSE: The potential value of adding pharmacy claims data to the medication history in the electronic health record (EHR) to improve the accuracy of medication reconciliation was studied. METHODS: Three medication history sources were used for this evaluation: a gold-standard preadmission medication list (PAML) created by reviewing all available medication history information, an EHR-generated PAML, and pharmacy claims data. The study population consisted of patients from the Partners Medication Reconciliation Study with medication history information available from all three medication history sources. The aggregated medication list from each medication history source was compared with the gold-standard PAML to identify and categorize missing medications, additional medications, and discrepancies in the various attributes of a medication order, including dose, route, and frequency. McNemar's test was used to compare paired proportions of medication entries across each source to the gold-standard PAMLs. RESULTS: Fifteen patients had medication histories in all three medication history sources. Medication entries across all three sources included 169 from the gold- standard PAMLs, 158 from the EHR-PAMLs, and 351 from pharmacy claims data. The EHR-PAMLs and pharmacy claims data correctly reflected 52.1% and 43.2% of the gold-standard PAMLs, respectively. Combining the EHR-PAMLs and pharmacy claims resulted in 69.2% of the gold-standard PAMLs correctly reflected (p < 0.0001). Combining these two data sources increased the accuracy of medication history by 17.1%. CONCLUSION: Combining the EHR-PAML and pharmacy claims data resulted in a significant increase in the number of medications correctly reflected in the gold-standard PAML compared with the EHR-PAML or claims data separately.

Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records.

BACKGROUND: High override rates for drug-drug interaction (DDI) alerts in electronic health records (EHRs) result in the potentially dangerous consequence of providers ignoring clinically significant alerts. Lack of uniformity of criteria for determining the severity or validity of these interactions often results in discrepancies in how these are evaluated. The purpose of this study was to identify a set of criteria for assessing DDIs that should be used for the generation of clinical decision support (CDS) alerts in EHRs. METHODS: We conducted a 20-year systematic literature review of MEDLINE and EMBASE to identify characteristics of high-priority DDIs. These criteria were validated by an expert panel consisting of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. RESULTS: Forty-four articles met the inclusion criteria for assessing characteristics of high-priority DDIs. The panel considered five criteria to be most important when assessing an interaction- Severity, Probability, Clinical Implications of the interaction, Patient characteristics, and the Evidence supporting the interaction. In addition, the panel identified barriers and considerations for being able to utilize these criteria in medication knowledge bases used by EHRs. CONCLUSIONS: A multi-dimensional approach is needed to understanding the importance of an interaction for inclusion in medication knowledge bases for the purpose of CDS alerting. The criteria identified in this study can serve as a first step towards a uniform approach in assessing which interactions are critical and warrant interruption of a provider's workflow.

Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records.

OBJECTIVE: Alert fatigue represents a common problem associated with the use of clinical decision support systems in electronic health records (EHR). This problem is particularly profound with drug-drug interaction (DDI) alerts for which studies have reported override rates of approximately 90%. The objective of this study is to report consensus-based recommendations of an expert panel on DDI that can be safely made non-interruptive to the provider's workflow, in EHR, in an attempt to reduce alert fatigue. METHODS: We utilized an expert panel process to rate the interactions. Panelists had expertise in medicine, pharmacy, pharmacology and clinical informatics, and represented both academic institutions and vendors of medication knowledge bases and EHR. In addition, representatives from the US Food and Drug Administration and the American Society of Health-System Pharmacy contributed to the discussions. RESULTS: Recommendations and considerations of the panel resulted in the creation of a list of 33 class-based low-priority DDI that do not warrant being interruptive alerts in EHR. In one institution, these accounted for 36% of the interactions displayed. DISCUSSION: Development and customization of the content of medication knowledge bases that drive DDI alerting represents a resource-intensive task. Creation of a standardized list of low-priority DDI may help reduce alert fatigue across EHR. CONCLUSIONS: Future efforts might include the development of a consortium to maintain this list over time. Such a list could also be used in conjunction with financial incentives tied to its adoption in EHR.

The unbooked mother: a cohort study of maternal and foetal outcomes in a North London Hospital.

OBJECTIVE: The aim of the study was to determine the pregnancy outcome of unbooked compared to booked women of similar parity and ethnic background over a period of 18 months at the North Middlesex University Hospital (NMUH), London. METHODS: A retrospective cohort study from September 2006 to March 2008 comparing the socio-demographics, foetal and maternal outcomes of pregnancies of unbooked versus booked women. Women who received no antenatal care or who delivered within 3 days of the initial booking visit were categorized as 'unbooked' while the next delivered women on the labour ward register (matched for ethnicity and parity) who booked prior to the second trimester served as comparison. Fischer's exact test (two-tailed), student's t test and Pearson's chi-square test, odds ratio and 95% confidence interval were used to compare the two groups statistically. RESULTS: There were 91 unbooked births in the 18 month study period. Unbooked mothers were younger (26.0 +/- 6.68 vs. 29.2 +/- 6.10 years, p < 0.0001), more likely to be unemployed, unmarried and non-English speaking compared to matched booked mothers (all p < 0.01). The rate of Caesarean sections ([OR] = 0.85, 95% [CI] = 0.39-1.88), p > 0.05), birth asphyxia, Apgar scores <7 at 1 min ([OR] = 1.10, 95% [CI] = 0.52-2.35, p = 0.80) and intrauterine foetal death (stillbirth) ([OR] = 2.03, 95% [CI] = 0.28-14.60, p = 0.60) were comparable between the two groups. However, unbooked mothers were five times more likely to have preterm delivery ([OR] = 6.44, 95% [CI] = 2.24-18.50, p < 0.0002); three times more likely to have low birth weight babies (<2,500 g) ([OR] = 2.87, 95% [CI] = 1.21-6.82, p < 0.02) and twice as likely to have postpartum haemorrhage ([OR] = 1.85, 95% [CI] = 0.69-4.98, p = 0.3). CONCLUSION: Unbooked women were more at risk of adverse foetal and maternal outcomes than booked women, even within a population of young, relatively healthy immigrant women.