Data for drugs available through low-cost prescription drug programs are available through pharmacy benefit manager and claims data.

BACKGROUND: Observational data are increasingly being used for pharmacoepidemiological, health services and clinical effectiveness research. Since pharmacies first introduced low-cost prescription programs (LCPP), researchers have worried that data about the medications provided through these programs might not be available in observational data derived from administrative sources, such as payer claims or pharmacy benefit management (PBM) company transactions. METHOD: We used data from the Indiana Network for Patient Care to estimate the proportion of patients with type 2 diabetes to whom an oral hypoglycemic agent was dispensed. Based on these estimates, we compared the proportions of patients who received medications from chains that do and do not offer an LCPP, the proportion trend over time based on claims data from a single payer, and to proportions estimated from the Medical Expenditure Panel Survey (MEPS). RESULTS: We found that the proportion of patients with type 2 diabetes who received oral hypoglycemic medications did not vary based on whether the chain that dispensed the drug offered an LCPP or over time. Additionally, the rates were comparable to those estimated from MEPS. CONCLUSION: Researchers can be reassured that data for medications available through LCPPs continue to be available through administrative data sources.

Continuity of Care Document (CCD) Enables Delivery of Medication Histories to the Primary Care Clinician.

INTRODUCTION: The goal of the Enhanced Medication History (EMH) project is to provide medication histories to ambulatory primary care practices in the Indiana Network for Patient Care. METHODS: Medications were aggregated from three different sources of pharmacy data (Medicaid, SureScripts, and the county health system of Indianapolis). Dispensing events were assembled into the Continuity of Care Document (CCD), and presented to clinicians as RxNorm Clinical Drugs. RESULTS: The EMH project completed 46 weeks of operation in a community health center in Indianapolis. Medication Histories were generated for 10498 office visits for 4449 distinct patients. Seven (of nine) attending physicians responded to a written survey and found the Medication Histories useful (3.9±0.4 on a scale of 1 to 5). CONCLUSION: Implementation of the EMH project demonstrated the successful use (as well as the challenging aspects) of the CCD and the RxNorm terminology in the outpatient clinical setting.

Development and use of a medication history service associated with a health information exchange: architecture and preliminary findings.

We describe our early experience with use in emergency department settings of a standards-based medication history service integrated into a health information exchange (HIE). The service sends queries from one Exchange's emergency department interface both to a local ambulatory care system and to the medication hub services provided by a second HIE. This second HIE in turn sends requests to SureScripts and returns histories for incorporation into the first Exchange's clinical interface. The service caches all requests to avoid costly duplicate query charges and maintains an account of queries, registered users, charges, and results obtained. Usage may be increasing as additional retail pharmacy data become available. Early results suggest that research and development emphasis requirements will of necessity shift from obtaining prescription medication history to finding new means to ensuring effective use.

Building a production-ready infrastructure to enhance medication management: early lessons from the nationwide health information network.

Poor medication management practices can lead to serious erosion of health care quality and safety. The DHHS Medication Management Use Case outlines methods for the exchange of electronic health information to improve medication management practices. In this case report, the authors describe initial development of Nationwide Health Information Network (NHIN) services to support the Medication Management Use Case. The technical approach and core elements of medication management transactions involved in the NHIN are presented. Early lessons suggest the pathway to improvements in quality and safety are achievable, yet there are challenges for the medical informatics community to address through future research and development activities.

Enhancing an ePrescribing system by adding medication histories and formularies: the Regenstrief Medication Hub.

Medication histories improve health care quality and safety; formularies serve to control costs. We describe the implementation of the Regenstrief Medication Hub: a system to provide both histories and formularies to the Gopher ePrescribing application. Currently the Medication Hub aggregates data from two sources: the RxHub consortium of pharmacy benefit managers, and Wishard Health Services. During one month, the system generated 53,764 queries, each representing a patient visit. RxHub responded with 4,012 histories; Wishard responded with 23,421 histories. The Medication Hub aggregated and filtered these histories before delivering them to Gopher. However, clinician users accessed the histories during only 0.6% of prescribing sessions. The Medication Hub also managed drug benefit eligibility data, which enabled formulary-based decision support. However, clinicians heeded only 41% of warnings based on the Wishard Formulary, and 16% of warnings based on commercial formularies. The Medication Hub is scalable to accommodate additional pharmacy data sources.

Aggregation of pharmacy dispensing data into a unified patient medication history.

The Regenstrief Medication Hub system collects pharmacy data from two different sources: Wishard Health Services, and dispensing claims provided by RxHub. These lists are indexed, aggregated, and filtered, to create a single Medication History for each patient. This history is then provided to the Gopher computerized prescribing system. The Medication Hub is a scalable system, capable of integrating additional sources of pharmacy data.

An effective computerized reminder for contact isolation of patients colonized or infected with resistant organisms.

PURPOSE: To improve contact isolation rates among patients admitted to the hospital with a known history of infection with Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin-resistant Enterococci (VRE). METHODS: A before and after interventional study implementing computerized reminders for contact isolation between February 25, 2005 and February 28, 2006. We measured rates of appropriate contact isolation, and time to isolation for the 4 month pre-intervention period, and the 12 month intervention period. We conducted a survey of ordering physicians at the midpoint of the intervention period. RESULTS: Implementing a computerized reminder increased the rate of patients appropriately isolated from 33% to fully 89% (P<0.0001). The median time to writing contact isolation orders decreased from 16.6 to 0.0 h (P<0.0001). Physicians accepted the order 80% of the time on the first or second presentation. Ninety-five percent of physicians felt the reminder had no impact on workflow, or saved them time. CONCLUSION: A human reviewed computerized reminder can achieve high rates of compliance with infection control recommendations for contact isolation, and dramatically reduce the time to orders being written upon admission.

A computerized decision support system improves the accuracy of temperature capture from nursing personnel at the bedside.

OBJECTIVE: To assess the effect of a computerized decision support system (CDSS) on the accuracy of patient temperature recording at the bed side. DESIGN: This is a randomized, controlled trial comparing nurses assigned to an intervention group that received CDSS whenever they attempted to store a low temperature (

Extensible Stylesheet Language Formatting Objects (XSL-FO): a tool to transform patient data into attractive clinical reports.

Clinicians at Wishard Hospital in Indianapolis print and carry clinical reports called "Pocket Rounds". This paper describes a new process we developed to improve these clinical reports. The heart of our new process is a World Wide Web Consortium standard: Extensible Stylesheet Language Formatting Objects (XSL-FO). Using XSL-FO stylesheets we generated Portable Document Format (PDF) and PostScript reports with complex formatting: columns, tables, borders, shading, indents, dividing lines. We observed patterns of clinical report printing during a eight month study period on three Medicine wards. Usage statistics indicated that clinicians accepted the new system enthusiastically: 78% of 26,418 reports were printed using the new system. We surveyed 67 clinical users. Respondents gave the new reports a rating of 4.2 (on a 5 point scale); they gave the old reports a rating of 3.4. The primary complaint was that it took longer to print the new reports. We believe that XSL-FO is a promising way to transform text data into functional and attractive clinical reports: relatively easy to implement and modify.

A simple error classification system for understanding sources of error in automatic speech recognition and human transcription.

OBJECTIVES: To (1) discover the types of errors most commonly found in clinical notes that are generated either using automatic speech recognition (ASR) or via human transcription and (2) to develop efficient rules for classifying these errors based on the categories found in (1). The purpose of classifying errors into categories is to understand the underlying processes that generate these errors, so that measures can be taken to improve these processes. METHODS: We integrated the Dragon NaturallySpeaking v4.0 speech recognition engine into the Regenstrief Medical Record System. We captured the text output of the speech engine prior to error correction by the speaker. We also acquired a set of human transcribed but uncorrected notes for comparison. We then attempted to error correct these notes based on looking at the context alone. Initially, three domain experts independently examined 104 ASR notes (containing 29,144 words) generated by a single speaker and 44 human transcribed notes (containing 14,199 words) generated by multiple speakers for errors. Collaborative group sessions were subsequently held where error categorizes were determined and rules developed and incrementally refined for systematically examining the notes and classifying errors. RESULTS: We found that the errors could be classified into nine categories: (1) announciation errors occurring due to speaker mispronounciation, (2) dictionary errors resulting from missing terms, (3) suffix errors caused by misrecognition of appropriate tenses of a word, (4) added words, (5) deleted words, (6) homonym errors resulting from substitution of a phonetically identical word, (7) spelling errors, (8) nonsense errors, words/phrases whose meaning could not be appreciated by examining just the context, and (9) critical errors, words/phrases where a reader of a note could potentially misunderstand the concept that was related by the speaker. CONCLUSIONS: A simple method is presented for examining errors in transcribed documents and classifying these errors into meaningful and useful categories. Such a classification can potentially help pinpoint sources of such errors so that measures (such as better training of the speaker and improved dictionary and language modeling) can be taken to optimize the error rates.