William E. Hammond

Improving drug prescribing in a primary care practice.

A model for improving physician prescribing that utilizes computerized feedback was studied in a family medicine residency practice. Resident and faculty physicians were stratified by level of experience and randomized into two groups. For 9 months the experimental group received monthly printouts identifying drugs they had prescribed by brand name with estimates of cost savings that might have been realized by prescribing generic drugs. The control group received no feedback. Prescription monitoring of both groups continued for 12 months after all feedback had ceased. Median weighted rates of generic prescribing for the experimental physicians were 14% for the baseline, 67% for the feedback, and 54% for the follow-up periods. Rates for the control physicians for the three periods were 32%, 37% and 31%, respectively. The increase in generic prescribing by physicians in the experimental group was significantly greater than for control physicians (P = 0.01). The feedback model improved rates of generic prescribing but should be evaluated for broader areas of physician prescribing.

A Chartless Record—Is It Adequate?

The computerized medical record supported by The Medical Record (TMR) has been the only record of physician-patient encounters on the nephrology service of the Durham Veterans Administration Medical Center since April 1981. Physicians using the system evaluated the adequacy of the computerized record as a replacement for the paper chart. The computerized record was able to capture and display all pertinent data. Manual or computerized narratives provided a useful supplement to the core computerized record only in those rare instances that a physician needed to point out which of the data in the record were important to his decision making.

Standard Formats for Electronic Transfer of Clinical Data

Excerpt Technical progress depends on standards. Standards for gauge and size make it possible to thread nuts onto bolts. Standards for voltage and frequency permit the use of an electrical applian...

Characteristics of adult primary care patients as predictors of future health services charges.

Background.Utilization risk assessment is potentially useful for allocation of health care resources, but precise measurement is difficult. Objective.Test the hypotheses that health-related quality of life (HRQOL), severity of illness, and diagnoses at a single primary care visit are comparable case-mix predictors of future 1-year charges in all clinical settings within a large health system, and that these predictors are more accurate in combination than alone. Research Design. Longitudinal observational study in which subjects’ characteristics were measured at baseline, and their outpatient clinic visits and charges and their inpatient hospital days and charges were tracked for 1 year. Subjects.Adult primary care patients. Measures.Duke Health Profile for HRQOL, Duke Severity of Illness Checklist for severity of illness, and Johns Hopkins Ambulatory Care Groups for diagnostic groups classification. Results.Of 1,202 patients, 84.4% had follow up in the primary care clinic, 63.2% in subspecialty clinics, 14.8% in the emergency room, and 9.6% in the hospital. Of

Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting

6,290,775 total charges,

A national agenda for public health informatics.

779,037 (12.2%) was for follow-up primary care. The highest accuracy was found for predicting primary care charges, where R2 for predictors ranged from 0.083 for medical record auditor-reported severity of illness to 0.107 for HRQOL. When predictors were combined, the highest R2 of 0.125 was found for the combination of HRQOL and diagnostic groups. Conclusions.Baseline HRQOL, severity of illness, and diagnoses were comparable predictors of 1-year health services charges in all clinical sites but most predictive for primary care charges, and were more accurate in combination than alone.

The role of standards in creating a health information infrastructure

Large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy. AMIAs 2012 Health Policy Meeting brought together healthcare academics, policy makers, and system stakeholders (including representatives of patient groups) to consider these topics and formulate recommendations. A review of a set of Proposed Principles of Health Data Use led to a set of findings and recommendations, including the assertions that the use of health data should be viewed as a public good and that achieving the broad benefits of this use will require understanding and support from patients.

Data mining methods find demographic predictors of preterm birth.

The American Medical Informatics Association 2001 Spring Congress brought together the public health and informatics communities to develop a national agenda for public health informatics. Discussions on funding and governance; architecture and infrastructure; standards and vocabulary; research, evaluation, and best practices; privacy, confidentiality, and security; and training and workforce resulted in 74 recommendations with two key themes: (1) all stakeholders need to be engaged in coordinated activities related to public health information architecture, standards, confidentiality, best practices, and research and (2) informatics training is needed throughout the public health workforce. Implementation of this consensus agenda will help promote progress in the application of information technology to improve public health.

Computerized medical records

In the belief that the existence of a variety of standards is an absolute necessity for health care professional workstations to work, this paper provides a detailed overview of the standards efforts of a number of groups. According to the International Standards Organization (ISO) Reference Model, workstations require a full level of standards from the physical level through and beyond the applications level. Rapidly changing technology challenges acceptance of standards at the lower levels. Current recommendations include fiberoptic media using certain protocols. Other standards in these lower levels also have support. At the applications level, data messaging standards are being developed by six groups. The consensus standards body for the United States is coordinating the efforts of these groups in order to produce a harmonized effort, and is coordinating the effort with Europe for an international effort. Work on the development of the full set of standards necessary for workstation implementations is lagging. Accelerating the process is mandatory if we are to achieve the necessary seamless interoperability required by workstations for ubiquitous intelligent communications between the workstations and the sources of data.

Data Standards: At the Intersection of Sites, Clinical Research Networks, and Standards Development Initiatives:

BackgroundPreterm births in the United States increased from 11.0% to 11.4% between 1996 and 1997; they continue to be a complex healthcare problem in the United States. ObjectiveThe objective of this research was to compare traditional statistical methods with emerging new methods called data mining or knowledge discovery in databases in identifying accurate predictors of preterm births. MethodAn ethnically diverse sample (N = 19,970) of pregnant women provided data (1,622 variables) for new methods of analysis. Preterm birth predictors were evaluated using traditional statistical and newer data mining analyses. ResultsSeven demographic variables (maternal age and binary coding for county of residence, education, marital status, payer source, race, and religion) yielded a .72 area under the curve using Receiving Operating Characteristic curves to test predictive accuracy. The addition of hundreds of other variables added only a .03 to the area under the curve. ConclusionSimilar results across data mining methods suggest that results are data-driven and not method-dependent, and that demographic variables offer a small set of parsimonious variables with reasonable accuracy in predicting preterm birth outcomes in a racially diverse population.