Edward P. Hoffer

The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs)

BACKGROUND In an era of short inpatient stays, residents may overlook relevant elements of the differential diagnosis as they try to evaluate and treat patients. However, if a residents first principal diagnosis is wrong, the patients appropriate evaluation and treatment may take longer, cost more, and lead to worse outcomes. A diagnostic decision support system may lead to the generation of a broader differential diagnosis that more often includes the correct diagnosis, permitting a shorter, more effective, and less costly hospital stay. METHODS We provided residents on General Medicine services access to DXplain, an established computer-based diagnostic decision support system, for 6 months. We compared charges and cost of service for diagnostically challenging cases seen during the fourth through sixth month of access to DXplain (intervention period) to control cases seen in the 6 months before the system was made available. RESULTS 564 cases were identified as diagnostically challenging by our criteria during the intervention period along with 1173 cases during the control period. Total charges were

Computers in medical education

1281 lower (p=.006), Medicare Part A charges

Healthcare information technology and economics

1032 lower (p=0.006) and cost of service

Experience with the use of computer simulation models in medical education

990 lower (p=0.001) per admission in the intervention cases than in control cases. CONCLUSIONS Using DXplain on all diagnostically challenging cases might save our medical center over

Computer interpretation of ventilatory studies

2,000,000 a year on the General Medicine Services alone. Using clinical diagnostic decision support systems may improve quality and decrease cost substantially at teaching hospitals.

Presence of key findings in the medical record prior to a documented high-risk diagnosis

The goals of medical education are to provide students and graduate clinicians specific facts and information, to teach strategies for applying this knowledge appropriately to the situations that arise in medical practice, and to encourage development of skills necessary to acquire new knowledge over a lifetime of practice. Students must learn about physiological processes and must understand the relationships between their observations and these underlying processes. They must learn to perform medical procedures, and they must understand the effects of different interventions on health outcomes. Medical school faculty employ a variety of strategies for teaching, ranging from the one-way, lecture-based transmission of information to the interactive, Socratic method of instruction. In general, we can view the teaching process as the presentation of a situation or a body of facts that contains the essential knowledge that students should learn; the explanations of what the important concepts and relationships are, how they can be derived, and why they are important; and the strategy for guiding interaction with a patient.

The capturing of more detailed medical information in costar

At the 2011 American College of Medical Informatics (ACMI) Winter Symposium we studied the overlap between health IT and economics and what leading healthcare delivery organizations are achieving today using IT that might offer paths for the nation to follow for using health IT in healthcare reform. We recognized that health IT by itself can improve health value, but its main contribution to health value may be that it can make possible new care delivery models to achieve much larger value. Health IT is a critically important enabler to fundamental healthcare system changes that may be a way out of our current, severe problem of rising costs and national deficit. We review the current state of healthcare costs, federal health IT stimulus programs, and experiences of several leading organizations, and offer a model for how health IT fits into our health economic future.

Patient Simulations in Clinical Education

Abstract This paper describes the rationale for using computer simulation models as aids in medical education. One such model for teaching about oral anticoagulant drug-clotting factor interaction and the effect of other drugs and illness on this interaction is briefly described. The rest of the paper describes a model used for teaching the management of diabetic ketoacidosis. This model uses empiric equations to simulate the interaction of insulin, blood sugar, serum potassium, PH, acetone and a variety of other parameters. Student input is accepted in free text. A “consultant” function is available for direct didactic interaction. This model has been used by several hundred students with favorable results.

Sharing Computer-Based Simulations for Clinical Education

Abstract A computer system is described which accepts raw data from a variety of ventilatory tests, calculates final and predicted normal values, and immediately prints a tabular report of these tests with an interpretation of the results. The logic used in giving interpretations of the test data is presented in detail. The system has proven satisfactory for daily clinical use with over twenty-five hundred reports to date.

Computer-aided-instruction in the emergency department.

BACKGROUND Failure or delay in diagnosis is a common preventable source of error. The authors sought to determine the frequency with which high-information clinical findings (HIFs) suggestive of a high-risk diagnosis (HRD) appear in the medical record before HRD documentation. METHODS A knowledge base from a diagnostic decision support system was used to identify HIFs for selected HRDs: lumbar disc disease, myocardial infarction, appendicitis, and colon, breast, lung, ovarian and bladder carcinomas. Two physicians reviewed at least 20 patient records retrieved from a research patient data registry for each of these eight HRDs and for age- and gender-compatible controls. Records were searched for HIFs in visit notes that were created before the HRD was established in the electronic record and in general medical visit notes for controls. RESULTS 25% of records reviewed (61/243) contained HIFs in notes before the HRD was established. The mean duration between HIFs first occurring in the record and time of diagnosis ranged from 19 days for breast cancer to 2 years for bladder cancer. In three of the eight HRDs, HIFs were much less likely in control patients without the HRD. CONCLUSIONS In many records of patients with an HRD, HIFs were present before the HRD was established. Reasons for delay include non-compliance with recommended follow-up, unusual presentation of a disease, and system errors (eg, lack of laboratory follow-up). The presence of HIFs in clinical records suggests a potential role for the integration of diagnostic decision support into the clinical workflow to provide reminder alerts to improve the diagnostic focus.