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Biomedical subjects

James R Flanagan

Publications and source records attributed to James R Flanagan.

3 recordsLinked to original sources
PubMed2004

Interactive video specialty consultations in long-term care.

OBJECTIVES: To assess provider and resident satisfaction with and outcomes of specialist physician consultations provided via interactive video to residents of a long-term care (LTC) center. DESIGN: Cross-sectional survey. SETTING: Two Veterans Affairs Medical Centers (VAMC) and a state LTC center. PARTICIPANTS: Physicians (n=12) at the VAMC and nurses (n=30) and residents (n=62) at the LTC center. INTERVENTION: Interactive video conferencing to provide physician specialty visits to residents at the LTC center. MEASUREMENTS: Satisfaction ratings and record review to determine changes in treatment plan and follow-up care. RESULTS: Data were collected on 76 individual consultations in six clinics. The most frequent outcome was a change in treatment plan with the resident remaining at the LTC setting (n=29, 38%) or no change in treatment (n=26, 34%). Physicians' ratings were 78% good to excellent for usefulness in developing a diagnosis, 87% good to excellent for usefulness in developing a treatment plan, 79% good to excellent for quality of transmission, and 86% good to excellent satisfaction with the consult format. Overall, 72% of residents were satisfied with the consult format, and 92% felt that it was easier to obtain medical care via telemedicine. Nurses felt that the telemedicine clinics were a good use of their time and skills (100%). CONCLUSION: There was a high rate of physician, patient, and nurse satisfaction with interactive video conferencing. Care delivered to residents of LTC settings via video conferencing offers a number of potential advantages, including avoidance of travel for patient and provider and potentially greater continuity of care.

Cross-Sectional Studies↗
PubMed2002

Email recruitment to use web decision support tools for pneumonia.

Application of guidelines to improve clinical decisions for Community Acquired Pneumonia (CAP) patients depends on accurate information about specific facts of each case and on presenting guideline support at the time decisions are being made. We report here on a system designed to solicit information from physicians about their CAP patients in order to classify CAP and present appropriate guidelines for type of care, length of stay, and use of antibiotics. We used elements of three existing information systems to create a achieve these goals: professionals coding diagnoses captured by the existing clinical information system (CIS), email, and web-based decision support tools including a pneumonia severity evaluation tool (SET). The non-secure IS components (email and web) were able to link to information in the CIS using tokens that do not reveal confidential patient-identifiable information. We examined their response to this strategy and the accuracy of pneumonia classification using this approach compared to chart review as a gold standard. On average physicians responded to email solicitations 50% of the time over the 14 month study. Also using this standard, we examined various information triggers for case finding. Professional coding of the primary reason for admission as pneumonia was fairly sensitive as an indicator of CAP. Physician use of the web SET was insensitive but fairly specific. Pneumonia classification using the SET was very reliable compared to experts' chart review using the same algorithm. We examined the distribution of severity of pneumonia for cases of pneumonia found by the various information triggers and for each severity the average length of stay. The distribution found by both chart review and by SET has demonstrated a shift toward more severe cases being admitted compared to only 3 years ago. The length of stay for level of severity is above expectations published by guidelines even for cases of true CAP by chart review. We suggest that the Fine classification system may not adequately describe patients in this setting. Physicians frequently responded that the guidelines presented did not fit their patients.

Algorithms↗