M.D. computing : computers in medical practice最新文献

We examined the reliability and validity of computer-administered versions of the Hamilton Depression (HAMD) and Hamilton Anxiety (HAMA) Rating Scales that were administered over the telephone using Interactive Voice Response (IVR). In two identical studies (HAMD: N = 113, HAMA: N = 74), both the IVR- and clinician-administered versions were administered in a counterbalanced order to a heterogeneous sample of subjects with psychiatric disorders and controls. Both the IVR HAMD and HAMA demonstrated adequate internal-consistency reliability (.90 and .93, respectively) and test-retest reliability (.74 and .97, respectively). The correlation between the IVR and clinician was high (HAMD = .96; HAMA = .65). The mean score difference between the IVR and clinician versions was less than one point for both the HAMD (.69 of a point) and HAMA (.60 of a point). It took subjects 12.23 minutes to complete the IVR HAMD, compared to 15.21 minutes for the clinician version; and 11.27 minutes for the IVR HAMA, compared to 15.33 minutes for the clinician (p < .001 for both comparisons). Subjects rated the clinician better in the areas of how much they liked being interviewed and how well they were able to describe their feelings. However, they were significantly more embarrassed with the clinician than with the IVR. Results support the psychometric properties of the IVR versions of the HAMD and HAMA scales. IVR technology presents new opportunities for expanding the utility of computerized clinical assessment.

The use of the Internet makes it possible to bring together learners and teachers from rural areas and academic centers and to deliver well-accepted educational materials quickly and effectively. The objective of this study was to determine feasibility and the effectiveness of a problem-based small-group learning (PBSGL) intervention conducted via the Internet in a randomized controlled trial. A group of 23 family physicians from rural Northern Ontario practices and across Canada were randomly assigned to a study group (n = 11) and a control group (n = 12). The study group spent two months discussing the topic of depression in the elderly with the help of a facilitator and two geriatric psychiatrists. The control group was given similar educational resources via the Internet but without the benefit of the small-group interaction. Outcome measures included qualitative feedback from the learners and teachers as well as a Multiple Choice Questions (MCQ) test before and after the study. The study provided important insight into the feasibility, keys to success, utility of Internet-assisted education from an education and evaluation perspective. Although the MCQ testing used revealed no significant differences between the study group and the control group, the usefulness of the measure is considered within the context of the educational approach. It is unclear whether this method of continuing medical education (CME) represents an effective way to conduct such activities.

Although electronic medical records and a central database have made accurate and consistent patient medical information more readily available than with the traditional patient chart, there are many locations in healthcare facilities where terminals for accessing patient data are not available. As patient care becomes decentralized and more patients require anesthesia outside of the operating suites, routing a network-based system to all these locations can be expensive and time consuming. We designed a system whereby essential patient data of interest to anesthesiologists is stored on an electronic memory device the size of a watch battery attached to the patient's wristband. Accessing and editing the data is done via a hand-held computer. This system provides secure patient data storage and management at the "point of care." At the conclusion of the patient's anesthesia-related care, the data is downloaded to a relational database for use in outcome analysis, billing, and quality assurance. After collecting preoperative evaluations, intraoperative data, and postoperative data on 560 patients anesthetized for surgery or other procedures, we find this system to be a reliable, low-cost, medical information management system, with possible application to other medical specialties.

Academic cancer centers will be hit, simultaneously, by all three of the technology tidal waves outlined above within the next five years. In preparing for this impact one should note the central role that Internet technologies will play in providing solutions in all three areas. In addition, as the volume and size of data objects increases dramatically, having an adequate networking infrastructure in place will be crucial. So what do we do now to prepare for the future? The following five steps are suggested: (1) Establish an oncology informatics group within the cancer center to provide the necessary expertise and begin the planning process. (2) Begin implementing a secure intranet based on standard Internet technologies. (3) Work with the host medical center and external agencies to determine who will pay for and implement a high-bandwidth networking infrastructure. (4) Recruit a bioinformatician who can help implement technologies to take advantage of the genomics data wave when it hits. (5) Ensure that the cancer center's EMR system can support cancer protocol data and facilitate the retrieval and delivery of the complex digital imaging data that are in our future.