Stephane Meystre | PubMed Health

PubMed2005

Automation of a problem list using natural language processing.

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information.

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PubMed2005

The current state of telemonitoring: a comment on the literature.

Telemonitoring, is defined as the use of information technology to monitor patients at a distance. This literature review suggests that the most promising applications for telemonitoring is for chronic illnesses such as cardiopulmonary disease, asthma, and heart failure in the home. Fetal heart rate monitoring and infant cardiopulmonary functions have also been monitored at a distance, as well as coagulation, or the level of activity of elderly people, assessed by the intelligent home monitoring devices. Hospitals, clinics, and prisons all have used telemonitoring, as have ambulances equipped with systems connected to the receiving hospital. Telemonitoring allows reduction of chronic disease complications thanks to a better follow-up; provides health care services without using hospital beds; and reduces patient travel, time off from work, and overall costs. Several systems have proven to be cost effective. Telemonitoring is also a way of responding to the new needs of home care in an ageing population. Real-time monitoring of patients in ambulances reduces the time to initiate treatment and allows the emergency crew to be better prepared. The obstacles to telemonitoring development include the initial costs of systems, physician licensing, and reimbursement. In the future, virtual reality, immersive environments, haptic feedback and nanotechnology promise new ways in improving the capabilities of telemonitoring.

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