PubMed · 4718530
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G Vash. 1973-08-16. Problem-oriented record for relaxed physician.. https://doi.org/10.1056/nejm197308162890728
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OBJECTIVE: To analyse the quality of information transmitted by the interclinical form (DI-1) between primary and secondary care levels; and the frequency and causes of the primary care doctor's not receiving a reply. DESIGN: Descriptive and blind for the doctors taking part. SETTING: Seven health centres and 12 specialist out-clinics in the Bizkaia Health region. PARTICIPANTS: The DIs issued by 43 general practitioners and answers from the specialists. INTERVENTIONS: We recorded all the referrals during a 7-day period (18-26 April 1994). After the normal waiting time we went to the centres to collect the returned DIs. Patients whose form was not found in the records were phoned. Five people (3 family doctors: one as Director and two specialists) evaluated the DI, in line with established criteria. RESULTS: Information was lost in 78% of the total number of referrals. The main cause was the specialist not replying (50.7%); the second, the route the form took and the patient (20.9%). The quality of information of the DI analysed was good/acceptable in 92.5% of those issued and in 90% of replies. CONCLUSIONS: The high percentage loss of information was mainly due to the specialist and, secondly, to the route taken and patient. We recommended the introduction of internal mail between both levels and a study of the attitudes and expectations of the professionals concerned in order to improve the interrelationship and quality of health provision.
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We evaluated the ability of two natural language parsers, CLARIT and the Xerox Tagger, to identify simple, noun phrases in medical discharge summaries. In twenty randomly selected discharge summaries, there were 1909 unique simple noun phrases. CLARIT and the Xerox Tagger exactly identified 77.0% and 68.7% of the phrases, respectively, and partially identified 85.7% and 80.8% of the phrases. Neither system had been specially modified or tuned to the medical domain. These results suggest that it is possible to apply existing natural language processing (NLP) techniques to large bodies of medical text, in order to empirically identify the terminology used in medicine. Virtually all the noun phrases could be regarded as having special medical connotation and would be candidates for entry into a controlled medical vocabulary.