Keywords: medical data, learning needs, family medicine
The use of medical data software has become routine for all types of health care providers. Data analysis can provide a detailed picture of the activity over a period of time or specific patient categories. The scientific sources show the interest in the widespread use of these technological facilities. The question is whether they can be used by individual doctor to improve their own professional knowledge.
Could medical data recorded using an office software be effective in identifying personal learning needs?
Retrospective descriptive study on a group of 1466 patients over 18 years of age, representing the capitation list of an urban Family Medicine practice. Consultations registered between 01.01.2020 and 31.12. 2022 were analysed, extracting data regarding the number of clinical examinations, referrals, the main symptoms and signs, prescriptions issued as well as recommended investigations.
During this period 12899 consultations were given, with 11147 clinical examinations (86.41% of the total). Preliminary results show: 1. The top 3 main symptoms: pain - 907 cases (8.13%), headache - 203 cases (1.82%), dizziness - 187 cases (1.67%); 2. The top 3 main pathological clinical signs: cough - 467 cases (4.18%), varicose veins - 295 cases (2.64%), oedema - 163 cases (1.46%).
There were 4089 referrals to clinical specialties, with the top 3 : Rheumatology: 599 (14.64%), Cardiology - 500 (12.22%), Ophthalmology : 310 (7.58%). There were 1265 recommendations for paraclinical investigations, the most - 819 (66.31%) being for haematological constants and radiological examinations: 230 (18.18%). In the same period, 10353 prescriptions were issued.
Knowing the patient's features, the practician can identify, by a self-reflection process, his own proffesional training needs according with the care needs of the population he serves. The data provided are preliminary but open the way to more detailed analyses on the rightness or wrongness of some professional attitudes detected.
Points for discussion:
validity of recorded data
barriers in using the software
adjusting to local conditions