Use of online knowledge base in primary health care and correlation to health care quality: an observational study

Doctor! Did you Google my symptoms? A qualitative study of patient perceptions of doctors' point-of-care information seeking

OBJECTIVE

To explore patient perceptions regarding doctors' information seeking during consultations.

DESIGN AND SETTING

Qualitative interviews with participants from six general practice waiting rooms in South East Queensland, Australia. Participants were asked about their experiences and opinions, and to comment on short videos of simulated consultations in which a doctor sought information. The interviews were analysed through a process of iterative thematic analysis using the framework of Braun and Clarke.

PARTICIPANTS

The 16 participants were purposively sampled including 5 men and 11 women from a diverse range of educational and age groups.

RESULTS

How a doctor's need to look up information impacted patient impressions of competence and trust was an overarching theme. The four dominant themes include: the trust a patient has in the doctor before the consultation, whether the doctor is expected to know the answer to a question without searching, has the doctor added value to the consultation by searching and the consultation skills used in the process.

CONCLUSIONS

Patient trust is fundamental to positive perceptions of general practitioners' information seeking at the point-of-care. Communication is key to building this trust. Understanding the patient's agenda, listening, assessing thoroughly and being honest and transparent about the need to seek information all contribute to a positive experience.

Title of Project: A Novel Tool that Allows Interactive Screening of PubMed Citations Showed Promise for the Semi-Automation of Identification of Biomedical Literature

OBJECTIVE

Systematic reviews form the basis of evidence-based medicine but are expensive and time-consuming to produce. To address this burden, we have developed a literature identification system (Pythia) that combines the query formulation and citation screening steps.

STUDY DESIGN

Pythia incorporates a set of natural-language questions with machine-learning algorithms to rank all PubMed citations based on relevance, returning the 100 top-ranked citations for human screening. The tagged citations are iteratively exploited by Pythia to refine the search and re-rank the citations.

RESULTS

Across seven systematic reviews, the ability of Pythia to identify the relevant citations (sensitivity) ranged from 0.09 to 0.58. The number of abstracts reviewed per relevant abstract (NNR) was lower than in the manually screened project in four reviews, higher in two, and had mixed results in one. The reviews that had greater overall sensitivity retrieved more relevant citations in early batches, but retrieval was generally unaffected by other aspects, such as study design, study size, and specific key question.

CONCLUSIONS

Due to its low sensitivity, Pythia is not ready for widespread use. Future research should explore ways to encode domain knowledge in query formulation to better enrich the questions used in the search.