Patient-reported outcome measures in value-based healthcare:A multiple methods study to assess patient-centredness

Objectives: Value-based healthcare (VBHC) involves the use of standardised outcome measures, including patient-reported outcome measures (PROMs). This study aimed to assess to what extent discussion of PROMs is associated with patient- and person-centredness. Methods: This study used a separate sample pre-/post-test design and multiple methods (observations, questionnaires, and interviews) in a VBHC care pathway for patients with a vestibular schwannoma, to assess to what extent the implementation of PROMs is associated with a difference in patient- and person-centredness. Results: A total of 139 patients with a vestibular schwannoma and their four treating physicians were included in the study. No significant differences were found in observed patient-centredness (Mpre=6.71 ± 2.42 vs. Mpost=6.93 ± 2.01; P = 0.60) or patient-reported patient-centredness (Mpre=1.73 vs. Mpost=1.68; P = 0.63) and person-centredness after PROM implementation (Mpre=11.81 vs. Mpost=13.42; P = 0.34). We observed more discussion of patient-reported outcomes. However, a majority of patients did not expect PRO discussion in consultations. Conclusions: The implementation of standardised PROMs in a VBHC care pathway was associated with more discussion on patient-reported outcomes in clinical consultations. Overall, the implementation of PROMs was not observed or perceived as leading to more patient-centred consultations. Practice implications: Physicians should assess whether the discussion of PROMs add value collaboratively with patients.</p

Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM)

Abstract Background Evaluating patients’ experiences is essential when incorporating the patients’ perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recognized. Natural language processing (NLP) can automate the analysis of open-ended questions for an efficient approach to patient-centeredness. Methods We developed the Artificial Intelligence Patient-Reported Experience Measures (AI-PREM) tool, consisting of a new, open-ended questionnaire, an NLP pipeline to analyze the answers using sentiment analysis and topic modeling, and a visualization to guide physicians through the results. The questionnaire and NLP pipeline were iteratively developed and validated in a clinical context. Results The final AI-PREM consisted of five open-ended questions about the provided information, personal approach, collaboration between healthcare professionals, organization of care, and other experiences. The AI-PREM was sent to 867 vestibular schwannoma patients, 534 of which responded. The sentiment analysis model attained an F1 score of 0.97 for positive texts and 0.63 for negative texts. There was a 90% overlap between automatically and manually extracted topics. The visualization was hierarchically structured into three stages: the sentiment per question, the topics per sentiment and question, and the original patient responses per topic. Conclusions The AI-PREM tool is a comprehensive method that combines a validated, open-ended questionnaire with a well-performing NLP pipeline and visualization. Thematically organizing and quantifying patient feedback reduces the time invested by healthcare professionals to evaluate and prioritize patient experiences without being confined to the limited answer options of closed-ended questions

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (|r^g| ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (∣r̂ g∣ ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.</p