Frequency and impact of medication reviews for people aged 65 years or above in UK primary care: an observational study using electronic health records.

BACKGROUND: Medication reviews in primary care provide an opportunity to review and discuss the safety and appropriateness of a person's medicines. However, there is limited evidence about access to and the impact of routine medication reviews for older adults in the general population, particularly in the UK. We aimed to quantify the proportion of people aged 65 years and over with a medication review recorded in 2019 and describe changes in the numbers and types of medicines prescribed following a review. METHODS: We used anonymised primary care electronic health records from the UK's Clinical Practice Research Datalink (CPRD GOLD) to define a population of people aged 65 years or over in 2019. We counted people with a medication review record in 2019 and used Cox regression to estimate associations between demographic characteristics, diagnoses, and prescribed medicines and having a medication review. We used linear regression to compare the number of medicines prescribed as repeat prescriptions in the three months before and after a medication review. Specifically, we compared the 'prescription count' - the maximum number of different medicines with overlapping prescriptions people had in each period. RESULTS: Of 591,726 people prescribed one or more medicines at baseline, 305,526 (51.6%) had a recorded medication review in 2019. Living in a care home (hazard ratio 1.51, 95% confidence interval 1.40-1.62), medication review in the previous year (1.83, 1.69-1.98), and baseline prescription count (e.g. 5-9 vs 1 medicine 1.41, 1.37-1.46) were strongly associated with having a medication review in 2019. Overall, the prescription count tended to increase after a review (mean change 0.13 medicines, 95% CI 0.12-0.14). CONCLUSIONS: Although medication reviews were commonly recorded for people aged 65 years or over, there was little change overall in the numbers and types of medicines prescribed following a review. This study did not examine whether the prescriptions were appropriate or other metrics, such as dose or medicine changes within the same class. However, by examining the impact of medication reviews before the introduction of structured medication review requirements in England in 2020, it provides a useful benchmark which these new reviews can be compared with.

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach.

BACKGROUND: Patient activation is defined as a patient's confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. OBJECTIVE: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. METHODS: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. RESULTS: The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. CONCLUSIONS: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions.