User-Centered Design of a Preference-Driven Patient Activation Tool for Optimizing Depression Treatment in Integrated Primary Care Settings (The Transform DepCare Study).

BACKGROUND: Few patient engagement tools incorporate the complex patient experiences, contexts, and workflows that limit depression treatment implementation. OBJECTIVE: Describe a user-centered design (UCD) process for operationalizing a preference-driven patient activation tool. DESIGN: Informed by UCD and behavior change/implementation science principles, we designed a preference-driven patient activation prototype for engaging patients in depression treatment. We conducted three usability cycles using different recruitment/implementation approaches: near live/live testing in primary care waiting rooms (V1-2) and lab-based think aloud testing (V3) oversampling older, low-literacy, and Spanish-speaking patients in the community and via EHR algorithms. We elicited clinician and "heuristic" expert input. MAIN MEASURES: We administered the system usability scale (SUS) all three cycles and pre-post V3, the patient activation measure, decisional conflict scale, and depression treatment barriers. We employed descriptive statistics and thematically analyzed observer notes and transcripts for usability constructs. RESULTS: Overall, 43 patients, 3 clinicians, and 5 heuristic (a usability engineering method for identifying usability problems) experts participated. Among patients, 41.9% were ≥ 65 years old, 79.1% female, 23.3% Black, 62.8% Hispanic, and 55.8% Spanish-speaking and 46.5% had ≤ high school education. We described V1-3 usability (67.2, 77.3, 81.8), treatment seeking (92.3%, 87.5%, 92.9%), likelihood/comfort discussing with clinician (76.9%, 87.5%, 100.0%), and pre vs. post decisional conflict (23.7 vs. 15.2), treatment awareness (71.4% vs. 92.9%), interest in antidepressants (7.1% vs. 14.3%), and patient activation (66.8 vs. 70.9), with fewer barriers pertaining to cost/insurance, access/coordination, and self-efficacy/stigma/treatment efficacy. Key themes included digital literacy, understandability, high acceptability for aesthetics, high usefulness of patient/clinician videos, and workflow limitations. We adapted manual entry/visibility/content; added patient activation and a personalized algorithm; and proposed flexible, care manager delivery leveraging clinic screening protocols. DISCUSSION: We provide an example of leveraging UCD to design/adapt a real-world, patient experience and workflow-aligned patient activation tool in diverse populations.

Imprecision and Preferences in Interpretation of Verbal Probabilities in Health: a Systematic Review.

INTRODUCTION: Many health providers and communicators who are concerned that patients will not understand numbers instead use verbal probabilities (e.g., terms such as "rare" or "common") to convey the gist of a health message. OBJECTIVE: To assess patient interpretation of and preferences for verbal probability information in health contexts. METHODS: We conducted a systematic review of literature published through September 2020. Original studies conducted in English with samples representative of lay populations were included if they assessed health-related information and elicited either (a) numerical estimates of verbal probability terms or (b) preferences for verbal vs. quantitative risk information. RESULTS: We identified 33 original studies that referenced 145 verbal probability terms, 45 of which were included in at least two studies and 19 in three or more. Numerical interpretations of each verbal term were extremely variable. For example, average interpretations of the term "rare" ranged from 7 to 21%, and for "common," the range was 34 to 71%. In a subset of 9 studies, lay estimates of verbal probability terms were far higher than the standard interpretations established by the European Commission for drug labels. In 10 of 12 samples where preferences were elicited, most participants preferred numerical information, alone or in combination with verbal labels. CONCLUSION: Numerical interpretation of verbal probabilities is extremely variable and does not correspond well to the numerical probabilities established by expert panels. Most patients appear to prefer quantitative risk information, alone or in combination with verbal labels. Health professionals should be aware that avoiding numeric information to describe risks may not match patient preferences, and that patients interpret verbal risk terms in a highly variable way.

Faces Scales for Anxiety and Anger: A National Study of Measurement Properties.

Importance: Faces scales are used worldwide to assess pain, but robust faces scales for anxiety and anger do not exist. These scales are urgently needed, because an estimated two-thirds of patients have difficulty reading written questionnaires.Objective: To develop and evaluate measurement properties of faces scales to monitor two mental health symptoms in US adults (anxiety and anger) in accordance with the COnsensus-based Standards for health Measurement INstruments (COSMIN).Methods: The development process included population identification, scale generation, and pretesting. The evaluation process included assessment of content validity, construct validity, criterion validity, test-retest reliability, and measurement error using 5 order-randomized, positively controlled online survey studies conducted between April and June 2020. We recruited national purposive samples of US adults representative on age, gender, and race. For each faces scale, participants assessed relevance, comprehensibility, and comprehensiveness (study 1, n = 300), strength-of-association (study 2, n = 300), convergent validity against the visual analog scale (VAS; study 3, n = 305), convergent validity against the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires (study 4, n = 1,000), and test-retest reliability and measurement error (study 5, n = 853).Results: The anxiety and anger faces scales showed high relevance (95%-96%), comprehensibility (93%-97%), comprehensiveness (94%-97%), and strength-of-association (74%-96%). We found very high agreement with the VAS (ρ = 0.94-0.95) and high agreement with PROMIS questionnaires (ρ = 0.74-0.79). Scales showed adequate test-retest reliability (intraclass correlation = 0.70-0.78) and measurement error (standard error of measurement = 1.14-1.22).Conclusions: Faces scales to monitor anxiety and anger show adequate measurement properties, including content validity, construct validity, criterion validity, test-retest reliability, and measurement error. The recommended use is non-diagnostic monitoring of anxiety and anger, particularly when mental health is an ancillary but important outcome of treatment.

Published models that predict hospital readmission: a critical appraisal.

INTRODUCTION: The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness. OBJECTIVE: To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation. METHODS: We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors. RESULTS: We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%). CONCLUSIONS: The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

A deep database of medical abbreviations and acronyms for natural language processing.

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6-14% increase in abbreviation coverage; 28-52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations .