Introduction

While there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation of causal effects from both observational data and randomised experiments (eg, A Guideline for Reporting Mediation Analyses of Randomised Trials and Observational Studies, Consolidated Standards of Reporting Trials, PATH) and on prediction modelling (eg, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis), none is purposely made for deriving and validating models from observational data to predict counterfactuals for individuals on one or more possible interventions, on the basis of given (or inferred) causal structures. This paper describes methods and processes that will be used to develop a Reporting Guideline for Causal and Counterfactual Prediction Models (PRECOG).

Methods and analysis

PRECOG will be developed following published guidance from the Enhancing the Quality and Transparency of Health Research (EQUATOR) network and will comprise five stages. Stage 1 will be meetings of a working group every other week with rotating external advisors (active until stage 5). Stage 2 will comprise a systematic review of literature on counterfactual prediction modelling for biomedical sciences (registered in Prospective Register of Systematic Reviews). In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline based on the results from the prior stages. Stage 5 will seek the peer-reviewed publication of the guideline, the scoping/systematic review and dissemination.

Ethics and dissemination

The study will follow the principles of the Declaration of Helsinki. The study has been registered in EQUATOR and approved by the University of Florida's Institutional Review Board (#202200495). Informed consent will be obtained from the working groups and the Delphi survey participants. The dissemination of PRECOG and its products will be done through journal publications, conferences, websites and social media.

BACKGROUND: Digital pain assessment is advantageous and timely for healthcare priorities in Turkey. However, a multi-dimensional, tablet-based pain assessment tool is not available in the Turkish language. PURPOSE: To validate the Turkish-PAINReportIt® as a multi-dimensional measure of post-thoracotomy pain. METHODS: In the first of a two-phased study, 32 Turkish patients (mean age 47.8 ± 15.6 years, 72 % male) participated in individual cognitive interviews as they completed the tablet-based Turkish-PAINReportIt® once during the first four days post-thoracotomy, and 8 clinicians participated in a focus group discussion of implementation barriers. In the second phase, 80 Turkish patients (mean age 59.0 ± 12.7 years, 80 % male) completed the Turkish-PAINReportIt® preoperatively, on postoperative days 1-4, and at the two-week post-operative follow-up visit. RESULTS: Patients generally interpreted accurately the Turkish-PAINReportIt® instructions and items. We eliminated some items unnecessary for daily assessment based on focus-group suggestions. In the second study phase, pain scores (intensity, quality, pattern) were low pre-thoracotomy for lung cancer and high postoperatively high on day 1, decreasing on days 2, 3 and 4, and back down to pre-surgical levels at 2-weeks. Over time, pain intensity decreased from post-operative day 1 to post-operative day 4 (p < .001) and from post-operative day 1 to post-operative week 2 (p < .001). CONCLUSIONS: The formative research supported proof of concept and informed the longitudinal study. Findings showed strong validity of the Turkish-PAINReportIt® to detect reduced pain over time as healing occurs after thoracotomy.

PURPOSE: The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units. METHODS: APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis. RESULTS: Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability. CONCLUSIONS: Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Objective

We designed an mHealth application (app) user interface (UI) prototype informed by participatory design sessions, persuasive systems design (PSD) principles, and Lorig and Holman's self-management behavior framework to support self-management activities of Hispanic informal dementia caregivers and assessed their perceptions and preferences regarding features and functions of the app.

Materials and methods

Our observational usability study design employed qualitative methods and forced choice preference assessments to identify: (1) the relationship between user preferences for UI features and functions and PSD principles and (2) user preferences for UI design features and functions and app functionality. We evaluated 16 pairs of mHealth app UI prototype designs. Eight paper-based paired designs were used to assess the relationship between PSD principles and caregiver preferences for UI features and functions to support self-management. An Apple iPad WIFI 32GB was used to display another 8 paired designs and assess caregiver preferences for UI functions to support the self-management process.

Results

Caregivers preferred an app UI with features and functions that incorporated a greater number of PSD principles and included an infographic to facilitate self-management. Moreover, caregivers preferred a design that did not depend on manual data entry, opting instead for functions such as drop-down list, drag-and-drop, and voice query to prioritize, choose, decide, and search when performing self-management activities.

Conclusion

Our assessment approaches allowed us to discern which UI features, functions, and designs caregivers preferred. The targeted application of PSD principles in UI designs holds promise for supporting personalized problem identification, goal setting, decision-making, and action planning as strategies for improving caregiver self-management confidence.

Background and significance

Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations.

Objectives

The goal of this research is to identify end-user (primary care staff and patients) needs through a human-centered design process for a tool that will generate CDS to protect older adults from falls and injuries.

Methods

Primary care staff (primary care providers, care coordinator nurses, licensed practical nurses, and medical assistants) and community-dwelling patients aged 60 years or older associated with Brigham & Women's Hospital-affiliated primary care clinics and the University of Florida Health Archer Family Health Care primary care clinic were eligible to participate in this study. Through semi-structured and exploratory interviews with participants, our team identified end-user needs through content analysis.

Results

User needs for primary care staff (n = 24) and patients (n = 18) were categorized under the following themes: workload burden; systematic communication; in-person assessment of patient condition; personal support networks; motivational tools; patient understanding of fall risk; individualized resources; and evidence-based safe exercises and expert guidance. While some of these themes are specific to either primary care staff or patients, several address needs expressed by both groups of end-users.

Conclusion

Our findings suggest that there are many care gaps in fall prevention management in primary care and that personalized, actionable, and evidence-based CDS has the potential to address some of these gaps.