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American Journal of Surgery Apr 2024
Topics: Humans; Big Data; Electronic Health Records; Delivery of Health Care; Outcome Assessment, Health Care; Health Services Research
PubMed: 38092643
DOI: 10.1016/j.amjsurg.2023.11.026 -
Implementation Science : IS Feb 2022Implementation outcomes research spans an exciting mix of fields, disciplines, and geographical space. Although the number of studies that cite the 2011 taxonomy has...
BACKGROUND
Implementation outcomes research spans an exciting mix of fields, disciplines, and geographical space. Although the number of studies that cite the 2011 taxonomy has expanded considerably, the problem of harmony in describing outcomes persists. This paper revisits that problem by focusing on the clarity of reporting outcomes in studies that examine them. Published recommendations for improved reporting and specification have proven to be an important step in enhancing the rigor of implementation research. We articulate reporting problems in the current implementation outcomes literature and describe six practical recommendations that address them.
RECOMMENDATIONS
Our first recommendation is to clearly state each implementation outcome and provide a definition that the study will consistently use. This includes providing an explanation if using the taxonomy in a new way or merging terms. Our second recommendation is to specify how each implementation outcome will be analyzed relative to other constructs. Our third recommendation is to specify "the thing" that each implementation outcome will be measured in relation to. This is especially important if you are concurrently studying interventions and strategies, or if you are studying interventions and strategies that have multiple components. Our fourth recommendation is to report who will provide data and the level at which data will be collected for each implementation outcome, and to report what kind of data will be collected and used to assess each implementation outcome. Our fifth recommendation is to state the number of time points and frequency at which each outcome will be measured. Our sixth recommendation is to state the unit of observation and the level of analysis for each implementation outcome.
CONCLUSION
This paper advances implementation outcomes research in two ways. First, we illustrate elements of the 2011 research agenda with concrete examples drawn from a wide swath of current literature. Second, we provide six pragmatic recommendations for improved reporting. These recommendations are accompanied by an audit worksheet and a list of exemplar articles that researchers can use when designing, conducting, and assessing implementation outcomes studies.
Topics: Humans; Outcome Assessment, Health Care
PubMed: 35135566
DOI: 10.1186/s13012-021-01183-3 -
Clinical Pharmacology and Therapeutics Apr 2020
Topics: Data Science; Drug Development; Humans; Outcome Assessment, Health Care; Pharmacology, Clinical
PubMed: 32202650
DOI: 10.1002/cpt.1803 -
Pediatrics Jul 2020As rates of neonatal opioid withdrawal are increasing, the need for research to evaluate new treatments is growing. Large heterogeneity exists in health outcomes...
BACKGROUND
As rates of neonatal opioid withdrawal are increasing, the need for research to evaluate new treatments is growing. Large heterogeneity exists in health outcomes reported in current literature. Our objective is to develop an evidence-informed and consensus-based core outcome set in neonatal opioid withdrawal syndrome (NOWS-COS) for use in studies and clinical practice.
METHODS
An international multidisciplinary steering committee was established. A systematic review and a 3-round Delphi was performed with open-ended and score-based assessments of the importance of each outcome to inform clinical management of neonatal opioid withdrawal. Interviews were conducted with parents and/or caregivers on outcome importance. Finally, a consensus meeting with diverse stakeholders was held to review all data from all sources and establish a core set of outcomes with definitions.
RESULTS
The NOWS-COS was informed by 47 published studies, 41 Delphi participants, and 6 parent interviews. There were 63 outcomes evaluated. Final core outcomes include (1) pharmacologic treatment, (2) total dose of opioid treatment, (3) duration of treatment, (4) adjuvant therapy, (5) feeding difficulties, (6) consolability, (7) time to adequate symptom control, (8) parent-infant bonding, (9) duration of time the neonate spent in the hospital, (10) breastfeeding, (11) weight gain at hospital discharge, (12) readmission to hospital for withdrawal, and (13) neurodevelopment.
CONCLUSIONS
We developed an evidence-informed and consensus-based core outcome set. Implementation of this core outcome set will reduce heterogeneity between studies and facilitate evidence-based decision-making. Future research will disseminate all the findings and pilot test the validity of the NOWS-COS in additional countries and populations to increase generalizability and impact.
Topics: Delphi Technique; Humans; Infant, Newborn; Neonatal Abstinence Syndrome; Outcome Assessment, Health Care; Treatment Outcome
PubMed: 32493710
DOI: 10.1542/peds.2020-0018 -
Archives of Dermatological Research Jun 2024Steven Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), grouped together under the terminology of epidermal necrolysis (EN), are a spectrum of... (Review)
Review
Steven Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), grouped together under the terminology of epidermal necrolysis (EN), are a spectrum of life-threatening dermatologic conditions. A lack of standardization and validation for existing endpoints has been identified as a key barrier to the comparison of these therapies and development of evidenced-based treatment. Following PRISMA guidelines, we conducted a systematic review of prospective studies involving systemic or topical treatments for EN, including dressing and ocular treatments. Outcomes were separated into mortality assessment, cutaneous outcomes, non-cutaneous clinical outcomes, and mucosal outcomes. The COSMIN Risk of Bias tool was used to assess the quality of studies on reliability and measurement error of outcome measurement instruments. Outcomes across studies assessing treatment in the acute phase of EN were varied. Most data came from prospective case reports and cohort studies representing the lack of available randomized clinical trial data available in EN. Our search did not reveal any EN-specific validated measures or scoring tools used to assess disease progression and outcomes. Less than half of included studies were considered "adequate" for COSMIN risk of bias in reliability and measurement error of outcome measurement instruments. With little consensus about management and treatment of EN, consistency and validation of measured outcomes is of the upmost importance for future studies to compare outcomes across treatments and identify the most effective means of combating the disease with the highest mortality managed by dermatologists.
Topics: Humans; Stevens-Johnson Syndrome; Reproducibility of Results; Outcome Assessment, Health Care; Treatment Outcome; Bandages
PubMed: 38878166
DOI: 10.1007/s00403-024-03062-5 -
BMC Geriatrics Jun 2024The aging population is a challenge for the healthcare system that must identify strategies that meet their needs. Practicing patient-centered care has been shown... (Review)
Review
INTRODUCTION
The aging population is a challenge for the healthcare system that must identify strategies that meet their needs. Practicing patient-centered care has been shown beneficial for this patient-group. The effect of patient-centered care is called patient-centered outcomes and can be appraised using outcomes measurements.
OBJECTIVES
The main aim was to review and map existing knowledge related to patient-centered outcomes and patient-centered outcomes measurements for older people, as well as identify key-concepts and knowledge-gaps. The research questions were: How can patient-centered outcomes for older people be measured, and which patient-centered outcomes matters the most for the older people?
STUDY DESIGN
Scoping review.
METHODS
Search for relevant publications in electronical databases, grey literature databases and websites from year 2000 to 2021. Two reviewers independently screened titles and abstracts, followed by full text review and extraction of data using a data extraction framework.
RESULTS
Eighteen studies were included, of which six with involvement of patients and/or experts in the process on determine the outcomes. Outcomes that matter the most to older people was interpreted as: access to- and experience of care, autonomy and control, cognition, daily living, emotional health, falls, general health, medications, overall survival, pain, participation in decision making, physical function, physical health, place of death, social role function, symptom burden, and time spent in hospital. The most frequently mentioned/used outcomes measurements tools were the Adult Social Care Outcomes Toolkit (ASCOT), EQ-5D, Gait Speed, Katz- ADL index, Patient Health Questionnaire (PHQ9), SF/RAND-36 and 4-Item Screening Zarit Burden Interview.
CONCLUSIONS
Few studies have investigated the older people's opinion of what matters the most to them, which forms a knowledge-gap in the field. Future research should focus on providing older people a stronger voice in what they think matters the most to them.
Topics: Humans; Patient-Centered Care; Aged; Outcome Assessment, Health Care; Patient Outcome Assessment
PubMed: 38890618
DOI: 10.1186/s12877-024-05134-7 -
Annual Review of Public Health Apr 2020Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our...
Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.
Topics: Epidemiologic Methods; Epidemiologic Research Design; Humans; Machine Learning; Outcome Assessment, Health Care
PubMed: 31577910
DOI: 10.1146/annurev-publhealth-040119-094437 -
Current Medical Research and Opinion Sep 2022To provide recommendations for overcoming the challenges associated with the generation and use of real-world evidence (RWE) in regulatory approvals, health technology... (Review)
Review
OBJECTIVE
To provide recommendations for overcoming the challenges associated with the generation and use of real-world evidence (RWE) in regulatory approvals, health technology assessments (HTAs), and reimbursement decision-making in East Asia.
METHODS
A panel of experts convened at the International Society for Pharmacoeconomics and Outcomes Research Asia Pacific 2020 congress to discuss the challenges limiting the use of RWE in healthcare decision-making and to provide insights into the perspectives of regulators, HTA agencies, the pharmaceutical industry, and physicians in China, Japan, and Taiwan. A nonsystematic literature review was conducted to expand on the themes addressed.
RESULTS
The use of RWE in regulatory approvals, HTAs, and reimbursement decision-making remains limited by legal/regulatory, technical, and attitudinal challenges in East Asia.
CONCLUSIONS
We recommend approaches and initiatives that aim to drive improvements in the utilization of RWE in healthcare decision-making in East Asia and other regions. We encourage large-scale collaborations that leverage the full range of skills offered by different stakeholders. Government agencies, hospitals, research organizations, patient groups, and the pharmaceutical industry must collaborate to ensure appropriate access to robust and reliable real-world data and seek alignment on how to address prioritized evidence needs. Increasingly, we believe that this work will be conducted by multidisciplinary teams with expertise in healthcare research and delivery, data science, and information technology. We hope this work will encourage further discussion among all stakeholders seeking to shape the RWE landscape in East Asia and other regions and drive next-generation healthcare.
Topics: Decision Making; Delivery of Health Care; Drug Industry; Humans; Outcome Assessment, Health Care; Technology Assessment, Biomedical
PubMed: 35786170
DOI: 10.1080/03007995.2022.2096354 -
British Journal of Anaesthesia Dec 2023Standardised and universal perioperative endpoint reporting are the cornerstone for outcomes assessment, reliable clinical trials, and health services research. The...
Standardised and universal perioperative endpoint reporting are the cornerstone for outcomes assessment, reliable clinical trials, and health services research. The Outcome4medicine initiative recently reported consensus recommendations on how to assess the quality of surgical interventions, proposing a framework for surgical outcome assessment and quality improvement after medical interventions. In the same field, the Standardised Endpoints in Perioperative Medicine - Core Outcome Measures for Perioperative and Anaesthetic Care (StEP-COMPAC) group recently proposed standardised and valid measures of mortality and morbidity, derived from a three-stage Delphi process. Here a core group of the Outcome4medicine conference discusses how these two initiatives are aligned and emphasises the importance of standardised outcome assessment by integrating the perspectives of different stakeholders.
Topics: Humans; Perioperative Care; Outcome Assessment, Health Care; Quality Improvement; Delphi Technique; Treatment Outcome; Research Design
PubMed: 37879999
DOI: 10.1016/j.bja.2023.09.014 -
Value in Health : the Journal of the... Jul 2022Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in...
Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.
Topics: Artificial Intelligence; Checklist; Economics, Medical; Humans; Machine Learning; Outcome Assessment, Health Care
PubMed: 35779937
DOI: 10.1016/j.jval.2022.03.022