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Patient Education and Counseling Nov 2023The objective was to identify candidate patient reported outcomes with potential to inform individual patient care and service development for inclusion in a digital... (Review)
Review
Which diabetes specific patient reported outcomes should be measured in routine care? A systematic review to inform a core outcome set for adults with Type 1 and 2 diabetes mellitus: The European Health Outcomes Observatory (H2O) programme.
OBJECTIVES
The objective was to identify candidate patient reported outcomes with potential to inform individual patient care and service development for inclusion in a digital outcome set to be collected in routine care, as part of an international project to enhance care outcomes for people with diabetes.
METHODS
PubMed, COSMIN and COMET databases were searched. Published studies were included if they recommended patient reported outcomes that were clinically useful and/or important to people with diabetes. To aid selection decisions, recommended outcomes were considered in terms of the evidence endorsing them and their importance to people with diabetes.
RESULTS
Twenty-seven studies recommending 53 diabetes specific outcomes, and patient reported outcome measures, were included. The outcomes reflected the experience of living with diabetes (e.g. psychological well-being, symptom experience, health beliefs and stigma) and behaviours (e.g. self-management). Diabetes distress and self-management behaviours were most endorsed by the evidence.
CONCLUSIONS
The review provides a comprehensive list of candidate outcomes endorsed by international evidence and informed by existing outcome sets, and suggestions for measures.
PRACTICE IMPLICATIONS
The review offers evidence to guide clinical application. Integrated measurement of these outcomes in care settings holds enormous potential to improve provision of care and outcomes in diabetes.
Topics: Humans; Adult; Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2; Outcome Assessment, Health Care
PubMed: 37672919
DOI: 10.1016/j.pec.2023.107933 -
Journal of Advanced Nursing Feb 2023Editorials are opinion pieces. This piece has not been subject to peer review and the opinions expressed are those of the authors. None of the authors have relevant...
Editorials are opinion pieces. This piece has not been subject to peer review and the opinions expressed are those of the authors. None of the authors have relevant political or other affiliations to declare.
Topics: Humans; Quality of Life; Holistic Health; Outcome Assessment, Health Care; Chronic Disease
PubMed: 36062872
DOI: 10.1111/jan.15433 -
Value in Health : the Journal of the... Dec 2022Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and... (Review)
Review
OBJECTIVES
Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.
METHODS
We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.
RESULTS
We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).
CONCLUSIONS
The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
Topics: Humans; Economics, Medical; Outcome Assessment, Health Care; Machine Learning; Cost-Benefit Analysis; Electronic Health Records
PubMed: 35989154
DOI: 10.1016/j.jval.2022.07.011 -
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 -
Value in Health : the Journal of the... Feb 2023With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR).... (Review)
Review
OBJECTIVES
With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR.
METHODS
We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses.
RESULTS
A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%).
CONCLUSION
There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.
Topics: Humans; Economics, Medical; Outcome Assessment, Health Care; Wearable Electronic Devices; Machine Learning; Mental Health
PubMed: 36115806
DOI: 10.1016/j.jval.2022.08.005 -
Headache Feb 2021Over the last six decades (earliest included publication from 1959), clinical trials of migraine preventive treatments have led to the regulatory approval of many...
BACKGROUND
Over the last six decades (earliest included publication from 1959), clinical trials of migraine preventive treatments have led to the regulatory approval of many medications and devices. Despite similar clinical goals, the outcomes and endpoints used in these trials are broad and not well standardized.
OBJECTIVE
To describe results from a systematic literature review focused on outcomes and endpoints used in preventive migraine clinical trials.
METHOD
A systematic literature review, following a pre-specified (unregistered) protocol developed to adhere to recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, was conducted to characterize the endpoints and outcomes used in preventive migraine clinical trials. Predetermined terms were searched in PubMed on October 28, 2019. Data related to trial design, subject characteristics, outcomes, and endpoints reported in each publication were extracted. Descriptive summaries of these features were tabulated for the recent subset of publications, published during or after 1988, that were randomized, blinded, and focused on pharmacological or device therapies for the preventive treatment of migraine.
RESULTS
The initial literature search identified 1506 publications, of which 757 publications were eligible for data extraction. Of specific clinical interest were the recent subset of 268 articles (268/757, 35.4%) fulfilling the targeted criteria. Results showed that the outcomes used to define endpoints varied substantially across publications. For example, in the recent subset of publications, 68.7% (184/268) of the publications examined ≥1 migraine-specific outcome, 39.6% (106/268) examined ≥1 headache-specific outcome, 50.7% (136/268) examined ≥1 acute/rescue medication use outcome, 40.3% (108/268) examined ≥1 headache-related patient-reported outcome measure (PROM), and 22.0% (59/268) examined ≥1 non-headache-specific PROM. Furthermore, the definition of the endpoints used (e.g., change from baseline, fixed timepoint comparisons, categorization of "responders" to treatment based on wide variety of "responder definitions") also differed across publications.
CONCLUSION
Publications from clinical trials of preventive migraine pharmacologic and device treatments differed in terms of study design, endpoint definitions, and how endpoints and outcomes were measured. Although there were common outcomes and endpoints used across publications, no clear "standardized" set of endpoints and outcomes emerged. The inconsistencies in endpoints and outcomes within this literature suggest that the development of a uniform set of outcomes and endpoints could improve the clinical meaningfulness of clinical trial results, facilitate cross-trial comparisons and better inform patient care. This standard set of outcomes and endpoints should be statistically robust and informed by the priorities of various stakeholders, most importantly, the needs and preferences of people living with migraine.
Topics: Clinical Trials as Topic; Humans; Migraine Disorders; Outcome Assessment, Health Care
PubMed: 33600610
DOI: 10.1111/head.14069 -
Medical Care Dec 2023PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging...
PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.
Topics: Aged; Humans; United States; Prospective Studies; Medicare; Patient Outcome Assessment; Outcome Assessment, Health Care; Patient-Centered Care
PubMed: 37963035
DOI: 10.1097/MLR.0000000000001929 -
The Journal of Foot and Ankle Surgery :... 2022Research demonstrating improved outcomes with third-generation ankle replacement implants has resulted in increasing utilization of total ankle arthroplasty over the...
Research demonstrating improved outcomes with third-generation ankle replacement implants has resulted in increasing utilization of total ankle arthroplasty over the past 3 decades. The purpose of this study was to examine the quality and trends of clinical outcomes research being published on third-generation total ankle arthroplasty implants. Two fellowship-trained foot and ankle surgeons reviewed all peer-reviewed, Medline-indexed English-language clinical outcomes studies evaluating total ankle arthroplasty published between 2006 and 2019. Articles were assessed for study design and indicators of study quality. A total of 694 published articles were reviewed and 231 met all inclusion criteria. The majority (78%) of studies were retrospective, most of which were case series (54%) or cohorts (32%). Ten percent (10%) of studies were funded by industry and 28% did not disclose funding sources. Thirty-eight percent (38%) of studies reported a conflict of interest and 6% did not disclose whether or not there were conflicts. The average patient follow-up time across studies was 72 months. We found that although the study of outcomes with third-generation total ankle arthroplasty prostheses is steadily increasing, most studies are Level IV, retrospective case series. Some studies have disclosed industry funding and/or a conflict of interest, and a considerable number did not disclose potential funding and/or financial conflicts. Future investigators should strive to design studies with the highest quality methodology possible.
Topics: Ankle; Ankle Joint; Arthrodesis; Arthroplasty, Replacement, Ankle; Humans; Outcome Assessment, Health Care; Retrospective Studies; Treatment Outcome
PubMed: 34244049
DOI: 10.1053/j.jfas.2021.05.011 -
BMC Health Services Research Dec 2022Dental diseases have detrimental effects on healthcare systems and societies at large. Providing access to dental care can arguably improve health outcomes, reduce... (Review)
Review
BACKGROUND
Dental diseases have detrimental effects on healthcare systems and societies at large. Providing access to dental care can arguably improve health outcomes, reduce healthcare utilization costs, and improve several societal outcomes.
OBJECTIVES
Our objective was to review the literature to assess the impacts of dental care programs on healthcare and societal outcomes. Specifically, to identify the nature of such programs, including the type of services delivered, who was targeted, where services were delivered, and how access to dental care was enabled. Also, what kind of societal and healthcare outcomes have been attempted to be addressed through these programs were identified.
METHODS
We conducted a scoping review by searching four databases, MEDLINE, EMBASE, CINAHL, and Sociological Abstracts. Relevant articles published in English language from January 2000 to February 2022 were screened by four reviewers to determine eligibility for inclusion.
RESULTS
The search resulted in 29,468 original articles, of which 25 were included in the data synthesis. We found minimal evidence that answers our proposed research question. The majority of identified programs have demonstrated effectiveness in reducing medical and dental healthcare utilization (especially for non-preventive services) and avert more invasive treatments, and to a lesser degree, resulting in cost-savings. Moreover, some promising but limited evidence about program impacts on societal outcomes such as reducing homelessness and improving employability was reported.
CONCLUSION
Despite the well-known societal and economic consequences of dental problem, there is a paucity of studies that address the impacts of dental care programs from the societal and healthcare system perspectives.
MESH TERMS
Delivery of Health Care, Dental Care, Outcome assessment, Patient acceptance of Health Care.
Topics: Humans; Delivery of Health Care; Patient Acceptance of Health Care; Outcome Assessment, Health Care; Dental Care
PubMed: 36564768
DOI: 10.1186/s12913-022-08951-x -
Journal of Psychiatry & Neuroscience :... Jan 2021The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional... (Review)
Review
The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.
Topics: Clinical Trials as Topic; Humans; Mental Disorders; Nervous System Diseases; Outcome Assessment, Health Care; Precision Medicine; Research Design
PubMed: 33206039
DOI: 10.1503/jpn.200042