Health measurement scales

Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.

Big data and machine learning algorithms for health-care delivery

Objective:The aim of this review was to explore the current evidence for conversational agents or chatbots in the field of psychiatry and their role in screening, diagnosis, and treatment of mental...

https://journals.sagepub.com/doi/pdf/10.1177/0706743719828977

Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape:

Social isolation and loneliness are serious yet underappreciated public health risks that affect a significant portion of the older adult population. Approximately one-quarter of community-dwelling Americans aged 65 and older are considered to be socially isolated, and a significant proportion of adults in the United States report feeling lonely. People who are 50 years of age or older are more likely to experience many of the risk factors that can cause or exacerbate social isolation or loneliness, such as living alone, the loss of family or friends, chronic illness, and sensory impairments. Over a life course, social isolation and loneliness may be episodic or chronic, depending upon an individual's circumstances and perceptions. A substantial body of evidence demonstrates that social isolation presents a major risk for premature mortality, comparable to other risk factors such as high blood pressure, smoking, or obesity. As older adults are particularly high-volume and high-frequency users of the health care system, there is an opportunity for health care professionals to identify, prevent, and mitigate the adverse health impacts of social isolation and loneliness in older adults. Social Isolation and Loneliness in Older Adults summarizes the evidence base and explores how social isolation and loneliness affect health and quality of life in adults aged 50 and older, particularly among low income, underserved, and vulnerable populations. This report makes recommendations specifically for clinical settings of health care to identify those who suffer the resultant negative health impacts of social isolation and loneliness and target interventions to improve their social conditions. Social Isolation and Loneliness in Older Adults considers clinical tools and methodologies, better education and training for the health care workforce, and dissemination and implementation that will be important for translating research into practice, especially as the evidence base for effective interventions continues to flourish.

Social Isolation and Loneliness in Older Adults: Opportunities for the Health Care System

Background: Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.
Objective: This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.
Methods: We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
Results: The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.
Conclusions: The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

/pdf/conversational-agents-in-health-care-scoping-review-and-vmy2djyc16.pdf

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis.

Conversational agents in healthcare: a systematic review.

Studies measuring the completeness and consistency of trial registration and reporting rely on linking registries with bibliographic databases. In this systematic review, we quantified the processes used to identify these links. PubMed and Embase databases were searched from inception to May 2016 for studies linking trial registries with bibliographic databases. The processes used to establish these links were categorised as automatic when the registration identifier was available in the bibliographic database or publication, or manual when linkage required inference or contacting of trial investigators. The number of links identified by each process was extracted where available. Linear regression was used to determine whether the proportions of links available via automatic processes had increased over time. In 43 studies that examined cohorts of registry entries, 24 used automatic and manual processes to find articles; 3 only automatic; and 11 only manual (5 did not specify). Twelve studies reported results for both manual and automatic processes and showed that a median of 23% (range from 13 to 42%) included automatic links to articles, while 17% (range from 5 to 42%) of registry entries required manual processes to find articles. There was no evidence that the proportion of registry entries with automatic links had increased (R
 2 = 0.02, p = 0.36). In 39 studies that examined cohorts of articles, 21 used automatic and manual processes; 9 only automatic; and 2 only manual (7 did not specify). Sixteen studies reported numbers for automatic and manual processes and indicated that a median of 49% (range from 8 to 97%) of articles had automatic links to registry entries, and 10% (range from 0 to 28%) required manual processes to find registry entries. There was no evidence that the proportion of articles with automatic links to registry entries had increased (R
 2 = 0.01, p = 0.73). The linkage of trial registries to their corresponding publications continues to require extensive manual processes. We did not find that the use of automatic linkage has increased over time. Further investigation is needed to inform approaches that will ensure publications are properly linked to trial registrations, thus enabling efficient monitoring of trial reporting.

/pdf/a-systematic-review-of-the-processes-used-to-link-clinical-kv9kclz305.pdf

A systematic review of the processes used to link clinical trial registrations to their published results

A number of methods for deciding when a systematic review should be updated have been proposed, yet little is known about whether systematic reviews are updated more quickly when new evidence becomes available. Our aim was to examine the timing of systematic review updates relative to the availability of new evidence. We performed a retrospective analysis of the update timing of systematic reviews published in the Cochrane Database of Systematic Reviews in 2010 relative to the availability of new trial evidence. We compared the update timing of systematic reviews with and without signals defined by the completion or publication of studies that were included in the updates. We found 43% (293/682) systematic reviews were updated before June 2017, of which 204 included an updated primary outcome meta-analysis (median update time 35.4 months; IQR 25.5–54.0), 38% (77/204) added new trials, and 4% (8/204) reported a change in conclusion. In the 171 systematic reviews with reconcilable trial reporting information, we did not find a clear difference in update timing (p = 0.05) between the 15 systematic reviews with a publication signal (median 25.3 months; IQR 15.3–43.5) and the 156 systematic reviews without a publication signal (median 34.4 months; IQR 25.1–52.2). In the 145 systematic reviews with reconcilable trial completion information, we did not find a difference in update timing (p = 0.33) between the 15 systematic reviews with a trial completion signal (median 26.0 months; IQR 19.3–49.5) and the 130 systematic reviews without a trial completion signal (median 32.4 months; IQR 24.1 to 46.0). A minority of 2010 Cochrane reviews were updated before June 2017 to incorporate evidence from new primary studies, and very few updates led to a change in conclusion. We did not find clear evidence that updates were undertaken faster when new evidence was made available. New approaches for finding early signals that a systematic review conclusion is at risk of change may be useful in allocated resources to the updating of systematic reviews.

https://link.springer.com/content/pdf/10.1186/s13643-018-0856-9.pdf

Time-to-update of systematic reviews relative to the availability of new evidence

Objectives Systematic reviews of clinical trials could be updated faster by automatically monitoring relevant trials as they are registered, completed, and reported. Our aim was to provide a public interface to a database of curated links between systematic reviews and trial registrations. Materials and Methods We developed the server-side system components in Python, connected them to a PostgreSQL database, and implemented the web-based user interface using Javascript, HTML, and CSS. All code is available on GitHub under an open source MIT license and registered users can access and download all available data. Results The trial2rev system is a web-based interface to a database that collates and augments information from multiple sources including bibliographic databases, the ClinicalTrials.gov registry, and the actions of registered users. Users interact with the system by browsing, searching, or adding systematic reviews, verifying links to trials included in the review, and adding or voting on trials that they would expect to include in an update of the systematic review. The system can trigger the actions of software agents that add or vote on included and relevant trials, in response to user interactions or by scheduling updates from external resources. Discussion and Conclusion We designed a publicly-accessible resource to help systematic reviewers make decisions about systematic review updates. Where previous approaches have sought to reactively filter published reports of trials for inclusion in systematic reviews, our approach is to proactively monitor for relevant trials as they are registered and completed.

Trial2rev: Combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews.

Abstract The recent covid 19 has increased the challenges for worldwide businesses to manage working capital. Compared to the studies on the financial crisis of 2008, management of working capital and firm performance relation during the covid 19 is less studied, particularly in developing countries. Therefore, this study examined the working capital management and firm performance relation in 577 firms from three Asian developing countries from 2004 to 2020. The working capital measurement includes working capital investment policy, working capital financing policy, cash conversion cycle (CCC), and net working capital (NWC). Firm performance is measured by return on assets (ROA) and Tobin’s Q (TQ). To examine the working capital management and firm performance during the crisis 2008 and covid 19, Kruskal-Wallis test is used. Results revealed that working capital management and firm performance were more affected during covid 19 than crisis 2008 period. In addition, this study compared the working capital management and firm performance relation for covid 19 and crisis 2008 using the dynamic panel system generalized method of moments (GMM). Results showed the difference in the effect of working capital management on firm performance during the covid 19 period as compared to the crisis 2008 period. CATAR significantly and negatively influenced ROA but significantly and positively influenced TQ. In contrast, CLTAR and CCC significantly and positively influenced ROA but significantly and negatively influenced TQ. NWC significantly and positively influenced ROA only. To the best of our knowledge, this study is the first empirical research study to extend cross-country analysis in respect of non-financial firms to the developing countries’ context. The results of this study provide important managerial implications for firms. The different results for different firm performance proxies imply that firm managers must adopt the working capital policies which are profitable for firms and shareholders. Thus, firms in developing countries would be able to optimize their working capital according to the economic conditions.

https://www.tandfonline.com/doi/pdf/10.1080/23322039.2022.2101224?needAccess=true

Rabia Bashir

Papers

Conversational agents in healthcare: a systematic review.

A systematic review of the processes used to link clinical trial registrations to their published results

Time-to-update of systematic reviews relative to the availability of new evidence

Trial2rev: Combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews.

Working capital management and firm performance: are their effects same in covid 19 compared to financial crisis 2008?