scholarly journals Best Paper Selection

2021 ◽  
Vol 30 (01) ◽  
pp. 089-090

Powell KR, Deroche CB, Alexander GL. Health Data Sharing in US Nursing Homes: A Mixed Methods Study. https://www.jamda.com/article/S1525-8610(20)30197-3/fulltext Cappetta K, Lago L, Potter J, Phillipson L. Under-coding of dementia and other conditions indicates scope for improved patient management: A longitudinal retrospective study of dementia patients in Australia. https://journals.sagepub.com/doi/abs/10.1177/1833358319897928?journalCode=himd Sheriffdeen A, Millar JL, Martin C, Evans M, Tikellis G, Evans SM. (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports. https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-020-05713-5

10.2196/18123 ◽  
2020 ◽  
Vol 4 (8) ◽  
pp. e18123
Author(s):  
Danny T Y Wu ◽  
Chen Xin ◽  
Shwetha Bindhu ◽  
Catherine Xu ◽  
Jyoti Sachdeva ◽  
...  

Background Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown. Objective A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD? Methods The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews. Results The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing. Conclusions This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.


BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e025614 ◽  
Author(s):  
Jonas Czwikla ◽  
Maike Schulz ◽  
Franziska Heinze ◽  
Thomas Kalwitzki ◽  
Daniel Gand ◽  
...  

IntroductionNursing home residents typically have greater needs for medical care than community-dwelling elderly. However, restricted cognitive abilities and limited mobility may impede their access to general practitioners and medical specialists. The provision of medical care in nursing homes may therefore be inappropriate in some areas of medical care. The purpose of this mixed-methods study is to systematically assess, evaluate and explain met and unmet medical care needs in German nursing homes and to develop solutions where medical care is found to be inappropriate.Methods and analysisFirst, statutory health insurance claims data are analysed to identify differences in the utilisation of medical care between nursing home residents and community-dwelling elderly with and without need for long-term care. Second, the health status and medical care of 500 nursing home residents are assessed and evaluated to quantify met and unmet medical care needs. Third, qualitative expert interviews and case conferences and, fourth, quantitative analyses of linked data are used to provide structural, case-specific and generalisable explanations of inappropriate medical care among nursing home residents. Fifth, a modified Delphi study is employed to develop pilot projects aiming to improve medical care in nursing homes.Ethics and disseminationThis study was approved by the Ethics Committee of the University of Bremen on 23 November 2017. Research findings are disseminated through presentations at national and international conferences and publications in peer-reviewed scientific journals.Trial registration numberDRKS00012383.


JMIR Aging ◽  
10.2196/29788 ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. e29788
Author(s):  
Ben Kim ◽  
Peyman Ghasemi ◽  
Paul Stolee ◽  
Joon Lee

Background Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults. Objective This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults. Methods A mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics. Results Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care. Conclusions This embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults.


2015 ◽  
Vol 5 (Suppl 2) ◽  
pp. A10.2-A10
Author(s):  
Tryge Johannes Lereim Sævareid ◽  
L Lillemoen ◽  
Lisbeth Thoresen ◽  
E Gjerberg ◽  
R Førde ◽  
...  

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