Landscape Analysis of Oncology Mobile Health Applications

2021 ◽  
pp. 579-587
Author(s):  
Vivek A. Upadhyay ◽  
Adam B. Landman ◽  
Michael J. Hassett
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Mobile Health ◽  
Health Care Professionals ◽  
English Language ◽  
Mobile Apps ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Intended Audience

PURPOSE More than 325,000 mobile health (mhealth) applications (apps) have been developed. We sought to describe the state of oncology-specific apps and to highlight areas of strength and opportunities for future development. METHODS We searched for oncology apps in the Apple iOS and Google Play app stores in January 2020. Apps were classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type. RESULTS We identified 794 oncology-specific, English language applications; only 257 (32%) met basic recency standards and were considered evaluable. Of evaluable apps, almost half (47%) were found in the Medical Store Category and the majority were free (88%). The most common intended audience was health care professionals (45%), with 28% being geared toward the general public and 27% being intended for patients. The intended function was education for 36%, clinical decision support for 19.5%, and patient support for 18%. Only 23% of education apps and 40% of clinical decision support apps reported any formal app content review process. Web developers created 61.5% of apps, scientific societies created 10%, and hospitals or health care organizations created just 6%. Of 54 studies that used mobile apps in oncology identified by a recent meta-analysis, only two could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination. CONCLUSION Relatively few oncology-related apps exist in the commercial marketplace, up-to-date apps are uncommon, and there is a notable absence of key oncology stakeholders in app development. Meaningful development opportunities exist.

Mobile health applications in oncology.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14115-e14115 ◽  
Author(s):  
Vivek Upadhyay ◽  
Adam Landman ◽  
Michael J. Hassett
Keyword(s):  
Mobile Health ◽  
English Language ◽  
Comprehensive Evaluation ◽  
Care Delivery ◽  
Mobile Apps ◽  
Digital Health ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Healthcare Organizations ◽  
Intended Audience

e14115 Background: Over 325,000 mobile health (mhealth) applications (apps) have been developed. There has been a substantial increase in mhealth investment, with over $8.1 billion invested in digital health startups in 2018. While apps have been studied within clinical oncology, we are aware of no comprehensive evaluation of the commercial footprint of oncology-specific apps. We sought to describe the state of oncology-specific apps and highlight notable areas of development. Methods: We conducted a systemic search for oncology apps in the Apple iOS and Google Play app stores in January 2020. Search terms included “cancer,” “oncology,” “radiotherapy,” and “chemotherapy.” All apps were manually reviewed and classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type. We also compared commercially available apps with those described in a recently conducted meta-analysis of oncology-app studies. We performed descriptive statistics using RStudio V1.2.335. Results: We identified 794 oncology-specific, English-language applications, but only 257 (32%) met basic quality standards and were considered evaluable. The primary reason for exclusion was lack of a recent update. Of included apps, almost half (47%) were found in the “Medical” Store Category and the majority were free (88%). The most common intended audience was healthcare professionals (45%), with 28% being geared towards the general public and 27% being intended for patients. The intended function was education for 37%, clinical decision support (CDS) for 19%, and patient support for 18%. Only 22% of education apps and 40% of CDS apps reported any formal app content review process. Web developers created 61% of apps, scientific societies created 10%, and hospitals/healthcare organizations created just 6% (Table). The most frequently downloaded apps tended to be geared toward educating/supporting the public. Of 54 studies that utilized mobile apps in oncology identified by a recent meta-analysis, only 2 could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination. Conclusions: Our analysis of oncology-related apps in the commercial marketplace found few high-quality, up-to-date apps, and a notable absence of key oncology stakeholders in app development. Future studies should explore barriers to developing and disseminating apps designed to advance oncology care delivery. [Table: see text]

scholarly journals The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation (Preprint)

2020 ◽  
Author(s):  
Maria Beatriz Walter Costa ◽  
Mark Wernsdorfer ◽  
Alexander Kehrer ◽  
Markus Voigt ◽  
Carina Cundius ◽  
...  
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Clinical Decision ◽  
Laboratory Diagnostics ◽  
Technical Requirements ◽  
Wide Range ◽  
Functional Components

BACKGROUND Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. OBJECTIVE With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. METHODS Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. RESULTS We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. CONCLUSIONS AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

scholarly journals Models Used in Clinical Decision Support Systems Supporting Healthcare Professionals Treating Chronic Wounds: Systematic Literature Review (Preprint)

2017 ◽  
Author(s):  
Clara Schaarup ◽  
Louise Bilenberg Pape-Haugaard ◽  
Ole Kristian Hejlesen
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Literature Review ◽  
Clinical Decision Support ◽  
Systematic Literature Review ◽  
Health Care Professionals ◽  
Chronic Wounds ◽  
Wound Care ◽  
Clinical Decision ◽  
Decision Support Models

BACKGROUND Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds. OBJECTIVE The aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview. METHODS A systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models. RESULTS The final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015. CONCLUSIONS The clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.

scholarly journals Mobile Apps to Improve Medication Adherence in Cardiovascular Disease: Systematic Review and Meta-analysis

10.2196/24190 ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. e24190
Author(s):  
Shahd Al-Arkee ◽  
Julie Mason ◽  
Deirdre A Lane ◽  
Larissa Fabritz ◽  
Winnie Chua ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Medication Adherence ◽  
Clinical Outcomes ◽  
Mobile Health ◽  
Health Care Professionals ◽  
Mobile Apps ◽  
Meta Analysis ◽  
Improve Medication Adherence

Background Adherence rates of preventative medication for cardiovascular disease (CVD) have been reported as 57%, and approximately 9% of all CVD events in Europe are attributable to poor medication adherence. Mobile health technologies, particularly mobile apps, have the potential to improve medication adherence and clinical outcomes. Objective The objective of this study is to assess the effects of mobile health care apps on medication adherence and health-related outcomes in patients with CVD. This study also evaluates apps’ functionality and usability and the involvement of health care professionals in their use. Methods Electronic databases (MEDLINE [Ovid], PubMed Central, Cochrane Library, CINAHL Plus, PsycINFO [Ovid], Embase [Ovid], and Google Scholar) were searched for randomized controlled trials (RCTs) to investigate app-based interventions aimed at improving medication adherence in patients with CVD. RCTs published in English from inception to January 2020 were reviewed. The Cochrane risk of bias tool was used to assess the included studies. Meta-analysis was performed for clinical outcomes and medication adherence, with meta-regression analysis used to evaluate the impact of app intervention duration on medication adherence. Results This study included 16 RCTs published within the last 6 years. In total, 12 RCTs reported medication adherence as the primary outcome, which is the most commonly self-reported adherence. The duration of the interventions ranged from 1 to 12 months, and sample sizes ranged from 24 to 412. Medication adherence rates showed statistically significant improvements in 9 RCTs when compared with the control, and meta-analysis of the 6 RCTs reporting continuous data showed a significant overall effect in favor of the app intervention (mean difference 0.90, 95% CI 0.03-1.78) with a high statistical heterogeneity (I2=93.32%). Moreover, 9 RCTs assessed clinical outcomes and reported an improvement in systolic blood pressure, diastolic blood pressure, total cholesterol, and low-density lipoprotein cholesterol levels in the intervention arm. Meta-analysis of these clinical outcomes from 6 RCTs favored app interventions, but none were significant. In the 7 trials evaluating app usability, all were found to be acceptable. There was a great variation in the app characteristics. A total of 10 RCTs involved health care professionals, mainly physicians and nurses, in the app-based interventions. The apps had mixed functionality: 2 used education, 7 delivered reminders, and 7 provided reminders in combination with educational support. Conclusions Apps tended to increase medication adherence, but interventions varied widely in design, content, and delivery. Apps have an acceptable degree of usability; yet the app characteristics conferring usability and effectiveness are ill-defined. Future large-scale studies should focus on identifying the essential active components of successful apps. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42019121385; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=121385

scholarly journals A Human(e) Factor in Clinical Decision Support Systems (Preprint)

2018 ◽  
Author(s):  
Tim Bezemer ◽  
Mark C H de Groot ◽  
Enja Blasse ◽  
Maarten J ten Berg ◽  
Teus H Kappen ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Expert Knowledge ◽  
Clinical Decision ◽  
Added Value ◽  
Support Design ◽  
Design Data

UNSTRUCTURED The overwhelming amount, production speed, multidimensionality, and potential value of data currently available—often simplified and referred to as big data —exceed the limits of understanding of the human brain. At the same time, developments in data analytics and computational power provide the opportunity to obtain new insights and transfer data-provided added value to clinical practice in real time. What is the role of the health care professional in collaboration with the data scientist in the changing landscape of modern care? We discuss how health care professionals should provide expert knowledge in each of the stages of clinical decision support design: data level, algorithm level, and decision support level. Including various ethical considerations, we advocate for health care professionals to responsibly initiate and guide interprofessional teams, including patients, and embrace novel analytic technologies to translate big data into patient benefit driven by human(e) values.

scholarly journals The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation

10.2196/20407 ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. e20407
Author(s):  
Maria Beatriz Walter Costa ◽  
Mark Wernsdorfer ◽  
Alexander Kehrer ◽  
Markus Voigt ◽  
Carina Cundius ◽  
...  
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Clinical Decision ◽  
Laboratory Diagnostics ◽  
Technical Requirements ◽  
Wide Range ◽  
Functional Components

Background Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. Objective With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. Methods Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. Results We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. Conclusions AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

scholarly journals Mobile Apps to Improve Medication Adherence in Cardiovascular Disease: Systematic Review and Meta-analysis (Preprint)

2020 ◽  
Author(s):  
Shahd Al-Arkee ◽  
Julie Mason ◽  
Deirdre A Lane ◽  
Larissa Fabritz ◽  
Winnie Chua ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Medication Adherence ◽  
Clinical Outcomes ◽  
Mobile Health ◽  
Health Care Professionals ◽  
Mobile Apps ◽  
Meta Analysis ◽  
Improve Medication Adherence

BACKGROUND Adherence rates of preventative medication for cardiovascular disease (CVD) have been reported as 57%, and approximately 9% of all CVD events in Europe are attributable to poor medication adherence. Mobile health technologies, particularly mobile apps, have the potential to improve medication adherence and clinical outcomes. OBJECTIVE The objective of this study is to assess the effects of mobile health care apps on medication adherence and health-related outcomes in patients with CVD. This study also evaluates apps’ functionality and usability and the involvement of health care professionals in their use. METHODS Electronic databases (MEDLINE [Ovid], PubMed Central, Cochrane Library, CINAHL Plus, PsycINFO [Ovid], Embase [Ovid], and Google Scholar) were searched for randomized controlled trials (RCTs) to investigate app-based interventions aimed at improving medication adherence in patients with CVD. RCTs published in English from inception to January 2020 were reviewed. The Cochrane risk of bias tool was used to assess the included studies. Meta-analysis was performed for clinical outcomes and medication adherence, with meta-regression analysis used to evaluate the impact of app intervention duration on medication adherence. RESULTS This study included 16 RCTs published within the last 6 years. In total, 12 RCTs reported medication adherence as the primary outcome, which is the most commonly self-reported adherence. The duration of the interventions ranged from 1 to 12 months, and sample sizes ranged from 24 to 412. Medication adherence rates showed statistically significant improvements in 9 RCTs when compared with the control, and meta-analysis of the 6 RCTs reporting continuous data showed a significant overall effect in favor of the app intervention (mean difference 0.90, 95% CI 0.03-1.78) with a high statistical heterogeneity (I<sup>2</sup>=93.32%). Moreover, 9 RCTs assessed clinical outcomes and reported an improvement in systolic blood pressure, diastolic blood pressure, total cholesterol, and low-density lipoprotein cholesterol levels in the intervention arm. Meta-analysis of these clinical outcomes from 6 RCTs favored app interventions, but none were significant. In the 7 trials evaluating app usability, all were found to be acceptable. There was a great variation in the app characteristics. A total of 10 RCTs involved health care professionals, mainly physicians and nurses, in the app-based interventions. The apps had mixed functionality: 2 used education, 7 delivered reminders, and 7 provided reminders in combination with educational support. CONCLUSIONS Apps tended to increase medication adherence, but interventions varied widely in design, content, and delivery. Apps have an acceptable degree of usability; yet the app characteristics conferring usability and effectiveness are ill-defined. Future large-scale studies should focus on identifying the essential active components of successful apps. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42019121385; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=121385

scholarly journals The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sharare Taheri Moghadam ◽  
Farahnaz Sadoughi ◽  
Farnia Velayati ◽  
Seyed Jafar Ehsanzadeh ◽  
Shayan Poursharif
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Physician Practice ◽  
Positive Effects ◽  
Practice Performance

Abstract Background Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity. Results On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Red Book Point-of-Care Clinical Decision Support Tool

2018 ◽  
Keyword(s):  
Decision Support ◽  
Infectious Diseases ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Point Of Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Pediatric Health Care ◽  
Flip Chart

This convenient flip chart provides pediatric health care professionals with point-of-care guidance on the assessment, prevention, and treatment of childhood infectious diseases. https://shop.aap.org/red-book-pediatric-infectious-diseases-clinical-decision-support-chart/

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