Background: Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. Methods: In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. Results: A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Conclusions: Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Studies have reported that patient-related factors significantly impact the risk of Chemotherapy-Induced Nausea and Vomiting (CINV). The objective of this study was to analyze those risk factors of CINV through a systematic literature review. Methods: We searched MEDLINE to identify articles that addressed patient-related risk factors of CINV through clinical studies. Results: A total of 49 articles were selected for this study. A total of 28 patient-related risk-factors that significantly impact the risk of CINV were documented. Three factors are demographically related, 17 factors are intrinsic in nature and innate to patient's physiology or influenced by physiology, and eight factors are extrinsic in nature. At least five studies identified seven risk factors with notable summary odds ratio: history of nausea/vomiting (odds ratio: 3.13, 95% CI 2.40–4.07, p < 0.05), female sex (odds ratio: 2.79, 95% CI 2.26–3.44, p < 0.05), expectancy of CINV (odds ratio: 2.61, 95%CI 1.69–4.02, p < 0.05), younger age (odds ratio: 2.59, 95% CI 2.18–3.07, p < 0.05), anxiety (odds ratio: 2.57, 95% CI 1.94–3.40, p < 0.05), history of morning sickness (odds ratio: 1.97, 95% CI 1.46–2.65, p < 0.05), and low alcohol intake (odds ratio: 1.94, 95% CI 1.68–2.24, p < 0.05). Conclusions: Oncologists can use these factors prior to the initiation of a chemotherapy regimen to identify patients at risk for CINV, in order to focus on more comprehensive antiemetic treatment options for those high-risk patients. This may enable better outcomes and avoid complications.

Pain is the most common and distressing symptom for patients in all clinical settings. The dearth of health informatics tools to support acute and chronic pain management may be contributing to the chronic pain and opioid abuse crises. The purpose of this study is to qualitatively evaluate the content and functionality of mobile pain management apps. The Apple App Store and the Google Play Store were searched to identify pain management apps. The inclusion criteria were as follows: (1) that apps include a pain diary function allowing users to record pain episodes, (2) are available in either Apple App Store or Google Play Store, and (3) are available in the English language. We excluded apps if they were limited to only specific forms of pain or specific diseases. A total of 36 apps met the inclusion criteria. Most of the apps served as pain diary tools to record the key characteristics of pain. The pain diary features of the apps were grouped into nine categories: the recordings of pain intensity, pain location, pain quality, pain’s impacts on daily life, other features of pain, other related symptoms, medication, patients’ habits and basic information, and other miscellaneous functions. The apps displayed various problems in use. The problem of not involving healthcare professionals in app development has not been resolved. Approximately 31% of apps including a pain diary function engaged clinicians in app development. Only 19% involved end-users in development and then only in an ad-hoc way. Only one third of the apps supported the cross-platforms, none of the apps supported clinician access to graphical pain data visualization, none secured HIPAA compliance, and none endorsed the PEG tool for primary care physicians’ chronic pain management. Most of the 36 pain management apps demonstrated various problems including user interface and security. Many apps lacked clinician and end-user involvement in app development impacting the clinical utility of these apps. We could not find any pain apps suitable for clinical usage despite high demand from clinicians due to the US opioid crisis.

Background: The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method: We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results: The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions: Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients.