1,909 results match your criteria BMC medical informatics and decision making[Journal]


Facilitating the design of HL7 domain models through a model-driven solution.

BMC Med Inform Decis Mak 2020 May 25;20(1):96. Epub 2020 May 25.

Group of Research and Innovation in Biomedical Computing, Biomedical Engineering and Health Economics, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain.

Background And Goal: Health information systems are increasingly sophisticated and developing them is a challenge for software developers. Software engineers usually make use of UML as a standard model language that allows defining health information system entities and their relations. However, working with health system requires learning HL7 standards, that defines and manages standards related to health information systems. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1093-4DOI Listing

A proof-of-concept framework for the preference elicitation and evaluation of health informatics technologies: the online PRESENT patient experience dashboard as a case example.

BMC Med Inform Decis Mak 2020 May 24;20(1):95. Epub 2020 May 24.

Social Science Research Unit, Department of Social Science, University College London (UCL), 18 Woburn Square, London, WC1H 0NR, UK.

Background: Constrained budgets within healthcare systems and the need to efficiently allocate resources often necessitate the valuation of healthcare interventions and services. However, when a technological product is developed for which no market exists it is a challenge to understand how to place the product and which specifications are the most sought after and important for end users. This was the case for a dashboard we developed, displaying analyses of patient experience survey free-text comments. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1098-zDOI Listing

Implementation and comparison of two text mining methods with a standard pharmacovigilance method for signal detection of medication errors.

BMC Med Inform Decis Mak 2020 May 24;20(1):94. Epub 2020 May 24.

Department of Safety Surveillance, Global Safety, Novo Nordisk A/S, Bagsværd, Denmark.

Background: Medication errors have been identified as the most common preventable cause of adverse events. The lack of granularity in medication error terminology has led pharmacovigilance experts to rely on information in individual case safety reports' (ICSRs) codes and narratives for signal detection, which is both time consuming and labour intensive. Thus, there is a need for complementary methods for the detection of medication errors from ICSRs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1097-0DOI Listing

Correction to: Implementation of machine learning algorithms to create diabetic patient re-admission profiles.

BMC Med Inform Decis Mak 2020 May 18;20(1):93. Epub 2020 May 18.

Department of Computer Science, Al-Maarif University College, Anbar, The city of Ramadi, 31001, Iraq.

An amendment to this paper has been published and can be accessed via the original article. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1102-7DOI Listing

Preparing for an orthopedic consultation using an eHealth tool: a randomized controlled trial in patients with hip and knee osteoarthritis.

BMC Med Inform Decis Mak 2020 May 15;20(1):92. Epub 2020 May 15.

Department of Rheumatology, Sint Maartenskliniek, PO Box 9011, Nijmegen, GM, 6500, The Netherlands.

Background: To evaluate the effect of a stand-alone mobile and web-based educational intervention (eHealth tool) compared to usual preparation of a first orthopedic consultation of patients with hip or knee osteoarthritis (OA) on patients' satisfaction.

Methods: A two-armed randomized controlled trial involving 286 patients with (suspicion of) hip or knee OA, randomly allocated to either receiving an educational eHealth tool to prepare their upcoming consultation (n = 144) or usual care (n = 142). Satisfaction with the consultation on three subscales (range 1-4) of the Consumer Quality Index (CQI - primary outcome) and knowledge (assessed using 22 statements on OA, range 0-22), treatment beliefs (assessed by the Treatment beliefs in OsteoArthritis questionnaire, range 1-5), assessment of patient's involvement in consultation by the surgeon (assessed on a 5-point Likert scale) and patient satisfaction with the outcome of the consultation (numeric rating scale), were assessed. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-01130-0DOI Listing

The role of text messaging intervention in Inner Mongolia among patients with type 2 diabetes mellitus: a randomized controlled trial.

BMC Med Inform Decis Mak 2020 May 14;20(1):90. Epub 2020 May 14.

Department of Endocrinology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.

Background: Short messages service (SMS) provides a practical medium for delivering content to address patients to adherence to self-management. The aim of study was to design some patient-centered health education messages, evaluate the feasibility of messages, and explore the effect of this model.

Methods: The messages were designed by a panel of experts, and SMS Quality Evaluation Questionnaire was used to evaluate their quality. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-01129-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222448PMC
May 2020
1.496 Impact Factor

Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool.

BMC Med Inform Decis Mak 2020 May 14;20(1):91. Epub 2020 May 14.

Institute of Health Policy Management & Evaluation, University of Toronto, 155 College Street, Toronto, Ontario, M5T 3M6, Canada.

Background: Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to inform a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-01128-8DOI Listing

Coronary heart disease and mortality following a breast cancer diagnosis.

BMC Med Inform Decis Mak 2020 May 13;20(1):88. Epub 2020 May 13.

Institute for Informatics (I2), Washington University School of Medicine, 600 S. Taylor Avenue, Suite 102, Campus Box 8102, St. Louis, MO, 63110, USA.

Background: Coronary heart disease (CHD) is a leading cause of morbidity and mortality for breast cancer survivors, yet the joint effect of adverse cardiovascular health (CVH) and cardiotoxic cancer treatments on post-treatment CHD and death has not been quantified.

Methods: We conducted statistical and machine learning approaches to evaluate 10-year risk of these outcomes among 1934 women diagnosed with breast cancer during 2006 and 2007. Overall CVH scores were classified as poor, intermediate, or ideal for 5 factors, smoking, body mass index, blood pressure, glucose/hemoglobin A1c, and cholesterol from clinical data within 5 years prior to the breast cancer diagnosis. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1127-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218836PMC

Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance.

BMC Med Inform Decis Mak 2020 May 13;20(1):89. Epub 2020 May 13.

Department of Urology, University of Michigan, NCRC Bldg. 16, 1st Floor, Room 114W, 2800 Plymouth Road, Ann Arbor, MI, 48109-2900, USA.

Background: Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.

Methods: Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1126-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218839PMC

How stable is lung function in patients with stable chronic obstructive pulmonary disease when monitored using a telehealth system? A longitudinal and home-based study.

BMC Med Inform Decis Mak 2020 May 12;20(1):87. Epub 2020 May 12.

Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University, Umeå, Sweden.

Background: Many telehealth systems have been designed to identify signs of exacerbations in patients with chronic obstructive pulmonary disease (COPD), but few previous studies have reported the nature of recorded lung function data and what variations to expect in this group of individuals. The aim of the study was to evaluate the nature of individual diurnal, day-to-day and long-term variation in important prognostic markers of COPD exacerbations by employing a telehealth system developed in-house.

Methods: Eight women and five men with COPD performed measurements (spirometry, pulse oximetry and the COPD assessment test (CAT)) three times per week for 4-6 months using the telehealth system. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1103-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218552PMC

Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients.

BMC Med Inform Decis Mak 2020 May 7;20(1):85. Epub 2020 May 7.

Multidisciplinary Epidemiological and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.

Background: With higher adoption of electronic health records at health-care centers, electronic search algorithms (computable phenotype) for identifying acute decompensated heart failure (ADHF) among hospitalized patients can be an invaluable tool to enhance data abstraction accuracy and efficacy in order to improve clinical research accrual and patient centered outcomes. We aimed to derive and validate a computable phenotype for ADHF in hospitalized patients.

Methods: We screened 256, 443 eligible (age > 18 years and with prior research authorization) individuals who were admitted to Mayo Clinic Hospital in Rochester, MN, from January 1, 2006, through December 31, 2014. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1092-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206747PMC

The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients.

BMC Med Inform Decis Mak 2020 May 7;20(1):86. Epub 2020 May 7.

Faculty of Medicine and Health, School of Medical Sciences, Department of Surgery, Örebro University, Örebro, Sweden.

Background: Geriatric patients frequently undergo emergency general surgery and accrue a greater risk of postoperative complications and fatal outcomes than the general population. It is highly relevant to develop the most appropriate care measures and to guide patient-centered decision-making around end-of-life care. Portsmouth - Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM) has been used to predict mortality in patients undergoing different types of surgery. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1100-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206787PMC

A combination of two methods for evaluating the usability of a hospital information system.

BMC Med Inform Decis Mak 2020 May 4;20(1):84. Epub 2020 May 4.

Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran.

Background: None of the evaluation methods can identify all the usability problems of information systems. So far, no study has sufficiently investigated the potential of a combination of these methods to identify usability problems. The present study aimed at examining the potential for combining two commonly utilized user-based and expert-based methods to evaluate the usability of a hospital information system. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1083-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199374PMC

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.

BMC Med Inform Decis Mak 2020 May 1;20(1):83. Epub 2020 May 1.

Cardiology Division, West China Hospital, Sichuan University, Chengdu, China.

Background: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1101-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195717PMC

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BMC Med Inform Decis Mak 2020 Apr 29;20(1):79. Epub 2020 Apr 29.

Center for Health Outcomes and Informatics Research, Loyola University Chicago, 2160 S. First Avenue, Maywood, IL, 60156, USA.

Background: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1099-yDOI Listing

Distributed representation and one-hot representation fusion with gated network for clinical semantic textual similarity.

BMC Med Inform Decis Mak 2020 Apr 30;20(Suppl 1):72. Epub 2020 Apr 30.

Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong, China.

Background: Semantic textual similarity (STS) is a fundamental natural language processing (NLP) task which can be widely used in many NLP applications such as Question Answer (QA), Information Retrieval (IR), etc. It is a typical regression problem, and almost all STS systems either use distributed representation or one-hot representation to model sentence pairs.

Methods: In this paper, we proposed a novel framework based on a gated network to fuse distributed representation and one-hot representation of sentence pairs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1045-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191689PMC

Development of a patient decision aid for the management of superficial basal cell carcinoma (BCC) in adults with a limited life expectancy.

BMC Med Inform Decis Mak 2020 Apr 29;20(1):81. Epub 2020 Apr 29.

Department of Dermatology, Stanford University, CCSR Building Room 4235, 269 Campus Drive, Stanford, USA.

Background: Basal cell carcinoma (BCC) is a slow-growing, rarely lethal skin cancer that affects people 65 years or older. A range of treatment options exist for BCC, but there is little evidence available to guide patients and providers in selecting the best treatment options.

Objectives: This study outlines the development of a patient decision aid (PDA) for low-risk BCC that can be used by patients and providers to assist in shared decision-making. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1081-8DOI Listing

Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records.

BMC Med Inform Decis Mak 2020 Apr 30;20(Suppl 1):73. Epub 2020 Apr 30.

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA.

Background: Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP2018 organizers have made the first attempt to annotate 1068 sentence pairs from clinical notes and have called for a community effort to tackle the Semantic Textual Similarity (BioCreative/OHNLP STS) challenge. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1044-0DOI Listing

The inhibiting effects of resistance to change of disability determination system: a status quo bias perspective.

BMC Med Inform Decis Mak 2020 Apr 29;20(1):82. Epub 2020 Apr 29.

Department of Information Management, Nanhua University, No.55, Sec. 1, Nanhua Rd., Dalin Township, Chiayi County, 62249, Taiwan, Republic of China.

Background: Information systems implementation projects have been historically plagued by failures for which user resistance has consistently been identified as a salient reason. Most prior studies investigated either the causes or the consequences of Resistance to Change (RTC) of medical related Information Systems. In this study, we simultaneously explore the causes and impacts of RTC of Disability Determination System (DDS). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1090-7DOI Listing

Comparison of explicit values clarification method (VCM), implicit VCM and no VCM decision aids for men considering prostate cancer screening: protocol of a randomized trial.

BMC Med Inform Decis Mak 2020 Apr 29;20(1):78. Epub 2020 Apr 29.

Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Al. Prof. Hernâni Monteiro, 4200 - 319, Porto, Portugal.

Background: Screening with prostate-specific antigen (PSA) test for prostate cancer is considered a preference sensitive decision; meaning it does not only depend on what is best from a medical point of view, but also from a patient value standpoint. Decision aids are evidence-based tools which are shown to help people feel clearer about their values; therefore it has been advocated that decision aids should contain a specific values clarification method (VCM). VCMs may be either implicit or explicit, but the evidence concerning the best method is scarce. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1094-3DOI Listing

Hospital doctors' attitudes to brief educational messages that aim to modify diagnostic test requests: a qualitative study.

BMC Med Inform Decis Mak 2020 Apr 29;20(1):80. Epub 2020 Apr 29.

Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK.

Background: Avoidable use of diagnostic tests can both harm patients and increase the cost of healthcare. Nudge-type educational interventions have potential to modify clinician behaviour while respecting clinical autonomy and responsibility, but there is little evidence how this approach may be best used in a healthcare setting. This study aims to explore attitudes of hospital doctors to two nudge-type messages: one concerning potential future cancer risk after receiving a CT scan, another about the financial costs of blood tests. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1087-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191798PMC

Correction to: The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions.

BMC Med Inform Decis Mak 2020 Apr 28;20(1):77. Epub 2020 Apr 28.

Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.

After publication of this supplement article [1], it is requested the grant ID in the Funding section should be corrected from NSF grant IIS-7811367 to NSF grant IIS-1902617. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1096-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7187491PMC

Creating value with eHealth: identification of the value proposition with key stakeholders for the resilience navigator app.

BMC Med Inform Decis Mak 2020 Apr 27;20(1):76. Epub 2020 Apr 27.

Psychology, Health & Technology, University of Twente, 10 De Zul, Enschede, 7522, NJ, The Netherlands.

Background: For a stress-management app to be persuasive and impactful, designers and developers should obtain a clear perspective of the value proposition according to key stakeholders before development. However, this is often not the case. In order to increase the chance of creating an impact by means of the Resilience Navigator app, this study aims to identify key stakeholders and work with them to gain an in-depth understanding of the value proposition of this stress-management app. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1088-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7184708PMC

Development and validation of data quality rules in administrative health data using association rule mining.

BMC Med Inform Decis Mak 2020 Apr 25;20(1):75. Epub 2020 Apr 25.

Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.

Background: Data quality assessment presents a challenge for research using coded administrative health data. The objective of this study is to develop and validate a set of coding association rules for coded diagnostic data.

Methods: We used the Canadian re-abstracted hospital discharge abstract data coded in International Classification of Disease, 10th revision (ICD-10) codes. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1089-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7183129PMC

Non-health outcomes affecting self-care behaviors and medical decision-making preference in patients with type 2 diabetes: a cross-sectional study.

BMC Med Inform Decis Mak 2020 Apr 23;20(1):74. Epub 2020 Apr 23.

Department of Development and Planning, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan.

Background: The effects of patient sustained self-care behaviors on glycemic control are even greater than the effects of medical treatment, indicating the value of identifying the factors that influence self-care behaviors. To date, these factors have not been placed in a single model to clarify the critical path affecting self-care behaviors. The aims of this study were to explore the relationships of these factors and the differences in patient preference for medical decision-making. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1095-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181515PMC

How an electronic health record became a real-world research resource: comparison between London's Whole Systems Integrated Care database and the Clinical Practice Research Datalink.

BMC Med Inform Decis Mak 2020 04 20;20(1):71. Epub 2020 Apr 20.

Dr Foster Unit, Imperial College London, School of Public Health, Imperial College London, 3 Dorset Rise, London, EC4Y 8EN, UK.

Background: In the UK, several initiatives have resulted in the creation of local data warehouses of electronic patient records. Originally developed for commissioning and direct patient care, they are potentially useful for research, but little is known about them outside their home area. We describe one such local warehouse, the Whole Systems Integrated Care (WSIC) database in NW London, and its potential for research as the "Discover" platform. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1082-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171852PMC

Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research register (SHARE).

BMC Med Inform Decis Mak 2020 04 19;20(1):70. Epub 2020 Apr 19.

School of Medicine, University of St. Andrews, North Haugh, St. Andrews, Scotland, KY16 9TF, UK.

Background: Trials often struggle to achieve their target sample size with only half doing so. Some researchers have turned to Electronic Health Records (EHRs), seeking a more efficient way of recruitment. The Scottish Health Research Register (SHARE) obtained patients' consent for their EHRs to be used as a searching base from which researchers can find potential participants. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1091-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169032PMC

Understanding the utilisation of a novel interactive electronic medication safety dashboard in general practice: a mixed methods study.

BMC Med Inform Decis Mak 2020 04 17;20(1):69. Epub 2020 Apr 17.

Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.

Background: Improving medication safety is a major concern in primary care settings worldwide. The Salford Medication safety dASHboard (SMASH) intervention provided general practices in Salford (Greater Manchester, UK) with feedback on their safe prescribing and monitoring of medications through an online dashboard, and input from practice-based trained clinical pharmacists. In this study we explored how staff working in general practices used the SMASH dashboard to improve medication safety, through interactions with the dashboard to identify potential medication safety hazards and their workflow to resolve identified hazards. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1084-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164282PMC

Personal decision support for survivor engagement: formulation and feasibility evaluation of a conceptual framework for implementing online cancer survivorship care plans.

BMC Med Inform Decis Mak 2020 03 23;20(1):59. Epub 2020 Mar 23.

Department of Health Informatics and Administration, Social Media and Health Research & Training Lab, College of Health Sciences, University of Wisconsin - Milwaukee, Northwest Quadrant Building B, Rm #6410, 2025 East Newport Avenue, Milwaukee, WI, 53201-0413, USA.

Background: Although cancer survivorship care plans have been in use for several years, they have been shown to not be effective in meeting the long-term needs of cancer survivors, in addition being generic and passive in nature. Interactive survivorship care plans in the form of a personal decision support aid could provide an opportunity to not only engage survivors in their health care, but also capture meaningful treatment-related outcomes to use as a rich data source as the basis for making informed decisions. The objective of this research is to formulate an evidence-based model framework for implementing breast cancer survivorship guidelines via an online breast cancer survivorship care plan (SCP). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1073-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7092430PMC

DLI-IT: a deep learning approach to drug label identification through image and text embedding.

BMC Med Inform Decis Mak 2020 04 15;20(1):68. Epub 2020 Apr 15.

FDA/National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR, 72079, USA.

Background: Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1078-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158001PMC
April 2020
1.496 Impact Factor

Construction of a semi-automatic ICD-10 coding system.

BMC Med Inform Decis Mak 2020 04 15;20(1):67. Epub 2020 Apr 15.

Department of Information, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.

Background: The International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1085-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7157985PMC

Thailand medical mobile application for patients triage base on criteria based dispatch protocol.

BMC Med Inform Decis Mak 2020 04 9;20(1):66. Epub 2020 Apr 9.

Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.

Background: Before patients are admitted into the emergency department, it is important to undertake a pre-hospital process, both in terms of treatment performance and a request for resources from an emergency unit. The existing system to triage patients in Thailand is not functioning to its full capacity in either the primary medical system or pre-hospital treatment with shortcomings in the areas of speed, features, and appropriate systems. There is a high possibility of issuing a false Initial Dispatch Code (IDC), which will cause the over or underutilisation of emergency resources, such as rescue teams, community hospitals and emergency medical volunteers. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1075-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147000PMC

Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine.

BMC Med Inform Decis Mak 2020 04 6;20(1):64. Epub 2020 Apr 6.

Basic Medical School, Chengdu University of Traditional Chinese Medicine, No. 37, Shi Er Qiao Road, Chengdu, 610075, People's Republic of China.

Background: In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future.

Methods: We developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1079-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132896PMC

Men's view on participation in decisions about prostate-specific antigen (PSA) screening: patient and public involvement in development of a survey.

BMC Med Inform Decis Mak 2020 04 6;20(1):65. Epub 2020 Apr 6.

Department of Psychology, University of Southern Denmark, Campusvej 55, DK-5230, Odense M, Denmark.

Background: Prostate-Specific Antigen (PSA) screening for early detection of prostate cancer (PCa) may prevent some cancer deaths, but also may miss some cancers or lead to unnecessary and potentially harmful treatment. Therefore, involving patients in decision-making about PSA screening is recommended. However, we know little about the attitude of men regarding participation in decisions about PSA screening and how to assess such attitudes. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1077-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132968PMC

Technical requirements framework of hospital information systems: design and evaluation.

BMC Med Inform Decis Mak 2020 04 3;20(1):61. Epub 2020 Apr 3.

Dr. Shari'ati Hospital, Hormozgan University of Medical Sciences, Hormozgan, Iran.

Background: Implementing the health information system (HIS) is more complex and costly than implementing other information systems. The present study was conducted to design and evaluate technical requirements for the HIS.

Methods: The present study was conducted in 2016 by determining technical requirements for the HIS using the Delphi technique and then evaluating this system using a checklist based on the approved requirements. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1076-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119017PMC

Assessing patient safety in a pediatric telemedicine setting: a multi-methods study.

BMC Med Inform Decis Mak 2020 04 3;20(1):63. Epub 2020 Apr 3.

The Emergency Department, Schneider Children's Medical Center, Petach-Tikvah, Israel.

Background: Telemedicine and telephone-triage may compromise patient safety, particularly if urgency is underestimated. We aimed to explore the level of safety of a pediatric telemedicine service, with particular reference to the appropriateness of the medical diagnoses made by the online physicians and the reasonableness of their decisions.

Methods: This retrospective multi-method study investigated the decision-making process of physicians in a pediatric tele-triage service provided in Israel. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1074-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126468PMC
April 2020
1.496 Impact Factor

Evaluating users' experiences of electronic prescribing systems in relation to patient safety: a mixed methods study.

BMC Med Inform Decis Mak 2020 04 3;20(1):62. Epub 2020 Apr 3.

NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, W2 1PE, UK.

Background: User interface (UI) design features such as screen layout, density of information, and use of colour may affect the usability of electronic prescribing (EP) systems, with usability problems previously associated with medication errors. To identify how to improve existing systems, our aim was to explore prescribers' perspectives of UI features of a commercially available EP system, and how these may affect patient safety.

Methods: Two studies were conducted, each including ten participants prescribing a penicillin for a test patient with a penicillin allergy. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1080-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126479PMC

Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

BMC Med Inform Decis Mak 2020 03 30;20(1):60. Epub 2020 Mar 30.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Background: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research.

Method: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1072-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106829PMC

The contribution of electronic health records to risk management through accreditation of residential aged care homes in Australia.

BMC Med Inform Decis Mak 2020 03 20;20(1):58. Epub 2020 Mar 20.

Centre for IT-enabled Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia.

Background: The Australian government has implemented a compulsory aged care accreditation system to guide and monitor the risk management approach in registered residential aged care (RAC) homes. This research assessed the contribution of electronic health records (EHR) to risk management in RAC homes in relation to the extent that aged care accreditation fulfils its role.

Methods: A convenience sample of 5560 aged care accreditation reports published from 2011 to 2018 was manually downloaded from the Accreditation Agency web site. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1070-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082951PMC
March 2020
1.496 Impact Factor

Mining and visualizing high-order directional drug interaction effects using the FAERS database.

BMC Med Inform Decis Mak 2020 Mar 18;20(Suppl 2):50. Epub 2020 Mar 18.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Background: Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1053-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079342PMC

A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.

BMC Med Inform Decis Mak 2020 Mar 18;20(Suppl 2):49. Epub 2020 Mar 18.

Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.

Background: The key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, precision and cost, traditional experimental methods are difficult to be widely used to infer these potential Drug-Target Interactions (DTIs). Therefore, it is urgent to develop effective computational methods to validate the interaction between drugs and target. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1052-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079345PMC

What matters to me - a web-based preference elicitation tool for clients in long-term care: a user-centred design.

BMC Med Inform Decis Mak 2020 03 17;20(1):57. Epub 2020 Mar 17.

Department of Family Medicine, CAPHRI School for Public Health and Primary Care, Maastricht University Medical Centre, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.

Background: During the process of decision-making for long-term care, clients are often dependent on informal support and available information about quality ratings of care services. However, clients do not take ratings into account when considering preferred care, and need assistance to understand their preferences. A tool to elicit preferences for long-term care could be beneficial. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1067-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077015PMC

The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions.

BMC Med Inform Decis Mak 2020 Mar 18;20(Suppl 2):51. Epub 2020 Mar 18.

Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.

In this editorial, we briefly summarize the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. Then, we introduce the two research articles included in this supplement issue. These two research articles were selected after careful review of 105 articles that were submitted to the conference, and cover topics on deep learning for drug-target interaction prediction and data mining and visualization of high-order drug-drug interactions. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1051-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079340PMC

Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms.

BMC Med Inform Decis Mak 2020 03 14;20(1):56. Epub 2020 Mar 14.

Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada.

Background: Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients' health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1068-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071570PMC

The design and development of technology platforms in a developing country healthcare context from an ecosystem perspective.

BMC Med Inform Decis Mak 2020 03 12;20(1):55. Epub 2020 Mar 12.

Department of Industrial Engineering, Stellenbosch University, New Industrial Engineering Building; Reception on 2nd floor, Banghoek Road, Stellenbosch, 7600, South Africa.

Background: Research on the development and functioning of technology platforms specifically for health applications in sub-Saharan Africa (SSA), is limited. The healthcare sector has also been resistant to platform adoption due to characteristics such as sensitive data and high cost of failure. A framework for the design, development and implementation of technology platforms in the South African health context could therefore contribute to the gap in research as well as provide a practical tool that platform owners could use to potentially increase the adoption of platforms in this context. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1028-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068897PMC

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains.

BMC Med Inform Decis Mak 2020 03 12;20(1):54. Epub 2020 Mar 12.

Department of Epidemiology, GROW - School for Oncology and Developmental Biology, Maastricht University, P. Debyeplein 1, 6200, MD, Maastricht, the Netherlands.

Background: Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1064-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068880PMC

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data.

BMC Med Inform Decis Mak 2020 03 11;20(1):53. Epub 2020 Mar 11.

Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27517, USA.

Background: Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease.

Results: We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1056-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066811PMC

A decision tree to improve identification of pathogenic mutations in clinical practice.

BMC Med Inform Decis Mak 2020 03 10;20(1):52. Epub 2020 Mar 10.

Bioinformatics Postgraduate Program, Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil.

Background: A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1060-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063785PMC

An evaluation of time series summary statistics as features for clinical prediction tasks.

BMC Med Inform Decis Mak 2020 03 5;20(1):48. Epub 2020 Mar 5.

Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, People's Republic of China.

Background: Clinical prediction tasks such as patient mortality, length of hospital stay, and disease diagnosis are highly important in critical care research. The existing studies for clinical prediction mainly used simple summary statistics to summarize information from physiological time series. However, this lack of statistics leads to a lack of information. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1063-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059727PMC

Content and system development of a digital patient-provider communication tool to support shared decision making in chronic health care: InvolveMe.

BMC Med Inform Decis Mak 2020 03 4;20(1):46. Epub 2020 Mar 4.

Department of Digital Health Research, Division of Medicine, Oslo University Hospital, Oslo, Norway.

Background: Chronic conditions present major health problems, affecting an increasing number of individuals who experience a variety of symptoms that impact their health related quality of life. Digital tools can be of support in chronic conditions, potentially improving patient-provider communication, promoting shared decision making for treatment and care, and possibly even improving patient outcomes. This study aimed to develop a digital tool for patient-provider communication in chronic health care settings and describes the data collection and subsequent content and software development of the InvolveMe tool. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12911-020-1065-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7057594PMC