JMIR Publications

JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.


Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) focusses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.

Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2013: 4.7), JMIR Med Inform has a different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.

JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.

JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (


Recent Articles:

  • Artemis in Cloud.
This picture has been created by the Authors. All rights reserved.

    Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework


    Background: Analytics-as-a-service (AaaS) is one of the latest provisions emerging from the cloud services family. Utilizing this paradigm of computing in health informatics will benefit patients, care providers, and governments significantly. This work is a novel approach to realize health analytics as services in critical care units in particular. Objective: To design, implement, evaluate, and deploy an extendable big-data compatible framework for health-analytics-as-a-service that offers both real-time and retrospective analysis. Methods: We present a novel framework that can realize health data analytics-as-a-service. The framework is flexible and configurable for different scenarios by utilizing the latest technologies and best practices for data acquisition, transformation, storage, analytics, knowledge extraction, and visualization. We have instantiated the proposed method, through the Artemis project, that is, a customization of the framework for live monitoring and retrospective research on premature babies and ill term infants in neonatal intensive care units (NICUs). Results: We demonstrated the proposed framework in this paper for monitoring NICUs and refer to it as the Artemis-In-Cloud (Artemis-IC) project. A pilot of Artemis has been deployed in the SickKids hospital NICU. By infusing the output of this pilot set up to an analytical model, we predict important performance measures for the final deployment of Artemis-IC. This process can be carried out for other hospitals following the same steps with minimal effort. SickKids’ NICU has 36 beds and can classify the patients generally into 5 different types including surgical and premature babies. The arrival rate is estimated as 4.5 patients per day, and the average length of stay was calculated as 16 days. Mean number of medical monitoring algorithms per patient is 9, which renders 311 live algorithms for the whole NICU running on the framework. The memory and computation power required for Artemis-IC to handle the SickKids NICU will be 32 GB and 16 CPU cores, respectively. The required amount of storage was estimated as 8.6 TB per year. There will always be 34.9 patients in SickKids NICU on average. Currently, 46% of patients cannot get admitted to SickKids NICU due to lack of resources. By increasing the capacity to 90 beds, all patients can be accommodated. For such a provisioning, Artemis-IC will need 16 TB of storage per year, 55 GB of memory, and 28 CPU cores. Conclusions: Our contributions in this work relate to a cloud architecture for the analysis of physiological data for clinical decisions support for tertiary care use. We demonstrate how to size the equipment needed in the cloud for that architecture based on a very realistic assessment of the patient characteristics and the associated clinical decision support algorithms that would be required to run for those patients. We show the principle of how this could be performed and furthermore that it can be replicated for any critical care setting within a tertiary institution.

  • The Center for SUDEP Research (CSR). This image is from a public domain (

    NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research


    Background: A unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for large multicenter studies, where thousands of research participants may be recruited from multiple sites. An important property of study identifiers is error tolerance (or validatable), in that inadvertent editing mistakes during their transmission and use will most likely result in invalid study identifiers. Objective: This paper introduces a novel method called "Randomized N-gram Hashing (NHash)," for generating unique study identifiers in a distributed and validatable fashion, in multicenter research. NHash has a unique set of properties: (1) it is a pseudonym serving the purpose of linking research data about a study participant for research purposes; (2) it can be generated automatically in a completely distributed fashion with virtually no risk for identifier collision; (3) it incorporates a set of cryptographic hash functions based on N-grams, with a combination of additional encryption techniques such as a shift cipher; (d) it is validatable (error tolerant) in the sense that inadvertent edit errors will mostly result in invalid identifiers. Methods: NHash consists of 2 phases. First, an intermediate string using randomized N-gram hashing is generated. This string consists of a collection of N-gram hashes f1, f2, ..., fk. The input for each function fi has 3 components: a random number r, an integer n, and input data m. The result, fi(r, n, m), is an n-gram of m with a starting position s, which is computed as (r mod |m|), where |m| represents the length of m. The output for Step 1 is the concatenation of the sequence f1(r1, n1, m1), f2(r2, n2, m2), ..., fk(rk, nk, mk). In the second phase, the intermediate string generated in Phase 1 is encrypted using techniques such as shift cipher. The result of the encryption, concatenated with the random number r, is the final NHash study identifier. Results: We performed experiments using a large synthesized dataset comparing NHash with random strings, and demonstrated neglegible probability for collision. We implemented NHash for the Center for SUDEP Research (CSR), a National Institute for Neurological Disorders and Stroke-funded Center Without Walls for Collaborative Research in the Epilepsies. This multicenter collaboration involves 14 institutions across the United States and Europe, bringing together extensive and diverse expertise to understand sudden unexpected death in epilepsy patients (SUDEP). Conclusions: The CSR Data Repository has successfully used NHash to link deidentified multimodal clinical data collected in participating CSR institutions, meeting all desired objectives of NHash.

  • Clinician documentation system. Image by Yuan Lai, 2014.

    Disrupting Electronic Health Records Systems: The Next Generation


    The health care system suffers from both inefficient and ineffective use of data. Data are suboptimally displayed to users, undernetworked, underutilized, and wasted. Errors, inefficiencies, and increased costs occur on the basis of unavailable data in a system that does not coordinate the exchange of information, or adequately support its use. Clinicians’ schedules are stretched to the limit and yet the system in which they work exerts little effort to streamline and support carefully engineered care processes. Information for decision-making is difficult to access in the context of hurried real-time workflows. This paper explores and addresses these issues to formulate an improved design for clinical workflow, information exchange, and decision making based on the use of electronic health records.

  • EBMPracticeNet brings the best our scientific community has to offer directly to the medical practice (Copyright by EBMPracticeNet,

    Technology for Large-Scale Translation of Clinical Practice Guidelines: A Pilot Study of the Performance of a Hybrid Human and Computer-Assisted Approach


    Background: The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, began in 2011 to optimize quality of care by promoting evidence-based decision making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL MultiTerm terminology databases) from medical terminology databases, and support from online machine translation. This resulted in a validated translation memory, which is now in use for the translation of new and updated guidelines. Objective: The objective of this experiment was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. Methods: We conducted a pilot study in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (1) by certificated junior translators without medical specialization using the hybrid method, (2) by an experienced medical translator without this support, and (3) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The human translation edit rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. Results: The average number of words per guideline was 1195 and the mean total translation time was 100.2 minutes/1000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 minutes/1000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Conclusions: Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines, there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator).

  • (c) Yi-Ju Tseng.

    A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation


    Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

  • Feature CERT image for homepage.

    Meaningful Use of Electronic Health Records: Experiences From the Field and Future Opportunities


    Background: With the aim of improving health care processes through health information technology (HIT), the US government has promulgated requirements for “meaningful use” (MU) of electronic health records (EHRs) as a condition for providers receiving financial incentives for the adoption and use of these systems. Considerable uncertainty remains about the impact of these requirements on the effective application of EHR systems. Objective: The Agency for Healthcare Research and Quality (AHRQ)-sponsored Centers for Education and Research in Therapeutics (CERTs) critically examined the impact of the MU policy relating to the use of medications and jointly developed recommendations to help inform future HIT policy. Methods: We gathered perspectives from a wide range of stakeholders (N=35) who had experience with MU requirements, including academicians, practitioners, and policy makers from different health care organizations including and beyond the CERTs. Specific issues and recommendations were discussed and agreed on as a group. Results: Stakeholders’ knowledge and experiences from implementing MU requirements fell into 6 domains: (1) accuracy of medication lists and medication reconciliation, (2) problem list accuracy and the shift in HIT priorities, (3) accuracy of allergy lists and allergy-related standards development, (4) support of safer and effective prescribing for children, (5) considerations for rural communities, and (6) general issues with achieving MU. Standards are needed to better facilitate the exchange of data elements between health care settings. Several organizations felt that their preoccupation with fulfilling MU requirements stifled innovation. Greater emphasis should be placed on local HIT configurations that better address population health care needs. Conclusions: Although MU has stimulated adoption of EHRs, its effects on quality and safety remain uncertain. Stakeholders felt that MU requirements should be more flexible and recognize that integrated models may achieve information-sharing goals in alternate ways. Future certification rules and requirements should enhance EHR functionalities critical for safer prescribing of medications in children.


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    Optimizing Patient Preparation and Surgical Experience Using eHealth Technology


    With population growth and aging, it is expected that the demand for surgical services will increase. However, increased complexity of procedures, time pressures on staff, and the demand for a patient-centered approach continue to challenge a system characterized by finite health care resources. Suboptimal care is reported in each phase of surgical care, from the time of consent to discharge and long-term follow-up. Novel strategies are thus needed to address these challenges to produce effective and sustainable improvements in surgical care across the care pathway. The eHealth programs represent a potential strategy for improving the quality of care delivered across various phases of care, thereby improving patient outcomes. This discussion paper describes (1) the key functions of eHealth programs including information gathering, transfer, and exchange; (2) examples of eHealth programs in overcoming challenges to optimal surgical care across the care pathway; and (3) the potential challenges and future directions for implementing eHealth programs in this setting. The eHealth programs are a promising alternative for collecting patient-reported outcome data, providing access to credible health information and strategies to enable patients to take an active role in their own health care, and promote efficient communication between patients and health care providers. However, additional rigorous intervention studies examining the needs of potential role of eHealth programs in augmenting patients’ preparation and recovery from surgery, and subsequent impact on patient outcomes and processes of care are needed to advance the field. Furthermore, evidence for the benefits of eHealth programs in supporting carers and strategies to maximize engagement from end users are needed.

  • (cc) Ji et al. CC-BY-SA 2.0, please cite as

    Using MEDLINE Elemental Similarity to Assist in the Article Screening Process for Systematic Reviews


    Background: Systematic reviews and their implementation in practice provide high quality evidence for clinical practice but are both time and labor intensive due to the large number of articles. Automatic text classification has proven to be instrumental in identifying relevant articles for systematic reviews. Existing approaches use machine learning model training to generate classification algorithms for the article screening process but have limitations. Objective: We applied a network approach to assist in the article screening process for systematic reviews using predetermined article relationships (similarity). The article similarity metric is calculated using the MEDLINE elements title (TI), abstract (AB), medical subject heading (MH), author (AU), and publication type (PT). We used an article network to illustrate the concept of article relationships. Using the concept, each article can be modeled as a node in the network and the relationship between 2 articles is modeled as an edge connecting them. The purpose of our study was to use the article relationship to facilitate an interactive article recommendation process. Methods: We used 15 completed systematic reviews produced by the Drug Effectiveness Review Project and demonstrated the use of article networks to assist article recommendation. We evaluated the predictive performance of MEDLINE elements and compared our approach with existing machine learning model training approaches. The performance was measured by work saved over sampling at 95% recall (WSS95) and the F-measure (F1). We also used repeated analysis over variance and Hommel’s multiple comparison adjustment to demonstrate statistical evidence. Results: We found that although there is no significant difference across elements (except AU), TI and AB have better predictive capability in general. Collaborative elements bring performance improvement in both F1 and WSS95. With our approach, a simple combination of TI+AB+PT could achieve a WSS95 performance of 37%, which is competitive to traditional machine learning model training approaches (23%-41% WSS95). Conclusions: We demonstrated a new approach to assist in labor intensive systematic reviews. Predictive ability of different elements (both single and composited) was explored. Without using model training approaches, we established a generalizable method that can achieve a competitive performance.

  • Spelling checker for health care content. URL:

    Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care


    Background: Consumer-generated content, such as postings on social media websites, can serve as an ideal source of information for studying health care from a consumer’s perspective. However, consumer-generated content on health care topics often contains spelling errors, which, if not corrected, will be obstacles for downstream computer-based text analysis. Objective: In this study, we proposed a framework with a spelling correction system designed for consumer-generated content and a novel ontology-based evaluation system which was used to efficiently assess the correction quality. Additionally, we emphasized the importance of context sensitivity in the correction process, and demonstrated why correction methods designed for electronic medical records (EMRs) failed to perform well with consumer-generated content. Methods: First, we developed our spelling correction system based on Google Spell Checker. The system processed postings acquired from MedHelp, a biomedical bulletin board system (BBS), and saved misspelled words (eg, sertaline) and corresponding corrected words (eg, sertraline) into two separate sets. Second, to reduce the number of words needing manual examination in the evaluation process, we respectively matched the words in the two sets with terms in two biomedical ontologies: RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT). The ratio of words which could be matched and appropriately corrected was used to evaluate the correction system’s overall performance. Third, we categorized the misspelled words according to the types of spelling errors. Finally, we calculated the ratio of abbreviations in the postings, which remarkably differed between EMRs and consumer-generated content and could largely influence the overall performance of spelling checkers. Results: An uncorrected word and the corresponding corrected word was called a spelling pair, and the two words in the spelling pair were its members. In our study, there were 271 spelling pairs detected, among which 58 (21.4%) pairs had one or two members matched in the selected ontologies. The ratio of appropriate correction in the 271 overall spelling errors was 85.2% (231/271). The ratio of that in the 58 spelling pairs was 86% (50/58), close to the overall ratio. We also found that linguistic errors took up 31.4% (85/271) of all errors detected, and only 0.98% (210/21,358) of words in the postings were abbreviations, which was much lower than the ratio in the EMRs (33.6%). Conclusions: We conclude that our system can accurately correct spelling errors in consumer-generated content. Context sensitivity is indispensable in the correction process. Additionally, it can be confirmed that consumer-generated content differs from EMRs in that consumers seldom use abbreviations. Also, the evaluation method, taking advantage of biomedical ontology, can effectively estimate the accuracy of the correction system and reduce manual examination time.

  • This is a royalty free image by pandpstock001 (

    Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer


    Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals.

  • Untitled.

    Analysis of PubMed User Sessions Using a Full-Day PubMed Query Log: A Comparison of Experienced and Nonexperienced PubMed Users


    Background: PubMed is the largest biomedical bibliographic information source on the Internet. PubMed has been considered one of the most important and reliable sources of up-to-date health care evidence. Previous studies examined the effects of domain expertise/knowledge on search performance using PubMed. However, very little is known about PubMed users’ knowledge of information retrieval (IR) functions and their usage in query formulation. Objective: The purpose of this study was to shed light on how experienced/nonexperienced PubMed users perform their search queries by analyzing a full-day query log. Our hypotheses were that (1) experienced PubMed users who use system functions quickly retrieve relevant documents and (2) nonexperienced PubMed users who do not use them have longer search sessions than experienced users. Methods: To test these hypotheses, we analyzed PubMed query log data containing nearly 3 million queries. User sessions were divided into two categories: experienced and nonexperienced. We compared experienced and nonexperienced users per number of sessions, and experienced and nonexperienced user sessions per session length, with a focus on how fast they completed their sessions. Results: To test our hypotheses, we measured how successful information retrieval was (at retrieving relevant documents), represented as the decrease rates of experienced and nonexperienced users from a session length of 1 to 2, 3, 4, and 5. The decrease rate (from a session length of 1 to 2) of the experienced users was significantly larger than that of the nonexperienced groups. Conclusions: Experienced PubMed users retrieve relevant documents more quickly than nonexperienced PubMed users in terms of session length.

  • Cover Picture, Copyright 2014 Lumiata, Inc.

    A Web-Based Tool for Patient Triage in Emergency Department Settings: Validation Using the Emergency Severity Index


    Background: We evaluated the concordance between triage scores generated by a novel Internet clinical decision support tool, Clinical GPS (cGPS) (Lumiata Inc, San Mateo, CA), and the Emergency Severity Index (ESI), a well-established and clinically validated patient severity scale in use today. Although the ESI and cGPS use different underlying algorithms to calculate patient severity, both utilize a five-point integer scale with level 1 representing the highest severity. Objective: The objective of this study was to compare cGPS results with an established gold standard in emergency triage. Methods: We conducted a blinded trial comparing triage scores from the ESI: A Triage Tool for Emergency Department Care, Version 4, Implementation Handbook to those generated by cGPS from the text of 73 sample case vignettes. A weighted, quadratic kappa statistic was used to assess agreement between cGPS derived severity scores and those published in the ESI handbook for all 73 cases. Weighted kappa concordance was defined a priori as almost perfect (kappa > 0.8), substantial (0.6 < kappa < 0.8), moderate (0.4 < kappa < 0.6), fair (0.2 < kappa< 0.4), or slight (kappa < 0.2). Results: Of the 73 case vignettes, the cGPS severity score matched the ESI handbook score in 95% of cases (69/73 cases), in addition, the weighted, quadratic kappa statistic showed almost perfect agreement (kappa = 0.93, 95% CI 0.854-0.996). In the subanalysis of 41 case vignettes assigned ESI scores of level 1 or 2, the cGPS and ESI severity scores matched in 95% of cases (39/41 cases). Conclusions: These results indicate that the cGPS is a reliable indicator of triage severity, based on its comparison to a standardized index, the ESI. Future studies are needed to determine whether the cGPS can accurately assess the triage of patients in real clinical environments.

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Latest Submissions Open for Peer-Review:

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  • Computer-aided Detection of Rib Fracture using Morphological Features in Chest Radiographs

    Date Submitted: Nov 23, 2015

    Open Peer Review Period: Nov 24, 2015 - Jan 19, 2016

    Background: The detection of rib fractures is extremely important for detecting the associated injuries, preventing complications, obviating medico-legal issues, detecting pathologic fractures, and he...

    Background: The detection of rib fractures is extremely important for detecting the associated injuries, preventing complications, obviating medico-legal issues, detecting pathologic fractures, and helping manage patients’ pain. However, the accuracy of detecting rib fractures from chest radiographs varies depending on the observer’s training level, the quality of the displayed images, and the clinical scenario for which the chest radiographs are obtained. Objective: We propose a new method for the detection of a rib fracture using image processing technique and morphological features in chest radiographs. Methods: The proposed method consists of the following steps: 1) acquisition of the cortical bone using an image processing technique in the region of interest (ROI); 2) acquisition of three morphological features, namely the cortical length, cortical perimeter, and cortical angle of the cortical bone region, for the fracture classification; and 3) classification of the fracture using support vector machine (SVM) classifier. Results: A statistically significant difference was found between the results of non-fracture and fracture states with respect to the defined features in the cortical bone region: cortical length (P< .001), cortical perimeter (P< .001), and cortical angle (P< .036). The result of the fracture classification using an SVM classifier revealed that the accuracy of 74.74% facilitates the classification of fractures. Conclusions: The proposed method, which includes an image processing technique for the cortical bone of ribs and the abovementioned features, could identify a fracture of the ribs from chest radiographs.

  • Challenges and Opportunities of Big Data in Healthcar

    Date Submitted: Nov 19, 2015

    Open Peer Review Period: Nov 19, 2015 - Jan 14, 2016

    Background: Big data analytics offer promise in many sectors, but with the aging of society, healthcare is looking at big data to provide answers to age-related issues, particularly dementia and disea...

    Background: Big data analytics offer promise in many sectors, but with the aging of society, healthcare is looking at big data to provide answers to age-related issues, particularly dementia and disease management. Objective: The purpose of this review is to summarize the challenges faced by big data analytics and the opportunities that big data opens. Methods: Four searches were performed for publications between January 1, 2010 to April 1, 2015 and an assessment made on their content germane to healthcare. From these publications (n=28), the authors summarized content and identified 9 and 11 themes under the categories Challenges and Opportunities, respectively. Results: The top challenges were issues of data structure, security, data standardisation, storage and transfers, and data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction. Conclusions: Big data analytic tools must overcome some legitimate obstacles, but the promise of its results could have positive, global implications. Clinical Trial: non applicable