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Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694; Impact Factor: 3.188) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a Pubmed/SCIE-indexed, top-rated, tier A journal which focuses 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 2018: 4.945), JMIR Med Inform has a slightly 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 is indexed in PubMed Central/PubMed and has also been accepted for SCIE. JMIR Medical Informatics received an inaugural Journal Impact Factor for 2018 (released June 2019) of 3.188.

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 (http://www.jmir.org/issue/current).

 

Recent Articles:

  • Source: Pexels; Copyright: Startup Stock Photos; URL: https://www.pexels.com/photo/working-woman-technology-computer-7374/; License: Licensed by JMIR.

    Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study

    Abstract:

    Background: Most current state-of-the-art models for searching the International Classification of Diseases, Tenth Revision Clinical Modification (ICD-10-CM) codes use word embedding technology to capture useful semantic properties. However, they are limited by the quality of initial word embeddings. Word embedding trained by electronic health records (EHRs) is considered the best, but the vocabulary diversity is limited by previous medical records. Thus, we require a word embedding model that maintains the vocabulary diversity of open internet databases and the medical terminology understanding of EHRs. Moreover, we need to consider the particularity of the disease classification, wherein discharge notes present only positive disease descriptions. Objective: We aimed to propose a projection word2vec model and a hybrid sampling method. In addition, we aimed to conduct a series of experiments to validate the effectiveness of these methods. Methods: We compared the projection word2vec model and traditional word2vec model using two corpora sources: English Wikipedia and PubMed journal abstracts. We used seven published datasets to measure the medical semantic understanding of the word2vec models and used these embeddings to identify the three–character-level ICD-10-CM diagnostic codes in a set of discharge notes. On the basis of embedding technology improvement, we also tried to apply the hybrid sampling method to improve accuracy. The 94,483 labeled discharge notes from the Tri-Service General Hospital of Taipei, Taiwan, from June 1, 2015, to June 30, 2017, were used. To evaluate the model performance, 24,762 discharge notes from July 1, 2017, to December 31, 2017, from the same hospital were used. Moreover, 74,324 additional discharge notes collected from seven other hospitals were tested. The F-measure, which is the major global measure of effectiveness, was adopted. Results: In medical semantic understanding, the original EHR embeddings and PubMed embeddings exhibited superior performance to the original Wikipedia embeddings. After projection training technology was applied, the projection Wikipedia embeddings exhibited an obvious improvement but did not reach the level of original EHR embeddings or PubMed embeddings. In the subsequent ICD-10-CM coding experiment, the model that used both projection PubMed and Wikipedia embeddings had the highest testing mean F-measure (0.7362 and 0.6693 in Tri-Service General Hospital and the seven other hospitals, respectively). Moreover, the hybrid sampling method was found to improve the model performance (F-measure=0.7371/0.6698). Conclusions: The word embeddings trained using EHR and PubMed could understand medical semantics better, and the proposed projection word2vec model improved the ability of medical semantics extraction in Wikipedia embeddings. Although the improvement from the projection word2vec model in the real ICD-10-CM coding task was not substantial, the models could effectively handle emerging diseases. The proposed hybrid sampling method enables the model to behave like a human expert.

  • Source: freepik; Copyright: jcomp; URL: https://www.freepik.com/free-photo/elderly-woman-sitting-wheelchairs-with-knee-pain_2888804.htm; License: Licensed by JMIR.

    Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study

    Abstract:

    Background: Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective: The aim of this study was to propose a Bayesian network (BN)–based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals. Methods: The proposed model’s structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results: A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78). Conclusions: The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs.

  • Source: freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/close-up-female-doctor-blue-uniform-examining-x-ray_4441442.htm; License: Licensed by JMIR.

    Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review

    Abstract:

    Background: With the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked. Objective: The aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use. Methods: The MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed. Results: A total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes. Conclusions: There is a gap in the literature between CAD’s well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use.

  • Source: Flickr; Copyright: Michael Coghlan; URL: https://www.flickr.com/photos/mikecogh/5040984567/in/photolist-8Fsn62-UihKzM-aEZfVA-FVs7A9-c6e8xb-obx4J5-25oGm3m-b7217z-5U9PsM-7gEr6V-TsHCT8-n7sQy2-axTHHE-fQohJF-83qGEH-dW7Tbr-djBvJc-79kMjY-ZhHbyh-bEmxuZ-dW7RWv-5qTuBR-5qTv48-2bZz3PY-dW7X9Z-59D9j9-fUwCRh-7; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

    Abstract:

    Background: A pressure ulcer is injury to the skin or underlying tissue, caused by pressure, friction, and moisture. Hospital-acquired pressure ulcers (HAPUs) may not only result in additional length of hospital stay and associated care costs but also lead to undesirable patient outcomes. Intensive care unit (ICU) patients show higher risk for HAPU development than general patients. We hypothesize that the care team’s decisions relative to HAPU risk assessment and prevention may be better supported by a data-driven, ICU-specific prediction model. Objective: The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the Braden scale on this specific population. Methods: We conducted a retrospective cohort study by using the data retrieved from the enterprise data warehouse of an academic medical center. Bivariate analyses were performed to compare the HAPU and non-HAPU groups. Multiple logistic regression was used to develop a prediction model with significant predictor variables from the bivariate analyses. Sensitivity, specificity, positive predictive values, negative predictive values, area under the receiver operating characteristic curve (AUC), and Youden index were used to compare with the Braden scale. Results: The total number of patient encounters studied was 12,654. The number of patients who developed an HAPU during their ICU stay was 735 (5.81% of the incidence rate). Age, gender, weight, diabetes, vasopressor, isolation, endotracheal tube, ventilator episode, Braden score, and ventilator days were significantly associated with HAPU. The overall accuracy of the model was 91.7%, and the AUC was .737. The sensitivity, specificity, positive predictive value, negative predictive value, and Youden index were .650, .693, .211, 956, and .342, respectively. Male patients were 1.5 times more, patients with diabetes were 1.5 times more, and patients under isolation were 3.1 times more likely to have an HAPU than female patients, patients without diabetes, and patients not under isolation, respectively. Conclusions: Using an extremely large, electronic health record–derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled us to develop a prediction model from the empirical data. The model showed acceptable performance compared with the Braden scale. The model may assist with clinicians’ decision on risk assessment, in addition to the Braden scale, as it is not difficult to interpret and apply to clinical practice. This approach may support avoidable reductions in HAPU incidence in intensive care.

  • Volunteers who participated in this study. Source: Image created by the Authors; Copyright: The Authors; URL: https://medinform.jmir.org/2019/3/e13331; License: Creative Commons Attribution (CC-BY).

    Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing–Driven Medical Information Extraction System:...

    Abstract:

    Background: The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming. Objective: To facilitate the data entry process, we developed a natural language processing–driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES–based eCRF application could improve the accuracy and efficiency of the data entry process. Methods: We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES–supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia. Results: For the congenital heart disease condition, the NLP-MIES–supported group increased accuracy by 15% (95% CI 4%-120%, P=.03) and reduced elapsed time by 33% (95% CI 22%-42%, P<.001) compared with the manual group. For the pneumonia condition, the NLP-MIES–supported group increased accuracy by 18% (95% CI 6%-32%, P=.008) and reduced elapsed time by 31% (95% CI 19%-41%, P<.001). Conclusions: Our system could improve both the accuracy and efficiency of the data entry process.

  • Source: Flickr; Copyright: U.S. Department of Agriculture; URL: https://www.flickr.com/photos/usdagov/8575102773/in/album-72157633043832227/; License: Creative Commons Attribution (CC-BY).

    Influence of Scribes on Patient-Physician Communication in Primary Care Encounters: Mixed Methods Study

    Abstract:

    Background: With the increasing adoption of electronic health record (EHR) systems, documentation-related burdens have been increasing for health care providers. Recent estimates indicate that primary care providers spend about one-half of their workdays interacting with the EHR, of which about half is focused on clerical tasks. To reduce documentation burdens associated with the EHR, health care systems and physician practices are increasingly implementing medical scribes to assist providers with real-time documentation. Scribes are typically unlicensed paraprofessionals who assist health care providers by documenting notes electronically under the direction of a licensed practitioner or physician in real time. Despite the promise of scribes, few studies have investigated their effect on clinical encounters, particularly with regard to patient-provider communication. Objective: The purpose of this quasi-experimental pilot study was to understand how scribes affect patient-physician communication in primary care clinical encounters. Methods: We employed a convergent mixed methods design and included a sample of three physician-scribe pairs and 34 patients. Patients’ clinical encounters were randomly assigned to a scribe or nonscribe group. We conducted patient surveys focused on perceptions of patient-provider communication and satisfaction with encounters, video recorded clinical encounters, and conducted physician interviews about their experiences with scribes. Results: Overall, the survey results revealed that patients across both arms reported very high satisfaction of communication with their physician, their physician’s use of the EHR, and their care, with very little variability. Video recording analysis supported patient survey data by demonstrating high measures of communication among physicians in both scribed and nonscribed encounters. Furthermore, video recordings revealed that the presence of scribes had very little effect on the clinical encounter. Conclusions: From the patient’s perspective, scribes are an acceptable addition to clinical encounters. Although they do not have much impact on patients’ perceptions of satisfaction and their impact on the clinical encounter itself was minimal, their potential to reduce documentation-related burden on physicians is valuable. Physicians noted important issues related to scribes, including important considerations for implementing scribe programs, the role of scribes in patient interactions, how physicians work with scribes, characteristics of good scribes, and the role of scribes in physician workflow.

  • Primary care provider creating an eConsult. Source: Image created by the Authors; Copyright: The Authors; URL: https://medinform.jmir.org/2019/3/e13354; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Improving the Referral Process, Timeliness, Effectiveness, and Equity of Access to Specialist Medical Services Through Electronic Consultation: Pilot Study

    Abstract:

    Background: Access to specialty care remains a major challenge in the Canadian health care system. Electronic consultation (eConsult) services allow primary care providers to seek specialist advice often without needing the patient to go for a face-to-face consultation. It improves overall access to specialists and the referral process using an electronic care consultation service in urban and rural primary care clinics. This study describes the preliminary results of a pilot study with an eConsult service across 3 regions in the province of Quebec, Canada. Objective: The main objective of this study was to provide a 1-year snapshot of the implementation of the eConsult Quebec Service in rural and urban primary care clinics to improve access to care and the specialty referral process for primary care providers (PCPs). Methods: We established an eConsult service that covers urban and rural communities in 3 regions of Quebec. We conducted a quantitative analysis of all eConsult cases submitted from July 4, 2017, to December 8, 2018. Results: For over a year, 1016 eConsults have been generated during the course of this study. A total of 97 PCPs submitted requests to 22 specialty groups and were answered by 40 different specialists. The most popular specialty was internal medicine (224/1016, 22%). Overall, 63% (640/1016) of completed cases did not require a face-to-face visit. PCPs rated the service as being of high or very high value for themselves in 98% (996/1016) of cases. Conclusions: The preliminary data highlight the success of the implementation of the eConsult Quebec Service across 6 primary care clinics. The eConsult platform proves to be effective, efficient, and well received by both patients and physicians. If used more widely, eConsult could help reducing wait times significantly. Recently, the Ministry of Health and Social Services of Quebec has identified developing a strategic plan to scale eConsults throughout other regions of the province as a top priority.

  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL: https://medinform.jmir.org/2019/3/e14248; License: Creative Commons Attribution (CC-BY).

    Initial Experience of the Synchronized, Real-Time, Interactive, Remote Transthoracic Echocardiogram Consultation System in Rural China: Longitudinal...

    Abstract:

    Background: China has a vast territory, and the quality of health care services provided, especially transthoracic echocardiography (TTE), in remote regions is still low. Patients usually need to travel long distances to tertiary care centers for confirmation of a diagnosis. Considering the rapid development of high-speed communication technology, telemedicine will be a significant technology for improving the diagnosis and treatment of patients at secondary care hospitals. Objective: This study aimed to discuss the feasibility and perceived clinical value of a synchronized, real-time, interactive, remote TTE consultation system based on cloud computing technology. Methods: By using the cloud computing platform coupled with unique dynamic image coding and decoding and synchronization technology, multidimensional communication information in the form of voice, texts, and pictures was integrated. A remote TTE consultation system connecting Henan Provincial People’s Hospital and two county-level secondary care hospitals located 300 km away was developed, which was used for consultation with 45 patients. Results: This remote TTE consultation system achieved remote consultation for 45 patients. The total time for consultation was 341.31 min, and the mean time for each patient was 7.58 (SD 6.17) min. Among the 45 patients, 3 were diagnosed with congenital heart diseases (7%) and 42 were diagnosed with acquired heart diseases (93%) at the secondary care hospitals. After expert consultation, the final diagnosis was congenital heart diseases in 5 patients (11%), acquired heart disease in 34 patients (76%), and absence of heart abnormalities in 6 patients (13%). Compared with the initial diagnosis at secondary care hospitals, remote consultation using this system revealed new abnormalities in 7 patients (16%), confirmation was obtained in 6 patients (13%), and abnormalities were excluded in 6 patients (13%). The expert opinions agreed with the initial diagnosis in the remaining 26 patients (58%). In addition, several questions about rare illnesses raised by the rural doctors at the secondary care hospitals were answered. Conclusions: The synchronized real-time interactive remote TTE consultation system based on cloud computing service and unique dynamic image coding and decoding technology had high feasibility and applicability.

  • Source: Freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/overhead-view-man-wearing-sock-lying-bed-suffering-from-stomach-pain_3614446.htm; License: Licensed by JMIR.

    Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis

    Abstract:

    Background: There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis. Objective: Our objective was to use interactive user-centered design methods to develop the Cirrhosis Order Set and Clinical Decision Support (CirrODS) tool in order to improve clinical decision-making and workflow. Methods: Two work groups were convened with clinicians, user experience designers, human factors and health services researchers, and information technologists to create user interface designs. CirrODS prototypes underwent several rounds of formative design. Physicians (n=20) at three hospitals were provided with clinical scenarios of patients with cirrhosis, and the admission orders made with and without the CirrODS tool were compared. The physicians rated their experience using CirrODS and provided comments, which we coded into categories and themes. We assessed the safety, usability, and quality of CirrODS using qualitative and quantitative methods. Results: We created an interactive CirrODS prototype that displays an alert when existing electronic data indicate a patient is at risk for cirrhosis. The tool consists of two primary frames, presenting relevant patient data and allowing recommended evidence-based tests and treatments to be ordered and categorized. Physicians viewed the tool positively and suggested that it would be most useful at the time of admission. When using the tool, the clinicians placed fewer orders than they placed when not using the tool, but more of the orders placed were considered to be high priority when the tool was used than when it was not used. The physicians’ ratings of CirrODS indicated above average usability. Conclusions: We developed a novel Web-based combined clinical decision-making and workflow support tool to alert and assist clinicians caring for patients with cirrhosis. Further studies are underway to assess the impact on quality of care for patients with cirrhosis in actual practice.

  • Observations of a doctor's use of mobile technology with a patient. Source: Flickr; Copyright: Tricia Wang; URL: https://www.flickr.com/photos/53991912@N00/4075802935; License: Creative Commons Attribution + Noncommercial + ShareAlike (CC-BY-NC-SA).

    Facility and Regional Factors Associated With the New Adoption of Electronic Medical Records in Japan: Nationwide Longitudinal Observational Study

    Abstract:

    Background: The rate of adoption of electronic medical record (EMR) systems has increased internationally, and new EMR adoption is currently a major topic in Japan. However, no study has performed a detailed analysis of longitudinal data to evaluate the changes in the EMR adoption status over time. Objective: This study aimed to evaluate the changes in the EMR adoption status over time in hospitals and clinics in Japan and to examine the facility and regional factors associated with these changes. Methods: Secondary longitudinal data were created by matching data in fiscal year (FY) 2011 and FY 2014 using reference numbers. EMR adoption status was defined as “EMR adoption,” “specified adoption schedule,” or “no adoption schedule.” Data were obtained for hospitals (n=4410) and clinics (n=67,329) that had no adoption schedule in FY 2011 and for hospitals (n=1068) and clinics (n=3132) with a specified adoption schedule in FY 2011. The EMR adoption statuses of medical institutions in FY 2014 were also examined. A multinomial logistic model was used to investigate the associations between EMR adoption status in FY 2014 and facility and regional factors in FY 2011. Considering the regional variations of these models, multilevel analyses with second levels were conducted. These models were constructed separately for hospitals and clinics, resulting in four multinomial logistic models. The odds ratio (OR) and 95% Bayesian credible interval (CI) were estimated for each variable. Results: A total of 6.9% of hospitals and 14.82% of clinics with no EMR adoption schedules in FY 2011 had adopted EMR by FY 2014, while 10.49% of hospitals and 33.65% of clinics with specified adoption schedules in FY 2011 had cancelled the scheduled adoption by FY 2014. For hospitals with no adoption schedules in FY 2011, EMR adoption/scheduled adoption was associated with practice size characteristics, such as number of outpatients (from quantile 4 to quantile 1: OR 1.67, 95% CI 1.005-2.84 and OR 2.40, 95% CI 1.80-3.21, respectively), and number of doctors (from quantile 4 to quantile 1: OR 4.20, 95% CI 2.39-7.31 and OR 2.02, 95% CI 1.52-2.64, respectively). For clinics with specified EMR adoption schedules in FY 2011, the factors negatively associated with EMR adoption/cancellation of scheduled EMR adoption were the presence of beds (quantile 4 to quantile 1: OR 0.57, 95% CI 0.45-0.72 and OR 0.74, 95% CI 0.58-0.96, respectively) and having a private establisher (quantile 4 to quantile 1: OR 0.27, 95% CI 0.13-0.55 and OR 0.43, 95% CI 0.19-0.91, respectively). No regional factors were significantly associated with the EMR adoption status of hospitals with no EMR adoption schedules; population density was positively associated with EMR adoption in clinics with no EMR adoption schedule (quantile 4 to quantile 1: OR 1.49, 95% CI 1.32-1.69). Conclusions: Different approaches are needed to promote new adoption of EMR systems in hospitals as compared to clinics. It is important to induce decision making in small- and medium-sized hospitals, and regional postdecision technical support is important to avoid cancellation of scheduled EMR adoption in clinics.

  • Physicians adopt medical technologies as a useful tool for their decisions. Source: Adobe Stock; Copyright: Milles Studio; URL: https://stock.adobe.com/hk/images/medicine-doctor-working-with-modern-computer-interface/61288704; License: Licensed by the authors.

    Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment

    Abstract:

    Background: Hospitals have deployed various types of technologies to alleviate the problem of high medical costs. The cost of pharmaceuticals is one of the main drivers of medical costs. The Prescription Automatic Screening System (PASS) aims to monitor physicians’ prescribing behavior, which has the potential to decrease prescription errors and medical treatment costs. However, a substantial number of cases with unsatisfactory results related to the effects of PASS have been noted. Objective: The objectives of this study were to systematically explore the imperative role of PASS on hospitals’ prescription errors and medical treatment costs and examine its contingency factors to clarify the various factors associated with the effective use of PASS. Methods: To systematically examine the various effects of PASS, we adopted a quasi-experiment methodology by using a 2-year observation dataset from 2 hospitals in China. We then analyzed the data related to physicians’ prescriptions both before and after the deployment of PASS and eliminated influences from a variety of perplexing factors by utilizing a control hospital that did not use a PASS system. In total, 754 physicians were included in this experiment comprising 11,054 patients: 400 physicians in the treatment group and 354 physicians in the control group. This study was also preceded by a series of interviews, which were employed to identify moderators. Thereafter, we adopted propensity score matching integrated with difference-in-differences to isolate the effects of PASS. Results: The effects of PASS on prescription errors and medical treatment costs were all significant (error: 95% CI –0.40 to –0.11, P=.001; costs: 95% CI –0.75 to –0.12, P=.007). Pressure from organizational rules and workload decreased the effect of PASS on prescription errors (95% CI 0.18-0.39; P<.001) and medical treatment costs (95% CI 0.07-0.55; P=.01), respectively. We also suspected that other pressures (eg, clinical title and risk categories of illness) also impaired physicians’ attention to alerts from PASS. However, the effects of PASS did not change among physicians with a higher clinical title or when treating diseases demonstrating high risk. This may be attributed to the fact that these physicians will focus more on their patients in these situations, regardless of having access to an intelligent system. Conclusions: Although implementation of PASS decreases prescription errors and medical treatment costs, workload and organizational rules remain problematic, as they tend to impair the positive effects of auxiliary diagnosis systems on performance. This again highlights the importance of considering both technical and organizational issues to obtain the highest level of effectiveness when deploying information technology in hospitals.

  • Source: Adobe Stock; Copyright: wladamir1804; URL: https://stock.adobe.com/images/family-medicine-insurance-concept-doctor-using-virtual-interface-offers-house-family-icon/183445574; License: Licensed by the authors.

    Incorporating Social Determinants of Health in Electronic Health Records: Qualitative Study of Current Practices Among Top Vendors

    Abstract:

    Background: Social determinants of health (SDH) are increasingly seen as important to understanding patient health and identifying appropriate interventions to improve health outcomes in what is a complex interplay between health system-, community-, and individual-level factors. Objective: The objective of the paper was to investigate the development of electronic health record (EHR) software products that allow health care providers to identify and address patients’ SDH in health care settings. Methods: We conducted interviews with six EHR vendors with large market shares in both ambulatory and inpatient settings. We conducted thematic analysis of the interviews to (1) identify their motivations to develop such software products, (2) describe their products and uses, and (3) identify facilitators and challenges to collection and use of SDH data—through their products or otherwise—either at the point of care or in population health interventions. Results: Our findings indicate that vendor systems and their functionalities are influenced by client demand and initiative, federal initiatives, and the vendors’ strategic vision about opportunities in the health care system. Among the small sample of vendors with large market shares, SDH is a new area for growth, and the vendors range in the number and sophistication of their SDH-related products. To enable better data analytics, population health management, and interoperability of SDH data, vendors recognized the need for more standardization of SDH performance measures across various federal and state programs, better mapping of SDH measures to multiple types of codes, and development of more codes for all SDH measures of interest. Conclusions: Vendors indicate they are actively developing products to facilitate the collection and use of SDH data for their clients and are seeking solutions to data standardization and interoperability challenges through internal product decisions and collaboration with policymakers. Due to a lack of policy standards around SDH data, product-specific decisions may end up being de facto policies given the market shares of particular vendors. However, commercial vendors appear ready to collaboratively discuss policy solutions such as standards or guidelines with each other, health care systems, and government agencies in order to further promote integration of SDH data into the standard of care for all health systems.

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  • Application and effects comparison of machine learning in the prediction of postpartum depression

    Date Submitted: Jul 18, 2019

    Open Peer Review Period: Jul 22, 2019 - Sep 16, 2019

    Background: Machine Learning is one of the important methods for disease prediction Objective: This study combined different machine learning methods to establish four postnatal depression prediction...

    Background: Machine Learning is one of the important methods for disease prediction Objective: This study combined different machine learning methods to establish four postnatal depression prediction models, and compared their performance to determine the prediction model with best performance . Methods: A total of 1126 pregnant women were enrolled in two women and children’s hospitals. A questionnaire survey was conducted on pregnant women in early pregnancy, second trimester, third trimester. Under the different Feature Selected methods of Expert knowledge method and the Filter Feature Selection algorithm based on random forest, four postnatal depression prediction models were generated by Support Vector Machine and Random Forest algorithm respectively. After optimizing the parameters of the model, a series of indicators are used to evaluate the predictive performance of each model, in order to find the best model. Results: We found that our four postnatal depression prediction models, models built using Random Forest modeling and the filter feature selection algorithm based on random forest feature selection has the best performance. The sensitivity of the model constructed by Support Vector Machine is slightly higher than that of the random forest approach. The importance of psychological resilience is found to be higher than other model factors. Conclusions: The postnatal depression prediction model established by Support Vector Machine proves the value when the sample content is smaller. The two Feature Selection methods have little effect on the performance of postnatal prediction model. Psychological resilience may be an important factor affecting the occurrence of postpartum depression. This application of machine learning may provide a sensitive approach for the the prediction and prevention of postpartum depression.

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