BMJ Health & Care Informatics最新文献

{"title":"Novel machine learning model for predicting multiple unplanned hospitalisations.","authors":"Paul Conilione,&nbsp;Rebecca Jessup,&nbsp;Anthony Gust","doi":"10.1136/bmjhci-2022-100682","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100682","url":null,"abstract":"<p><strong>Background: </strong>In the Australian public healthcare system, hospitals are funded based on the number of inpatient discharges and types of conditions treated (casemix). Demand for services is increasing faster than public funding and there is a need to identify and support patients that have high service usage. In 2016, the Victorian Department of Health and Human Services developed an algorithm to predict multiple unplanned admissions as part of a programme, Health Links Chronic Care (HLCC), that provided capitation funding instead of activity based funding to support patients with high admissions.</p><p><strong>Objectives: </strong>The aim of this study was to determine whether an algorithm with higher performance than previously used algorithms could be developed to identify patients at high risk of three or more unplanned hospital admissions 12 months from discharge.</p><p><strong>Methods: </strong>The HLCC and Hospital Unplanned Readmission Tool (HURT) models were evaluated using 34 801 unplanned inpatient episodes (27 216 patients) from 2017 to 2018 with an 8.3% prevalence of 3 or more unplanned admissions in the following year of discharge.</p><p><strong>Results: </strong>HURT had a higher AUROC (84%, 95% CI 83.4% to 84.9% vs 71%, 95% CI 69.4% to 71.8%) than HLCC, that was statistically significant using Delong test at p<0.05.</p><p><strong>Discussion: </strong>We found features that appear to be strong predictors of admission risk that have not been previously used in models, including socioeconomic status and social support.</p><p><strong>Conclusion: </strong>The high AUROC, moderate sensitivity and high specificity for the HURT algorithm suggests it is a very good predictor of future multi-admission risk and that it can be used to provide targeted support for at-risk individual.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fc/c5/bmjhci-2022-100682.PMC10083802.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9283084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Women's health service access and associated factors in Ethiopia: application of geographical information system and multilevel analysis.","authors":"Addisalem Workie Demsash,&nbsp;Agmasie Damtew Walle","doi":"10.1136/bmjhci-2022-100720","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100720","url":null,"abstract":"<p><strong>Objectives: </strong>Women's access to healthcare services is challenged by various factors. This study aimed to assess women's health service access and identify associated factors.</p><p><strong>Methods: </strong>A cross-sectional study design with a two-stage stratified sampling technique, and 12 945 women from the 2016 Ethiopia Demographic and Health Survey dataset were used. The spatial hotspot analysis and purely Bernoulli-based model scan statistics were used to highlight hot and cold spot areas, and to detect significant local clusters of women's health service access. A multilevel logistic regression analysis was used to assess factors that affect women's access to health services. A variable with a p<o.o5 was considered as a significant factor.</p><p><strong>Results: </strong>Overall, 29.8%% of women had health services access. 70.2% of women had problems with health services access such as: not wanting to go alone (42%), distance to health facilities (51%), getting the money needed for treatment (55%) and getting permission to go for medical care (32.3%). The spatial distribution of health service access in Ethiopia was clustered, and low health service access was observed in most areas of the country. Women who lived in primary, secondary and tertiary clusters were 96%, 39% and 72% more likely to access health services. Educational status, rich wealth status, media exposure and rural residence were statistically significant factors.</p><p><strong>Conclusions: </strong>In Ethiopia, women have problems with health services access. The spatial distribution of health services access was non-random, and hotspot areas of women's health service access were visualised in parts of Benishangul Gumez, Amhara, Afar, DireDawa, Harari, and Somali regions. Creating job opportunities, public health promotion regarding maternal health service utilisation and constructing nearby health facilities are required for better healthcare service access for women.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9409982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving from non-emergency bleeps and long-range pagers to a hospital-wide, EHR-integrated secure messaging system: an implementer report.","authors":"Ari Ercole,&nbsp;Claire Tolliday,&nbsp;William Gelson,&nbsp;James H F Rudd,&nbsp;Ewen Cameron,&nbsp;Afzal Chaudhry,&nbsp;Fiona Hamer,&nbsp;Justin Davies","doi":"10.1136/bmjhci-2022-100706","DOIUrl":"10.1136/bmjhci-2022-100706","url":null,"abstract":"<p><strong>Introduction: </strong>Obsolete bleep/long-range pager equipment remains firmly embedded in the National Health Service (NHS).</p><p><strong>Objective: </strong>To introduce a secure, chart-integrated messaging system (Epic Secure Chat) in a large NHS tertiary referral centre to replace non-emergency bleeps/long-range pagers.</p><p><strong>Methods: </strong>The system was socialised in the months before go-live. Operational readiness was overseen by an implementation group with stakeholder engagement. Cutover was accompanied by a week of Secure Chat and bleeps running in parallel.</p><p><strong>Results: </strong>Engagement due to socialisation was high with usage stabilising approximately 3 months after go-live. Contact centre internal call activity fell significantly after go-live. No significant patient safety concerns were reported.</p><p><strong>Discussion: </strong>Uptake was excellent with substantial utilisation well before cutover indirectly supporting high levels of engagement. The majority of those who previously carried bleeps were content to use personal devices for messaging because of user convenience after reassurance about privacy.</p><p><strong>Conclusion: </strong>An integrated secure messaging system can replace non-emergency bleeps with beneficial impact on service.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9b/19/bmjhci-2022-100706.PMC10580273.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9422752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Routine health information use among healthcare providers in Ethiopia: a systematic review and meta-analysis.","authors":"Maru Meseret Tadele,&nbsp;Tesfahun Melese Yilma,&nbsp;Zeleke Abebaw Mekonnen,&nbsp;Binyam Tilahun","doi":"10.1136/bmjhci-2022-100693","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100693","url":null,"abstract":"<p><strong>Introduction: </strong>Healthcare policy formulation, programme planning, monitoring and evaluation, and healthcare service delivery as a whole are dependent on routinely generated health information in a healthcare setting. Several individual research articles on the utilisation of routine health information exist in Ethiopia; however, each of them revealed inconsistent findings.</p><p><strong>Objective: </strong>The main aim of this review was to combine the magnitude of routine health information use and its determinants among healthcare providers in Ethiopia.</p><p><strong>Methods: </strong>Databases and repositories such as PubMed, Global Health, Scopus, Embase, African journal online, Advanced Google Search and Google Scholar were searched from 20 to 26 August 2022.</p><p><strong>Result: </strong>A total of 890 articles were searched but only 23 articles were included. A total of 8662 (96.3%) participants were included in the studies. The pooled prevalence of routine health information use was found to be 53.7% with 95% CI (47.45% to 59.95%). Training (adjusted OR (AOR)=1.56, 95% CI (1.12 to 2.18)), competency related to data management (AOR=1.94, 95% CI (1.35 to 2.8)), availability of standard guideline (AOR=1.66, 95% CI (1.38 to 1.99)), supportive supervision (AOR=2.07, 95% CI (1.55 to 2.76)) and feedback (AOR=2.20, 95% CI (1.30 to 3.71)) were significantly associated with routine health information use among healthcare providers at p value≤0.05 with 95% CI.</p><p><strong>Conclusion: </strong>The use of routinely generated health information for evidence-based decision-making remains one of the most difficult problems in the health information system. The study's reviewers suggested that the appropriate health authorities in Ethiopia invest in enhancing the skills in using routinely generated health information.</p><p><strong>Prospero registration number: </strong>CRD42022352647.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3d/47/bmjhci-2022-100693.PMC10069504.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9301612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis.","authors":"Patrik Bachtiger,&nbsp;Mihir A Kelshiker,&nbsp;Camille F Petri,&nbsp;Manisha Gandhi,&nbsp;Moulesh Shah,&nbsp;Tahereh Kamalati,&nbsp;Samir Ali Khan,&nbsp;Gareth Hooper,&nbsp;Jon Stephens,&nbsp;Abdullah Alrumayh,&nbsp;Carys Barton,&nbsp;Daniel B Kramer,&nbsp;Carla M Plymen,&nbsp;Nicholas S Peters","doi":"10.1136/bmjhci-2022-100718","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100718","url":null,"abstract":"<p><strong>Background and aims: </strong>Most patients with heart failure (HF) are diagnosed following a hospital admission. The clinical and health economic impacts of index HF diagnosis made on admission to hospital versus community settings are not known.</p><p><strong>Methods: </strong>We used the North West London Discover database to examine 34 208 patients receiving an index diagnosis of HF between January 2015 and December 2020. A propensity score-matched (PSM) cohort was identified to adjust for differences in socioeconomic status, cardiovascular risk and pre-diagnosis health resource utilisation cost. Outcomes were stratified by two pathways to index HF diagnosis: a 'hospital pathway' was defined by diagnosis following hospital admission; and a 'community pathway' by diagnosis via a general practitioner or outpatient services. The primary clinical and health economic endpoints were all-cause mortality and cost-consequence differential, respectively.</p><p><strong>Results: </strong>The diagnosis of HF was via hospital pathway in 68% (23 273) of patients. The PSM cohort included 17 174 patients (8582 per group) and was matched across all selected confounders (p>0.05). The ratio of deaths per person-months at 24 months comparing community versus hospital diagnosis was 0.780 (95% CI 0.722 to 0.841, p<0.0001). By 72 months, the ratio of deaths was 0.960 (0.905 to 1.020, p=0.18). Diagnosis via hospital pathway incurred an overall extra longitudinal cost of £2485 per patient.</p><p><strong>Conclusions: </strong>Index diagnosis of HF through hospital admission continues to dominate and is associated with a significantly greater short-term risk of mortality and substantially increased long-term costs than if first diagnosed in the community. This study highlights the potential for community diagnosis-early, before symptoms necessitate hospitalisation-to improve both clinical and health economic outcomes.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9173537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling admission lengths within psychiatric intensive care units.","authors":"Stephen Dye,&nbsp;Faisil Sethi,&nbsp;Thomas Kearney,&nbsp;Elizabeth Rose,&nbsp;Leia Penfold,&nbsp;Malcolm Campbell,&nbsp;Koravangattu Valsraj","doi":"10.1136/bmjhci-2022-100685","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100685","url":null,"abstract":"<p><strong>Objectives: </strong>To examine whether discharge destination is a useful predictor variable for the length of admission within psychiatric intensive care units (PICUs).</p><p><strong>Methods: </strong>A clinician-led process separated PICU admissions by discharge destination into three types and suggested other possible variables associated with length of stay. Subsequently, a retrospective study gathered proposed predictor variable data from a total of 368 admissions from four PICUs. Bayesian models were developed and analysed.</p><p><strong>Results: </strong>Clinical patient-type grouping by discharge destination displayed better intraclass correlation (0.37) than any other predictor variable (next highest was the specific PICU to which a patient was admitted (0.0585)). Patients who were transferred to further secure care had the longest PICU admission length. The best model included both patient type (discharge destination) and unit as well as an interaction between those variables.</p><p><strong>Discussion: </strong>Patient typing based on clinical pathways shows better predictive ability of admission length than clinical diagnosis or a specific tool that was developed to identify patient needs. Modelling admission lengths in a Bayesian fashion could be expanded and be useful within service planning and monitoring for groups of patients.</p><p><strong>Conclusion: </strong>Variables previously proposed to be associated with patient need did not predict PICU admission length. Of the proposed predictor variables, grouping patients by discharge destination contributed the most to length of stay in four different PICUs.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/14/47/bmjhci-2022-100685.PMC10040048.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9192670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting healthcare professionals' acceptance towards electronic personal health record systems in a resource-limited setting: using modified technology acceptance model.","authors":"Agmasie Damtew Walle,&nbsp;Tigist Andargie Ferede,&nbsp;Nebebe Demis Baykemagn,&nbsp;Aynadis Worku Shimie,&nbsp;Shimels Derso Kebede,&nbsp;Masresha Derese Tegegne,&nbsp;Sisay Maru Wubante,&nbsp;Chalachew Msganaw Yehula,&nbsp;Addisalem Workie Demsash,&nbsp;Mequannent Sharew Melaku,&nbsp;Muluken Belachew Mengistie","doi":"10.1136/bmjhci-2022-100707","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100707","url":null,"abstract":"<p><strong>Objectives: </strong>Personal health record systems allow users to manage their health information in a confidential manner. However, there is little evidence about healthcare providers' intentions to use such technologies in resource-limited settings. Therefore, this study aimed to assess predicting healthcare providers' acceptance of electronic personal health record systems.</p><p><strong>Methods: </strong>An institutional-based cross-sectional study was conducted from 19 July to 23 August 2022 at teaching hospitals in the Amhara regional state of Ethiopia. A total of 638 health professionals participated in the study. Simple random sampling techniques were used to select the study participants. Structural equation modelling analysis was employed using AMOS V.26 software.</p><p><strong>Result: </strong>Perceived ease of use had a significant effect on the intention to use electronic personal health records (β=0. 377, p<0.01), perceived usefulness (β=0.104, p<0.05) and attitude (β=0.204, p<0.01); perceived ease of use and information technology experience had a significant effect on perceived usefulness (β=0.077, p<0.05); and digital literacy (β=0.087, p<0.05) and attitude had also a strong effect on intention to use electronic personal health records (β=0.361, p<0.01). The relationship between perceived ease of use and the intention to use was mediated by attitude (β=0.076, p<0.01).</p><p><strong>Conclusion: </strong>Perceived ease of use, attitude and digital literacy had a significant effect on the intention to use electronic personal health records. The perceived ease of use had a greater influence on the intention to use electronic personal health record systems. Thus, capacity building and technical support could enhance health providers' acceptance of using electronic personal health records in Ethiopia.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9990677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9084513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of decision tree algorithms: effects of air pollution on under five mortality in Ulaanbaatar.","authors":"Akhit Tileubai,&nbsp;Javzmaa Tsend,&nbsp;Bat-Enkh Oyunbileg,&nbsp;Purevdolgor Luvsantseren,&nbsp;Ajnai Luvsan-Ish,&nbsp;Baasandorj Chilhaasuren,&nbsp;Jargalbat Puntsagdash,&nbsp;Galbadrakh Chuluunbaatar,&nbsp;Baatarkhuu Tsagaan","doi":"10.1136/bmjhci-2022-100678","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100678","url":null,"abstract":"<p><strong>Objectives: </strong>4.2 million people die every year from many diseases due to air pollution. The WHO confirms that 92% of the world's population lives in areas where the air quality limit is exceeded. In 251 days of 2011, the concentration of fine particulate matter in Ulaanbaatar exceeded the permissible level by 62%-76%. According to the results of the research, the content of fine particles decreased by 37%-46% in 2019. Because it is harmful to the health of children, we aimed to show the effect of air pollution on the mortality through data mining.</p><p><strong>Methods: </strong>In many countries, research is being conducted to generate effective knowledge from big data using data mining methods. So, we are working to introduce this method to the health sector of Mongolia. In this study, we used the decision tree algorithms.</p><p><strong>Results: </strong>We collected data on air pollution and under five mortality for 2019-2022 in Ulaanbaatar and created the database, built the models using the algorithms, and compared the results with the Mongolian standard. If the average of PM10 in winter is higher than the concentration specified in the standard, the mortality rate is likely to be high. Mortality is likely to be high if the nitrogen dioxide tolerance is high in the spring.</p><p><strong>Conclusion: </strong>The accuracy of the models calculated by the C5.0 algorithm is higher than the determined by the CART algorithm, the sensitivity and specificity values are higher than 0.50, so the mortality rates are uniformly predicted and low mortality prevails.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f8/da/bmjhci-2022-100678.PMC9980318.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10826178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organizational and health professional readiness for the implementation of electronic medical record system: an implication for the current EMR implementation in northwest Ethiopia.","authors":"Tesfahun Melese Yilma,&nbsp;Binyam Tilahun,&nbsp;Adane Mamuye,&nbsp;Hailemariam Kerie,&nbsp;Fedlu Nurhussien,&nbsp;Endalkachew Zemen,&nbsp;Aragaw Mebratu,&nbsp;Tewodros Abebaw,&nbsp;Henok Gebeyehu,&nbsp;Sefiw Abay,&nbsp;Girma Sisay,&nbsp;Redet Getachew,&nbsp;Wondewossen Zemene,&nbsp;Selamsew Tesfaye,&nbsp;Masresha Derese Tegegne","doi":"10.1136/bmjhci-2022-100723","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100723","url":null,"abstract":"<p><strong>Objective: </strong>The WHO developed a manual outlining the preliminary organizational and health professionals' readiness to implement electronic medical records (EMR). On the other hand, the readiness assessment in Ethiopia only includes the evaluation of health professionals, leaving out organisational readiness components. As a result, this research aimed to determine health professionals' and organizational readiness to implement EMR at a specialized teaching hospital.</p><p><strong>Methods: </strong>An institutional-based cross-sectional study design was conducted among 423 health professionals and 54 managers. Self-administered and pretested questionnaires were used to collect data. Binary logistic regression analysis was used to identify factors associated with health professionals' readiness for EMR implementation. An OR with a 95% CI and p<0.05 was used to determine the strength of the association and the statistical significance, respectively.</p><p><strong>Results: </strong>In this study, 53.7% management capacity, 33.3% finance and budget capacity, 42.6% operational capacity, 37.0% technology capability and 53.7% organisational alignment among the five dimensions evaluated to assess an organisation's readiness to implement an EMR system. Of 411 health professionals in this study, 173 (42.1%) with (95 CI 37.3% to 46.8%) were ready to implement an EMR system at the hospital. Sex (AOR 2.69, 95% CI 1.73 to 4.18), basic computer training (AOR 1.59, 95% CI 1.02 to 2.46), knowledge of EMR (AOR 1.88, 95% CI 1.19 to 2.97) and attitudes towards EMR (AOR 1.65, 95% CI 1.05 to 2.59) were significantly associated with health professionals' readiness towards EMR system implementation.</p><p><strong>Conclusions: </strong>Findings showed that most dimensions of organizational readiness for EMR implementation were below 50%. This study also revealed a lower level of EMR implementation readiness among health professionals compared with previous research studies' results. To improve organisational readiness to implement an electronic medical record system, a focus on management capability, financial and budget capability, operational capability, technical capability and organisational alignment was crucial. Likewise, having basic computer training, giving special attention to female health professionals and improving health professionals' knowledge of and attitudes towards EMR could help improve the readiness level of health professionals for implementing an EMR system.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8d/71/bmjhci-2022-100723.PMC9990606.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9131334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the electronic health record in university education: helping health care professionals of the future prepare for 21st century practice.","authors":"Brian McMillan,&nbsp;Gail Davidge,&nbsp;Fatima Nadeem,&nbsp;Dawn Dowding,&nbsp;Kurt Wilson,&nbsp;Angela Davies","doi":"10.1136/bmjhci-2022-100722","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100722","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1c/5b/bmjhci-2022-100722.PMC10016237.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9128422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}