4,614 research outputs found

    Implementation Assessment of Electronic Records Management System in Bayelsa State, Nigeria

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    The quality of information available to medical practitioners when delivering treatment to patients influences the outcome of service delivery. In the healthcare industry, health information technology has been shown to improve patient safety and treatment quality. The purpose of this research is to identify the facilitators and hurdles to the deployment of an electronic records management system at NDUTH, Okolobiri. For this study, a descriptive cross-sectional research design was used. The stratified random sample was taken from the 204 staff members that participated in the survey at NDUTH Okolobiri. Telephone interviews were also conducted with a small number of hospital staff who had deployed the electronic records system. According to the findings, the majority of participants (51.0%) were female, with an average age of 37(SD+9.0) years. The respondentsโ€™ overall understanding of electronic records management systems was 45.9%. The overall implementation rate was 22.8%. The studyโ€™s facilitators were leadership support along with the availability of ICT equipment, whereas the barriers were funding, a lack of power, insufficient ICT infrastructures, administrative challenges, poor staff compliance, a lack of government support, and poor maintenance of software and ICT equipment. The findings indicate that government and private enterprises should invest more in healthcare delivery via electronic records management systems. This is critical because the quality of information that medical practitioners have access to when caring for patients influences the effectiveness of health service delivery. It has also been discovered to ensure the proper operation of health institutions

    Digital technologies and information translucence in healthcare management: An institutional theory perspective for adopting electronic incidence reporting systems

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    The purpose of this study was to provide an institutional theory perspective on the adoption of electronic IRS technology for healthcare management. This research employs institutional theory to investigate the adoption of electronic IRS for healthcare management. The studyโ€™s conceptual analysis demonstrates that coercive, normative, and imitative forces influence the adoption of electronic IRS for healthcare management. International healthcare regulations and standards reflect the presence of coercive forces. International healthcare societies and professional networks mirror normative forces. Imitative forces exert pressure on smaller enterprises and developing nations to adopt electronic IRS. This research contributes to the literature and theory by extending the application of institutional theory to the adoption of digital technologies such as the electronic IRS. In addition, the study has practical implications because it demonstrates the importance of digital technologies such as electronic IRS for information translucence and healthcare management. Small businesses in developing nations can learn from large businesses in developed nations to adopt electronic IRS for efficient and effective healthcare management

    Electronic Records Management System Adoption Readiness Framework for Higher Professional Education Institutions in Yemen

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    Electronic records (e-records) are used to provide proof of organizational activities. E-records are crucial in complementing business functions, essential tool to assess organizational performance and are the core of good governance. E-records in Higher Professional Education (HPE) institutions contain valuable information in running the education business in an efficient and effective manner, supplying services consistently and in supporting effective performance evaluation and decisions. There are serious consequences and risk awaiting when the administrators of HPE are not based on information contained in e-records in making decisions. Well-informed decision makings would thus be impossible if electronic records are not efficiently and effectively managed using system. Therefore, Electronic Records Management System (ERMS) is an effective and efficient tool to hinder such a problem. Voluminous electronic records are created every day in HPE. The record keepers inclusive of records managers, archivists, administrators and IT personnel, who are the people essentially involved in creating, maintaining and preserving the contents of the e-records.ย  Thus, these personnel participatinginthe records keeping should identify the readiness of the HPE institutions to adopt ERMS. Therefore, the aim of this paper is to investigate the readiness of the Yemeni HPE institutions to adopt the ERMS. The study involves interviewing 20 specialists from Yemeni HPE institutions who are involved in ERMS. The findings showed that in order to promote effective ERMS readiness in the HPE institutions, there should be a framework to be used as guidance in such process

    Lessons From Healthcare Providers' Attitudes Toward Pay-for-performance: What Should Purchasers Consider in Designing and Implementing a Successful Program?

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    We conducted a systematic review to summarize providers' attitudes toward pay-for-performance (P4P), focusing on their general attitudes, the effects of P4P, their favorable design and implementation methods, and concerns. An electronic search was performed in PubMed and Scopus using selected keywords including P4P. Two reviewers screened target articles using titles and abstract review and then read the full version of the screened articles for the final selections. In addition, one reference of screened articles and one unpublished report were also included. Therefore, 14 articles were included in this study. Healthcare providers' attitudes on P4P were summarized in two ways. First, we gathered their general attitudes and opinions regarding the effects of P4P. Second, we rearranged their opinions regarding desirable P4P design and implementation methods, as well as their concerns. This study showed the possibility that some healthcare providers still have a low level of awareness about P4P and might prefer voluntary participation in P4P. In addition, they felt that adequate quality indicators and additional support for implementation of P4P would be needed. Most healthcare providers also had serious concerns that P4P would induce unintended consequences. In order to conduct successful implementation of P4P, purchaser should make more efforts such as increasing providers' level of awareness about P4P, providing technical and educational support, reducing their burden, developing a cooperative relationship with providers, developing more accurate quality measures, and minimizing the unintended consequences

    Approach for Electronic Medical Record Data Analysis

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    As the healthcare environment is being digitalized and changed rapidly, research using medical big data is increasing. One of the most applicable data is electronic medical records which can provide a large amount of clinically practical meaning. Electronic medical data include patient's demographic information, laboratory test results, imaging and biosignal data. In this article, we provide support for a wide variety of researchers in their efforts to use electronic medical record data accurately and usefully in their work. From the basic concept of the research using electronic medical records to challenging aspects like data integration between multiple institutions are described. Also, examples of each type of data are covered; structured such as numeric data and unstructured such as images, biosignals and narrative text. Using these kinds of electronic medical records, analyses are processed by data cleansing, transforming, and reducing in order. Many kinds of variables such as the exposure and outcome of interest, covariate and the research design can be chosen during the preprocessing. As many machine-learning-based studies as well as epidemiologic-based studies have been conducted using electronic medical records, various research frameworks have been proposed. However, data quality management and data standardization for multi-center data analysis are still remaining as challenging tasks.ope

    The Impacts of Role Overload and Role Conflict on Physicians\u27 Technology Adoption

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    Technology adoption is an important solution for physicians to increase work efficiency, and thus deal with role conflict among their multiple job roles. Prior studies have not investigated how multiple job roles and role conflict influence physiciansโ€™ technology adoption intentions. Based on role strain theory and role identity theory, we present a model of physiciansโ€™ technology adoption intentions to support their primary (clinical care) versus secondary (teaching or research) job roles. We test the model using surveys with 156 physicians at nine medical schools in Korea. The results of our data analysis largely support our hypotheses. Role overload in each of their job roles increases role conflict between any pair of associated roles. Furthermore, role conflict between a physicianโ€™s primary and secondary role is affected more by role overload in the secondary role than by overload in the primary role. Moreover, the impact of role conflict on technology adoption intentions is also influenced by the hierarchical relationship between two roles. This study contributes to technology adoption research by demonstrating how physiciansโ€™ job characteristics affect technology adoption

    Contrast-Induced Nephropathy in Patients Undergoing Intravenous Contrast-Enhanced Computed Tomography in Korea: A Multi-Institutional Study in 101487 Patients

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    OBJECTIVE: To evaluate the prevalence of known risk factors for contrast-induced nephropathy (CIN) and their association with the actual occurrence of CIN in patients undergoing intravenous contrast-enhanced computed tomography (CECT) in Korea. MATERIALS AND METHODS: Patients who underwent CECT in 2008 were identified in the electronic medical records of 16 tertiary hospitals of Korea. Data on demographics, comorbidities, prescriptions and laboratory test results of patients were collected following a standard data extraction protocol. The baseline renal function was assessed using the estimated glomerular filtration rate (eGFR). We identified the prevalence of risk factors along the eGFR strata and evaluated their influence on the incidence of CIN, defined as a 0.5 mg/dL or 25% increase in serum creatinine after CECT. RESULTS: Of 432425 CECT examinations in 272136 patients, 140838 examinations in 101487 patients met the eligibility criteria for analysis. The mean age of the participants was 57.9 ยฑ 15.5 years; 25.1% of the patients were older than 70 years. The prevalence of diabetes mellitus was 11.9%, of hypertension 13.7%, of gout 0.55% and of heart failure was 1.7%. Preventive measures were used in 40238 CECT examinations (28.6%). The prevalence of risk factors and use of preventive measures increased as the renal function became worse. A CIN was occurred after 3103 (2.2%) CECT examinations, revealing a significant association with decreased eGFR, diabetes mellitus, and congestive heart failure after adjustment. CONCLUSION: Risk factors for CIN are prevalent among the patients undergoing CECT. Preventive measures were seemingly underutilized and a system is needed to improve preventive care.ope

    ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ๊ณผ ๋ฉ”ํƒ€๋ถ„์„์„ ํ†ตํ•œ ์ „์‚ฐ์ฒ˜๋ฐฉ์ž๋™ํ™”์‹œ์Šคํ…œ๊ณผ ๊ด€๋ จ๋œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ํ‰๊ฐ€ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2020. 8. ๊น€์€๊ฒฝ.Computerized Physician Order Entry (CPOE) systems and Clinical Decision Support Systems (CDSS) have been proven to contribute to improve patients safety and quality of care; however, the adoption of computerization introduced a new type of error, called system-related or technology-induced errors. A comprehensive evaluation regarding the prevalence of CPOE-related errors (CRE) is lacking. The aim of this study was to describe the prevalence of CRE evaluated by pharmacists and to evaluate the association between the introduction of CPOE and prescribing errors. A systematic review and meta-analysis were conducted of studies retrieved from the MEDLINE, Embase, Cochrane, and Scopus up to March 2020. All studies reporting the rate of prescribing errors related to CPOE were included. The prevalence of CRE among overall prescribing errors occurred in the hospitals was estimated using pooled prevalence estimate with a 95% confidence interval (CI) and relative risk (RR) was calculated for the subgroup analysis. A total of 14 studies were identified and included in the systematic review and meta-analysis. In the meta-analysis of 13 data of estimate, the overall pooled prevalence of CRE across studies were 32.36% (95% CI 22.87 โ€“ 42.62). Among the 6 types of error identified throughout the studies: omission, wrong drug, wrong dose, wrong route/form, wrong time, and monitoring error, the main type of error related to CPOE were wrong dose (47.28%, 95% CI 38.38-56.26), followed by wrong drug (14.45%, 95% CI 7.96-22.40). The subgroup analysis revealed that the risk of error was not significantly reduced with CPOE (RR 0.842, 95% CI 0.559 โ€“ 1.268), except omission which was significantly reduced after the implementation of CPOE (RR 0.484, 95% CI 0.282 โ€“ 0.831). Our study findings support that system-related errors were a major reason for CPOE not delivering a significant reduction in the overall rate of clinical errors. A considerable risk for prescribing errors still exists, which healthcare professionals should be aware that CPOE could also lead to a new type of medication errors. In order to reduce the prescribing error related to CPOE, the system should be continually examined and users should receive periodic and multidisciplinary training on the use of CPOE and CDSS.์ฒ˜๋ฐฉ์ž๋™ํ™”์‹œ์Šคํ…œ(Computerized Physician Order Entry, CPOE)๊ณผ ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์ง€์›์‹œ์Šคํ…œ(Clinical Decision Support System)์˜ ํ™œ์„ฑํ™”๋กœ ์ „์ฒด์ ์ธ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ๋น„์œจ์€ ๊ฐ์†Œํ•˜์˜€์ง€๋งŒ, CPOE์™€ ๊ฐ™์€ ์ƒˆ๋กœ์šด ์‹œ์Šคํ…œ์œผ๋กœ ์ธํ•˜์—ฌ ์ƒˆ๋กœ์šด ์˜ค๋ฅ˜๊ฐ€ ์ถœํ˜„๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์›๋‚ด CPOE์™€ ๊ด€๋ จ๋œ ์•ฝ๋ฌผ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ์ค‘ ์•ฝ์‚ฌ๊ฐ€ ํ‰๊ฐ€ํ•œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ๋ฐœ์ƒ๋ฅ ๊ณผ CPOE ๋„์ž… ์ „ํ›„ ์˜ค๋ฅ˜์œ ํ˜•์˜ ๋ณ€ํ™”๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ์„ ํ–‰์—ฐ๊ตฌ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ๊ณผ ๋ฉ”ํƒ€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. PubMed, EMBASE, Cochrane Register of Controlled Trials, Scopus์—์„œ 2020๋…„ 3์›”๊นŒ์ง€ ๊ฒ€์ƒ‰๋˜๋Š” ๋ฌธํ—Œ ์ค‘ CPOE ๋„์ž… ํ›„ ๋ฐœ์ƒํ•œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์— ํ•ด๋‹นํ•˜๋Š” ๋ฌธํ—Œ์„ ์ถ”์ถœํ•˜์˜€๊ณ  ์„ ์ • ๋ฐ ์ œ์™ธ๊ธฐ์ค€์— ๋”ฐ๋ผ ์ด 14๊ฐœ์˜ ์ตœ์ข… ๋ฌธํ—Œ์„ ์„ ์ •ํ•˜์˜€๋‹ค. ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ํ•ฉ๋™ ๋ฐœ์ƒ๋ฅ  ์ˆ˜์น˜์™€ CPOE ๋„์ž… ์ „๊ณผ ํ›„ ์œ ํ˜• ๋ณ„ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ๋ฐœ์ƒ์˜ ์ƒ๋Œ€ ์œ„ํ—˜๋„ ๋ฐ 95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„์€ ๋žœ๋ค ํšจ๊ณผ ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ ์ œ์‹œํ•˜์˜€๋‹ค. CPOE ๋„์ž… ํ›„ ์ „์ฒด ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ์ค‘ CPOE๋กœ ์ธํ•œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ๋ฐœ์ƒ๋ฅ  ์ถ”์ •์น˜ ๋ฒ”์œ„๋Š” 12.78%์—์„œ 58.54% ์‚ฌ์ด์˜€๊ณ  ๋žœ๋ค ํšจ๊ณผ ๋ชจ๋ธ์—์„œ ๊ณ„์‚ฐ๋œ ํ•ฉ๋™ ๋ฐœ์ƒ๋ฅ ์€ 32.36%์˜€๋‹ค (95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„ 22.87-42.62). National Coordinating Council for Medication Error Reporting and Prevention ๋ถ„๋ฅ˜์ฒด๊ณ„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๋ฌธํ—Œ์—์„œ ์ถ”์ถœ ๊ฐ€๋Šฅํ•œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ์œ ํ˜•์„ ์ฒ˜๋ฐฉ ๋ˆ„๋ฝ์˜ค๋ฅ˜, ์•ฝ๋ฌผ ์˜ค๋ฅ˜, ์šฉ๋Ÿ‰์˜ค๋ฅ˜, ์ œํ˜• ๋ฐ ํˆฌ์—ฌ๊ฒฝ๋กœ ์˜ค๋ฅ˜, ํˆฌ์—ฌ ์‹œ๊ฐ„ ์˜ค๋ฅ˜, ์•ฝ๋ฌผ ๋ชจ๋‹ˆํ„ฐ๋ง๊ณผ ๊ฐ™์ด ์ด 6๊ฐœ ์œ ํ˜•์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€์„ ๋•Œ, ์šฉ๋Ÿ‰์˜ค๋ฅ˜๊ฐ€ 47.28% (95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„ 38.38-56.26)๋กœ ๊ฐ€์žฅ ๋†’์•˜๊ณ  ๊ทธ ๋‹ค์Œ์€ ์•ฝ๋ฌผ ์˜ค๋ฅ˜๊ฐ€ 14.45% (95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„ 7.96-22.40)์œผ๋กœ ๋†’์•˜๋‹ค. CPOE ๋„์ž… ์ „๊ณผ ํ›„์˜ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ์œ ํ˜•๋ณ„ ๋ฐœ์ƒ์„ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ•˜์œ„๊ทธ๋ฃน ๋ฉ”ํƒ€ ๋ถ„์„์„ ํ•˜์˜€์„ ๋•Œ, CPOE ๋„์ž… ํ›„ ์ „์ฒด์ ์ธ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ๋ฐœ์ƒ๋ฅ ์€ CPOE ๋„์ž… ์ „์— ๋น„ํ•ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜์ง€ ์•Š์•˜์œผ๋‚˜ (Relative risk, RR 0.842, 95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„ 0.559-1.168), 6๊ฐœ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ์œ ํ˜• ์ค‘ ๋ฉ”ํƒ€๋ถ„์„์ด ๊ฐ€๋Šฅํ•œ 5๊ฐœ ์˜ค๋ฅ˜ ์œ ํ˜• ์ค‘ (์ฒ˜๋ฐฉ ๋ˆ„๋ฝ์˜ค๋ฅ˜, ์•ฝ๋ฌผ ์˜ค๋ฅ˜, ์šฉ๋Ÿ‰์˜ค๋ฅ˜, ์ œํ˜• ๋ฐ ํˆฌ์—ฌ๊ฒฝ๋กœ ์˜ค๋ฅ˜, ์•ฝ๋ฌผ ๋ชจ๋‹ˆํ„ฐ๋ง) ์ฒ˜๋ฐฉ ๋ˆ„๋ฝ์˜ค๋ฅ˜๋งŒ CPOE ๋„์ž… ํ›„ ์œ ์˜ํ•˜๊ฒŒ ์ค„์–ด๋“ค์—ˆ๋‹ค (RR 0.484, 95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„ 0.282-0.831). ์ฒด๊ณ„์  ๋ฌธํ—Œ๊ณ ์ฐฐ ๋ฐ ๋ฉ”ํƒ€๋ถ„์„์„ ํ†ตํ•ด ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์ธ CPOE ๋„์ž… ํ›„ CPOE์™€ ๊ด€๋ จ๋œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜๊ฐ€ ์ „์ฒด ์ฒ˜๋ฐฉ์˜ค๋ฅ˜ ์ค‘ 1/3์˜ ๋นˆ๋„๋กœ ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํŒŒ์•…๋˜์—ˆ๋‹ค. ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ์œ ํ˜• ์ค‘ ์ฒ˜๋ฐฉ ๋ˆ„๋ฝ์˜ค๋ฅ˜, ์•ฝ๋ฌผ ์˜ค๋ฅ˜, ์šฉ๋Ÿ‰์˜ค๋ฅ˜, ์ œํ˜• ๋ฐ ํˆฌ์—ฌ๊ฒฝ๋กœ ์˜ค๋ฅ˜, ์•ฝ๋ฌผ ๋ชจ๋‹ˆํ„ฐ๋ง์˜ ์˜ค๋ฅ˜์˜ ๋ฐœ์ƒ ๋น„์œจ์€ CPOE ๋„์ž… ์ „๊ณผ ํ›„์— ์œ ์˜ํ•œ ๋ณ€ํ™”๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜์œผ๋‚˜, ์ฒ˜๋ฐฉ ๋ˆ„๋ฝ์˜ ๋น„์œจ์€ CPOE ๋„์ž… ํ›„์— ๋‚ฎ์•„์ง„ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์•ฝ๋ฌผ์ฒ˜๋ฐฉ์˜ ์ „์žํ™”์™€ ์ฒ˜๋ฐฉ ์ง€์› ์‹œ์Šคํ…œ๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์˜ ๋„์ž…์œผ๋กœ ๋‹จ์ˆœ ์‹ค์ˆ˜๋กœ ์ธํ•œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜๋Š” ๋ฐฉ์ง€๋˜์—ˆ์œผ๋‚˜ ๋‹ค์–‘ํ•œ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜๊ฐ€ ์ง€์†ํ•ด์„œ ๋ฐœ์ƒํ•จ์œผ๋กœ ํ™˜์ž์˜ ์•ˆ์ „์„ ์œ„ํ•œ ์‹œ์Šคํ…œ ์‚ฌ์šฉ์ž์˜ ์ง€์†์ ์ธ ๊ต์œก๊ณผ ์‹œ์Šคํ…œ์˜ ๊ธฐ์ˆ ์  ๊ฐœ์„ ์œผ๋กœ ์ฒ˜๋ฐฉ์˜ค๋ฅ˜์˜ ์˜ˆ๋ฐฉ, ๊ฐ์ง€, ๋ฐ ๋ชจ๋‹ˆํ„ฐ๋ง์˜ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค.1. Introduction 1 2. Methods 3 3. Results 8 4. Discussion 25 5. Conclusion 31 References 32 Appendix 40 ์š”์•ฝ (๊ตญ๋ฌธ์ดˆ๋ก) 48Maste
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