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BACKGROUND: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals.
OBJECTIVE: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout.
METHODS: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management.
RESULTS: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57).
CONCLUSIONS: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout.
BACKGROUND: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

Information technology is one of the most rapidly growing technologies globally. Over the last decade, its usage in healthcare has been remarkable. Over the last decade, its usage in healthcare has been remarkable. The study examines the impact of various factors as barriers to adopting the information system in healthcare. These factors are categorized into three major types: external attacks, which include phishing attacks and ransomware; employee factors, including lack of skills and the issue of information misuse; and technological factors, including complexity and vulnerability. The findings show that external attacks and technological factors are the main barriers to adopting information systems, while employee factors have no significant impact on the adoption of information systems in the healthcare industry of Pakistan. The study provides implications for healthcare policy makers, professionals and organziations regarding the successful adoption of health information system.

Recent decades of improvements to routine health information systems in low- and middle-income countries (LMICs) have increased the volume of health data collected. However, countries continue to face several challenges with quality production and use of information for decision-making at sub-national levels, limiting the value of health information for policy, planning and research. Improving the quality of data production and information use is thus a priority in many LMICs to improve decision-making and health outcomes. This qualitative study identified the challenges of producing and using routine health information in Western Province, Zambia. We analysed the interview responses from 37 health and social sector professionals at the national, provincial, district and facility levels to understand the barriers to using data from the Zambian health management information system (HMIS). Respondents raised several challenges that we categorized into four themes: governance and health system organization, geographic barriers, technical and procedural barriers, and challenges with human resource capacity and staff training. Staff at the facility and district levels were arguably the most impacted by these barriers as they are responsible for much of the labour to collect and report routine data. However, facility and district staff had the least authority and ability to mitigate the barriers to data production and information use. Expectations for information use should therefore be clearly outlined for each level of the health system. Further research is needed to understand to what extent the available HMIS data address the needs and purposes of the staff at facilities and districts.

Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study was motivated by recent attacks on health care organizations that have resulted in the compromise of sensitive data held in HISs. Existing research on cybersecurity in the health care domain places an imbalanced focus on protecting medical devices and data. There is a lack of a systematic way to investigate how attackers may breach an HIS and access health care records.
This study aimed to provide new insights into HIS cybersecurity protection. We propose a systematic, novel, and optimized (artificial intelligence-based) ethical hacking method tailored specifically for HISs, and we compared it with the traditional unoptimized ethical hacking method. This allows researchers and practitioners to identify the points and attack pathways of possible penetration attacks on the HIS more efficiently.
In this study, we propose a novel methodological approach to ethical hacking in HISs. We implemented ethical hacking using both optimized and unoptimized methods in an experimental setting. Specifically, we set up an HIS simulation environment by implementing the open-source electronic medical record (OpenEMR) system and followed the National Institute of Standards and Technology\'s ethical hacking framework to launch the attacks. In the experiment, we launched 50 rounds of attacks using both unoptimized and optimized ethical hacking methods.
Ethical hacking was successfully conducted using both optimized and unoptimized methods. The results show that the optimized ethical hacking method outperforms the unoptimized method in terms of average time used, the average success rate of exploit, the number of exploits launched, and the number of successful exploits. We were able to identify the successful attack paths and exploits that are related to remote code execution, cross-site request forgery, improper authentication, vulnerability in the Oracle Business Intelligence Publisher, an elevation of privilege vulnerability (in MediaTek), and remote access backdoor (in the web graphical user interface for the Linux Virtual Server).
This research demonstrates systematic ethical hacking against an HIS using optimized and unoptimized methods, together with a set of penetration testing tools to identify exploits and combining them to perform ethical hacking. The findings contribute to the HIS literature, ethical hacking methodology, and mainstream artificial intelligence-based ethical hacking methods because they address some key weaknesses of these research fields. These findings also have great significance for the health care sector, as OpenEMR is widely adopted by health care organizations. Our findings offer novel insights for the protection of HISs and allow researchers to conduct further research in the HIS cybersecurity domain.

Overuse and misuse of antibiotics are major factors in the development of antibiotic resistance in primary care institutions of rural China. In this study, the effectiveness of a Health Information System-based, automatic, and confidential antibiotic feedback intervention was evaluated.
A randomized, cross-over, cluster-controlled trial was conducted in primary care institutions. All institutions were randomly divided into two groups and given either a three-month intervention followed by a three-month period without any intervention or vice versa. The intervention consisted of three feedback measures: a real-time pop-up warning message of inappropriate antibiotic prescriptions on the prescribing physician\'s computer screen, a 10-day antibiotic prescription summary, and distribution of educational manuals. The primary outcome was the 10-day inappropriate antibiotic prescription rate.
There were no significant differences in inappropriate antibiotic prescription rates (69.1% vs. 72.0%) between two groups at baseline (P = 0.072). After three months (cross-over point), inappropriate antibiotic prescription rates decreased significantly faster in group A (12.3%, P < 0.001) compared to group B (4.4%, P < 0.001). At the end point, the inappropriate antibiotic prescription rates decreased in group B (15.1%, P < 0.001) while the rates increased in group A (7.2%, P < 0.001). The characteristics of physicians did not significantly affect the rate of antibiotic or inappropriate antibiotic prescription rates.
A Health Information System-based, real-time pop-up warnings, a 10-day prescription summary, and the distribution of educational manuals, can effectively reduce the rates of antibiotic and inappropriate antibiotic prescriptions.

BACKGROUND: The COVID-19 pandemic and its collateral damage severely impact health systems globally and risk to worsen the malaria situation in endemic countries. Malaria is a leading cause of morbidity and mortality in Ghana. This study aims to describe the potential effects of the COVID-19 pandemic on malaria cases observed in health facilities in the Northern Region of Ghana.
METHODS: Monthly routine data from the District Health Information Management System II (DHIMS2) of the Northern Region of Ghana were analysed. Overall outpatient department visits (OPD) and malaria case rates from the years 2015-2019 were compared to the corresponding data of the year 2020.
RESULTS: Compared to the corresponding periods of the years 2015-2019, overall visits and malaria cases in paediatric and adult OPDs in northern Ghana decreased in March and April 2020, when major movement and social restrictions were implemented in response to the pandemic. Cases slightly rebounded afterwards in 2020, but stayed below the average of the previous years. Malaria data from inpatient departments showed a similar but more pronounced trend when compared to OPDs. In pregnant women, however, malaria cases in OPDs increased after the first COVID-19 wave.
CONCLUSIONS: The findings from this study show that the COVID-19 pandemic affects the malaria burden in health facilities of northern Ghana, with declines in inpatient and outpatient rates except for pregnant women. They may have experienced reduced access to insecticide-treated nets and intermittent preventive malaria treatment in pregnancy, resulting in subsequent higher malaria morbidity. Further data, particularly from community-based studies and ideally complemented by qualitative research, are needed to fully determine the impact of the pandemic on the malaria situation in Africa.

OBJECTIVE: Antibiotic overuse is one of the major prescription problems in rural China and a major risk factor for antibiotic resistance. Low antibiotic prescription rates can effectively reduce the risk of antibiotic resistance. We hypothesized that under a paperless, computer-based feedback system the rates of antibiotic prescriptions among primary care physicians can be reduced.
METHODS: A cluster randomized crossover open controlled trial was conducted in 31 hospitals. These hospitals were randomly allocated to two groups to receive the intervention for three months followed by no intervention for three months in a random sequence. The feedback intervention information, which displayed the physicians\' antibiotic prescription rates and ranking, was updated every 10 days. The primary outcome was the 10-day antibiotic prescription rate of the physicians.
RESULTS: There were 82 physicians in group 1 (intervention first followed by control) and 81 in group 2 (control first followed by intervention). Baseline comparison showed no significant difference in antibiotic prescription rate between the two groups (30.8% vs 35.2%, P-value=0.07). At the crossover point, the relative reduction in antibiotic prescription rate was significantly higher among physicians in the intervention group than in the control group (33.1% vs 20.3%, P-value<0.001). After a further 3 months, the rate of decline in antibiotic prescriptions was also significantly greater in the intervention group compared to the control group (14.2% vs 4.6%, P-value<0.001). The characteristics of physicians did not significantly determine the change in rate of antibiotic prescriptions.
CONCLUSIONS: A computer network-based feedback intervention can significantly reduce the antibiotic prescription rates of primary care outpatient physicians and continuously affected their prescription behavior for up to six months.
BACKGROUND: ChiCTR1900021823.

BACKGROUND: Taiwan\'s Diabetes Shared Care Program has been implemented since 2012, and the health information system plays a vital role in supporting most services of this program. However, little is known regarding the effectiveness of this information-based program. Therefore, this study investigated the effects of the participation of the Diabetes Shared Care Program on preventable hospitalizations.
METHODS: This longitudinal study examined the data of health-care claims from 2011 to 2014 obtained from the diabetes mellitus health database. Patients with diabetes aged ≥18 years were included. Preventable hospitalizations were identified on the basis of prevention quality indicators developed for administrative data by the US Agency for Healthcare Research and Quality. A multilevel logistic regression was performed to examine the effects of the participation of the Diabetes Shared Care Program on preventable hospitalizations after adjustment for other variables. Analyses were conducted in late 2018.
RESULTS: A medium level of participation (p = 0.05), age between 40 and 64 years(p < 0.0001), and absence of a catastrophic illness(p < 0.0001) were associated with a lower probability of having a preventable hospitalization. Male sex(p < 0.0001), age ≥ 65 years(p = 0.0203), low income level(p < 0.0001), living in the Southern division(p = 0.0106), and presence of many comorbidities(p < 0.0001) were associated with a higher probability of having a preventable hospitalization after adjustment for characteristics at the individual and county levels.
CONCLUSIONS: The health information system records patients\' medical history, monitors quality of care, schedules patient follow-ups, and reminds case managers to provide timely health education. This health-information-based Diabetes Shared Care Program is associated with better quality care of ambulatory, so it should be promoted on a broader scale.

Immunization averts an expected 2 to 3 million deaths every year from diphtheria, tetanus, pertussis (whooping cough), and measles; however, an additional 1.5 million deaths could be avoided if vaccination coverage was improved worldwide. 11 Data source for immunization records of 1.5 M: http://www.who.int/mediacentre/factsheets/fs378/en/ New vaccination technologies provide earlier diagnoses, personalized treatments and a wide range of other benefits for both patients and health care professionals. Childhood diseases that were commonplace less than a generation ago have become rare because of vaccines. However, 100% vaccination coverage is still the target to avoid further mortality. Governments have launched special campaigns to create an awareness of vaccination. In this paper, we have focused on data mining algorithms for big data using a collaborative approach for vaccination datasets to resolve problems with planning vaccinations in children, stocking vaccines, and tracking and monitoring non-vaccinated children appropriately. Geographical mapping of vaccination records helps to tackle red zone areas, where vaccination rates are poor, while green zone areas, where vaccination rates are good, can be monitored to enable health care staff to plan the administration of vaccines. Our recommendation algorithm assists in these processes by using deep data mining and by accessing records of other hospitals to highlight locations with lower rates of vaccination. The overall performance of the model is good. The model has been implemented in hospitals to control vaccination across the coverage area.