Primary Care Physicians' Experience Using Advanced Electronic Medical Record Features to Support Chronic Disease Prevention and Management: Qualitative Study.
Rahal Rana Melissa,Mercer Jay,Kuziemsky Craig,Yaya Sanni
JMIR medical informatics
BACKGROUND:Chronic diseases are the leading cause of death worldwide. In Canada, more than half of all health care spending is used for managing chronic diseases. Although studies have shown that the use of advanced features of electronic medical record (EMR) systems improves the quality of chronic disease prevention and management (CDPM), a 2012 international survey found that Canadian physicians were the least likely to use 2 or more EMR system functions. Some studies show that maturity vis-à-vis clinicians' EMR use is an important factor when evaluating the use of advanced features of health information systems. The Clinical Adoption Framework (CAF), a common evaluation framework used to assess the success of EMR adoption, does not incorporate the process of maturing. Nevertheless, the CAF and studies that discuss the barriers to and facilitators of the adoption of EMR systems can be the basis for exploring the use of advanced EMR features. OBJECTIVE:This study aimed to explore the factors that primary care physicians in Ontario identified as influencing their use of advanced EMR features to support CDPM and to extend the CAF to include primary care physicians' perceptions of how their use of EMRs for performing clinical tasks has matured. METHODS:Guided by the CAF, directed content analysis was used to explore the barriers and facilitating factors encountered by primary care physicians when using EMR features. Participants were primary care physicians in Ontario, Canada, who use EMRs. Data were coded using categories from the CAF. RESULTS:A total of 9 face-to-face interviews were conducted from January 2017 to July 2017. Dimensions from the CAF emerged from the data, and one new dimension was derived: physicians' perception of their maturity of EMR use. Primary care physicians identified the following key factors that impacted their use of advanced EMR features: performance of EMR features, information quality of EMR features, training and technical support, user satisfaction, provider's productivity, personal characteristics and roles, cost benefits of EMR features, EMR systems infrastructure, funding, and government leadership. CONCLUSIONS:The CAF was extended to include physicians' perceptions of how their use of EMR systems had matured. Most participants agreed that their use of EMR systems for performing clinical tasks had evolved since their adoption of the system and that certain system features facilitated their care for patients with chronic diseases. However, several barriers were identified and should be addressed to further enhance primary care physicians' use of advanced EMR features to support CDPM.
10.2196/13318
Facts and figures on medical record management from a multi super specialty hospital in Delhi NCR: A descriptive analysis.
Journal of family medicine and primary care
AIM OF STUDY:A study of the medical records department of a multi super specialty secondary care hospital in NCR. MATERIALS AND METHODS:Primary data was collected through direct observation and retrospective study of documents maintained in MRD. Secondary data was collected from quality control department books, journals, scholarly articles, and internet. RESULTS AND CONCLUSION:Sample sizes of 350 retrospective and current medical records were thoroughly scrutinized. Conclusion revealed the hospital has published as exhaustive medical records manual listing and the scope, objective, hierarchy chart, job description, policies, procedures, and processes. The MRD has a well-documented flow process of medical records, but on checking the flow of patient records between Nov 2016 to Feb 2017; it was revealed that in month of Nov 2016, out of the total 278 patients discharged only 276 files were received in MRD and 0.72% files were not received. Moreover, it took over 31 days for 71 patients (23.67%) to receive files in MRD. In Jan 2017, out of 286 patients discharged, only 237 files were received in MRD contrasting to 10.14% files not received. Moreover, it took over 31 days for 28 patients (9.80%) to receive files in MRD. In Feb 2017, out of 268 patients discharged, only 206 files were received in MRD and 22.39% files were not received as on 11 March 2017. This study concluded that there is no effective system in place to monitor/track files from ward/billing section to MRD once the patient is discharged. CLINICAL SIGNIFICANCE:Medical records are valuable to patients, physicians, healthcare institutions, researchers, National Health agencies, and International health organizations. Memories fade, people lie, witnesses die; however, medical records live forever. A thorough system of flow process of monitoring/tracking files is to be in place to ensure accountability, smooth functioning, and quality of care being provided without violating basic patient sight of confidentiality of information.
10.4103/jfmpc.jfmpc_612_19
Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study.
Li Rui,Niu Yue,Scott Sarah Robbins,Zhou Chu,Lan Lan,Liang Zhigang,Li Jia
JMIR medical informatics
BACKGROUND:With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China. OBJECTIVE:This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data. METHODS:For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests. RESULTS:From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff. CONCLUSIONS:The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.
10.2196/24405
Effective data quality management for electronic medical record data using SMART DATA.
International journal of medical informatics
OBJECTIVES:In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer. METHODS:We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data. RESULTS:We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction. CONCLUSIONS:In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.
10.1016/j.ijmedinf.2023.105262
Blockchain vehicles for efficient Medical Record management.
NPJ digital medicine
The lack of interoperability in Britain's medical records systems precludes the realisation of benefits generated by increased spending elsewhere in healthcare. Growing concerns regarding the security of online medical data following breaches, and regarding regulations governing data ownership, mandate strict parameters in the development of efficient methods to administrate medical records. Furthermore, consideration must be placed on the rise of connected devices, which vastly increase the amount of data that can be collected in order to improve a patient's long-term health outcomes. Increasing numbers of healthcare systems are developing Blockchain-based systems to manage medical data. A Blockchain is a decentralised, continuously growing online ledger of records, validated by members of the network. Traditionally used to manage cryptocurrency records, distributed ledger technology can be applied to various aspects of healthcare. In this manuscript, we focus on how Electronic Medical Records in particular can be managed by Blockchain, and how the introduction of this novel technology can create a more efficient and interoperable infrastructure to manage records that leads to improved healthcare outcomes, while maintaining patient data ownership and without compromising privacy or security of sensitive data.
10.1038/s41746-019-0211-0
Application of DRGs in hospital medical record management and its impact on service quality.
International journal for quality in health care : journal of the International Society for Quality in Health Care
BACKGROUND:To explore the application of diagnosis-related groups (DRGs) in hospital medical record management and the impact on service quality. OBJECTIVE:This study introduced DGRs management into hospital medical record management in order to improve the quality of hospital medical record management. METHOD:The medical record management of our hospital was analysed retrospectively between August 2020 and April 2021. A total of 7263 cases without DRG management before January 2021 were included in a control group, and 7922 cases with DRG management after January 2021 were included in a study group. The error rate of medical records, the specific error items and the scores of service capability, service efficiency and service quality were compared along with the comprehensive scores of the two groups. RESULTS:The error rate of medical records in the study group was significantly lower than that in the control group (19.35% vs. 31.24%, P < 0.05). The error rates in terms of diagnosis on admission, surgical procedures, main diagnosis and other diagnoses in the study group were significantly lower than those in the control group. The scores for service ability, service efficiency and service quality were significantly higher in the study group than in the control group (P < 0.05). The comprehensive evaluation score of the study group was significantly higher than that of the control group (P < 0.01). CONCLUSION:Applying DRGs in the hospital medical record management can effectively reduce the error rate of medical records and improve the quality of hospital services.
10.1093/intqhc/mzac090
Artificial Intelligence Algorithm with ICD Coding Technology Guided by the Embedded Electronic Medical Record System in Medical Record Information Management.
Wang Cheng,Yao Chenlong,Chen Pengfei,Shi Jiamin,Gu Zhe,Zhou Zheying
Journal of healthcare engineering
The study aims to explore the application of international classification of diseases (ICD) coding technology and embedded electronic medical record (EMR) system. The study established an EMR information knowledge system and collected the data of patient medical records and disease diagnostic codes on the front pages of 8 clinical departments of endocrinology, oncology, obstetrics and gynecology, ophthalmology, orthopedics, neurosurgery, and cardiovascular medicine for statistical analysis. Natural language processing-bidirectional recurrent neural network (NLP-BIRNN) algorithm was used to optimize medical records. The results showed that the coder was not clear about the basic rules of main diagnosis selection and the classification of disease coding and did not code according to the main diagnosis principles. The disease was not coded according to different conditions or specific classification, the code of postoperative complications was inaccurate, the disease diagnosis was incomplete, and the code selection was too general. The solutions adopted were as follows: communication and knowledge training should be strengthened for coders and medical personnel. BIRNN was compared with the convolutional neural network (CNN) and recurrent neural network (RNN) in accuracy, symptom accuracy, and symptom recall, and it suggested that the proposed BIRNN has higher value. Pathological language reading under artificial intelligence algorithm provides some convenience for disease diagnosis and treatment.
10.1155/2021/3293457