The role of medical data in efficient patient care delivery: a review

Citizen Views on an Opt-Out Approach to National Electronic Health Records in Germany: A Small-Scale Qualitative Study

Objectives

Electronic health records (German: elektronische Patientenakte - ePA) are an important healthcare tool. However, in Germany, current participation remains low for their national ePA. To rectify this, the German government recently adopted an opt-out approach to their national ePA system. The objective of this study is to investigate and provide a brief overview of German public attitudes towards this approach to inform policymakers with evidence-based insights.

Methods

Four public focus groups were conducted with 12 German citizens to discuss their opinions on the German governments new opt-out approach to the ePA.

Results

Three major thematic categories were identified (Contributors to Opt-Out Implementation, Barriers to Opt-Out Implementation, and Contingent Factors) to describe citizen views on the opt-out approach for the ePA.

Conclusion

The public is generally supportive of an opt-out approach to ePAs in Germany, as they see the benefits ePAs can provide to German society; but they are skeptical on how successful this approach might be due to extant issues that policymakers must be aware of in order to successfully implement an opt-out approach for Germany's national ePA system.

A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort

Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study's findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability.

Factors Associated with Online Patient Portal Utilization Experience in an Arkansas Phone Survey

Introduction: Accessing electronic health record information through a patient portal is associated with numerous benefits to both health care providers and patients. However, patient portal utilization remains low. Little is known about the factors associated with patient portal utilization following the onset of the COVID-19 pandemic. Methods: In March 2022, we conducted a random digit dial phone survey of both cell phones and landlines of adults living in Arkansas that asked numerous demographic and health-related measures, including patient portal utilization in the past 12 months. A total of 2,201 adult Arkansans completed the survey between March 1 and March 28, 2022. Weighted estimates were generated using rank ratio estimation to approximate the 2019 American Community Survey 1-year Arkansas estimates for race/ethnicity (72% White, 15% Black/African American, 7.8% Hispanic, 4.9% other race/ethnicity), age (73% 18-39, 32% 40-59, and 31% 60+), and gender (49% male, 51% female). We fit the data to a logistic regression model. Results: We found that education, employment, prior telehealth experience, having a check-up in the past 2 years, and having a primary care provider were all positively associated with patient portal utilization. We also found that non-Hispanic Black/African-American respondents were less likely to access a patient portal relative to non-Hispanic White respondents. Discussion: Patient portal utilization is related to several demographic and health-related factors among an adult population in Arkansas. Given that the documented benefits of patient portal utilization are broad, under-utilization by groups that already experience relatively worse health outcomes could reproduce or even exacerbate existing health disparities. Additional research is needed to further investigate what barriers to patient portal utilization remain for these populations.

Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing

BACKGROUND

Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care. However, limitations of EHR-based research include challenges in abstracting key clinical variables from non-structured data at scale. This is further compounded by missing data. Here we develop a natural language processing (NLP) model that automatically reads EHR notes to determine the NIH stroke scale (NIHSS) score of patients with acute stroke.

METHODS

The study included notes from acute stroke patients (>= 18 years) admitted to the Massachusetts General Hospital (MGH) (2015-2022). The MGH data were divided into training (70%) and hold-out test (30%) sets. A two-stage model was developed to predict the admission NIHSS. A linear model with the least absolute shrinkage and selection operator (LASSO) was trained within the training set. For notes in the test set where the NIHSS was documented, the scores were extracted using regular expressions (stage 1), for notes where NIHSS was not documented, LASSO was used for prediction (stage 2). The reference standard for NIHSS was obtained from Get With The Guidelines Stroke Registry. The two-stage model was tested on the hold-out test set and validated in the MIMIC-III dataset (Medical Information Mart for Intensive Care-MIMIC III 2001-2012) v1.4, using root mean squared error (RMSE) and Spearman correlation (SC).

RESULTS

We included 4,163 patients (MGH = 3,876; MIMIC = 287); average age of 69 [SD 15] years; 53% male, and 72% white. 90% patients had ischemic stroke and 10% hemorrhagic stroke. The two-stage model achieved a RMSE [95% CI] of 3.13 [2.86-3.41] (SC = 0.90 [0.88-0. 91]) in the MGH hold-out test set and 2.01 [1.58-2.38] (SC = 0.96 [0.94-0.97]) in the MIMIC validation set.

CONCLUSIONS

The automatic NLP-based model can enable large-scale stroke severity phenotyping from EHR and therefore support real-world quality improvement and comparative effectiveness studies in stroke.

Digital health and health equity: How digital health can address healthcare disparities and improve access to quality care in Africa

The healthcare industry is constantly evolving to bridge the inequality gap and provide precision care to its diverse population. One of these approaches is the integration of digital health tools into healthcare delivery. Significant milestones such as reduced maternal mortality, rising and rapidly proliferating health tech start-ups, and the use of drones and smart devices for remote health service delivery, among others, have been reported. However, limited access to family planning, migration of health professionals, climate change, gender inequity, increased urbanization, and poor integration of private health firms into healthcare delivery rubrics continue to impair the attainment of universal health coverage and health equity. Health policy development for an integrated health system without stigma, addressing inequalities of all forms, should be implemented. Telehealth promotion, increased access to infrastructure, international collaborations, and investment in health interventions should be continuously advocated to upscale the current health landscape and achieve health equity.

Ensemble machine learning reveals key features for diabetes duration from electronic health records

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.

The Environmental Impacts of Electronic Medical Records Versus Paper Records at a Large Eye Hospital in India: Life Cycle Assessment Study

BACKGROUND

Health care providers worldwide are rapidly adopting electronic medical record (EMR) systems, replacing paper record-keeping systems. Despite numerous benefits to EMRs, the environmental emissions associated with medical record-keeping are unknown. Given the need for urgent climate action, understanding the carbon footprint of EMRs will assist in decarbonizing their adoption and use.

OBJECTIVE

We aimed to estimate and compare the environmental emissions associated with paper medical record-keeping and its replacement EMR system at a high-volume eye care facility in southern India.

METHODS

We conducted the life cycle assessment methodology per the ISO (International Organization for Standardization) 14040 standard, with primary data supplied by the eye care facility. Data on the paper record-keeping system include the production, use, and disposal of paper and writing utensils in 2016. The EMR system was adopted at this location in 2018. Data on the EMR system include the allocated production and disposal of capital equipment (such as computers and routers); the production, use, and disposal of consumable goods like paper and writing utensils; and the electricity required to run the EMR system. We excluded built infrastructure and cooling loads (eg. buildings and ventilation) from both systems. We used sensitivity analyses to model the effects of practice variation and data uncertainty and Monte Carlo assessments to statistically compare the 2 systems, with and without renewable electricity sources.

RESULTS

This location's EMR system was found to emit substantially more greenhouse gases (GHGs) than their paper medical record system (195,000 kg carbon dioxide equivalents [CO2e] per year or 0.361 kg CO2e per patient visit compared with 20,800 kg CO2e per year or 0.037 kg CO2e per patient). However, sensitivity analyses show that the effect of electricity sources is a major factor in determining which record-keeping system emits fewer GHGs. If the study hospital sourced all electricity from renewable sources such as solar or wind power rather than the Indian electric grid, their EMR emissions would drop to 24,900 kg CO2e (0.046 kg CO2e per patient), a level comparable to the paper record-keeping system. Energy-efficient EMR equipment (such as computers and monitors) is the next largest factor impacting emissions, followed by equipment life spans. Multimedia Appendix 1 includes other emissions impact categories.

CONCLUSIONS

The climate-changing emissions associated with an EMR system are heavily dependent on the sources of electricity. With a decarbonized electricity source, the EMR system's GHG emissions are on par with paper medical record-keeping, and decarbonized grids would likely have a much broader benefit to society. Though we found that the EMR system produced more emissions than a paper record-keeping system, this study does not account for potential expanded environmental gains from EMRs, including expanding access to care while reducing patient travel and operational efficiencies that can reduce unnecessary or redundant care.

Healthcare providers' perception towards utilization of health information applications and its associated factors in healthcare delivery in health facilities in Cape Coast Metropolis, Ghana

BACKGROUND

Information and communication technology (ICT) has significantly advanced global healthcare, with electronic health (e-Health) applications improving health records and delivery. These innovations, including electronic health records, strengthen healthcare systems. The study investigates healthcare professionals' perceptions of health information applications and their associated factors in the Cape Coast Metropolis of Ghana's health facilities.

METHODS

We used a descriptive cross-sectional study design to collect data from 632 healthcare professionals (HCPs), in the three purposively selected health facilities in the Cape Coast municipality of Ghana, in July 2022. Shapiro-Wilk test was used to check the normality of dependent variables. Descriptive statistics were used to report means with corresponding standard deviations for continuous variables. Proportions were also reported for categorical variables. Bivariate regression analysis was conducted to determine the factors influencing the Benefits of Information Technology (BoIT); Barriers to Information Technology Use (BITU); and Motives of Information Technology Use (MoITU) in healthcare delivery. Stata SE version 15 was used for the analysis. A p-value of less than 0.05 served as the basis for considering a statistically significant accepting hypothesis.

RESULTS

Healthcare professionals (HCPs) generally perceived moderate benefits (Mean score (M) = 5.67) from information technology (IT) in healthcare. However, they slightly agreed that barriers like insufficient computers (M = 5.11), frequent system downtime (M = 5.09), low system performance (M = 5.04), and inadequate staff training (M = 4.88) hindered IT utilization. Respondents slightly agreed that training (M = 5.56), technical support (M = 5.46), and changes in work procedures (M = 5.10) motivated their IT use. Bivariate regression analysis revealed significant influences of education, working experience, healthcare profession, and IT training on attitudes towards IT utilization in healthcare delivery (BoIT, BITU, and MoITU). Additionally, the age of healthcare providers, education, and working experience significantly influenced BITU. Ultimately, age, education, working experience, healthcare profession, and IT training significantly influenced MoITU in healthcare delivery.

CONCLUSIONS

Healthcare professionals acknowledge moderate benefits of IT in healthcare but encounter barriers like inadequate resources and training. Motives for IT use include staff training and support. Bivariate regression analysis shows education, working experience, profession, and IT training significantly influence attitudes towards IT adoption. Targeted interventions and policies can enhance IT utilization in the Cape Coast Metropolis, Ghana.

Evaluation of the District Health Information System in District Kotli, Azad Jammu and Kashmir: A Retrospective Analysis

BACKGROUND

It is essential to implement a high-quality electronic database for keeping important information. The District Health Information System (DHIS) is an active data-keeping system in Pakistan. This study aimed to evaluate the patients' data from the DHIS dashboard for the District Headquarters Hospital, Kotli, Azad Jammu and Kashmir (AJK).

METHODOLOGY

The data was requested from the hospital administration at District Headquarters Hospital, Kotli, AJK, and the data was analyzed after permission was granted. The data was given in two forms; one was a hard copy of the data for August and September and the other was a comma-separated values file for October and November, 2023.

RESULTS

The highest frequency of patients was received in the department of emergency and trauma and the patient's median age was between 15 and 49 years. The second department was medicine with the >50 years of age. Common conditions that needed more attention were chronic obstructive pulmonary disease, acute respiratory infection, diarrhea, pneumonia, diabetes mellitus, hypertension, and ischemic heart disease.

CONCLUSION

For nations with constrained healthcare systems and funds, primary health care (PHC) is the only viable approach for managing non-communicable diseases (NCDs). However, PHC systems intended for infectious diseases have not sufficiently adapted to the growing requirement of chronic care for NCD. Research using health information databases offers numerous benefits, such as the evaluation of large data sets and unexpected prevalence of disease in certain populations, such as a higher prevalence of disease in one gender or age group. Health information system-based data analysis or studies are less expensive and faster but lack scientific control over data collection.

Centralized healthcare database for ensuring better healthcare: Are we lagging behind?

doi: https://doi.org/10.12669/pjms.40.3.9084 How to cite this: Tariq S, Tariq S, Shoukat AA. Centralized healthcare database for ensuring better healthcare: Are we lagging behind? Pak J Med Sci. 2024;40(3):---------. doi: https://doi.org/10.12669/pjms.40.3.9084 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

What is public trust in national electronic health record systems? A scoping review of qualitative research studies from 1995 to 2021

Objective

Public trust in national electronic health record systems is essential for the successful implementation within a healthcare system. Research investigating public trust in electronic health records is limited, leading to a lack of conceptual clarity. In response, the objective of this study is to gain a clearer understanding on the conceptualizations of public trust in electronic health records, which can support the implementation of national electronic health record systems.

Methods

Guided by the PRISMA-ScR checklist, a scoping review of 27 qualitative studies on public trust in electronic health records found between January 2022 and June 2022 was conducted using an inclusive search method. In an iterative process, conceptual themes were derived describing the promoters and outcomes of public trust in electronic health records.

Results

Five major conceptual themes with 15 sub-themes were present across the literature. Comprehension, autonomy, and data protection promote public trust in electronic health record; while personal and system benefits are the outcomes once public trust in electronic health records exists. Additional findings highlight the pivotal role of healthcare actors for the public trust building process.

Conclusions

The results underscore comprehension, autonomy, and data protection as important themes that help ascertain and solidify public trust in electronic health records. As well, health system actors have the capacity to promote or hinder national electronic health record implementation, depending on their actions and how the public perceives those actions. The findings can assist researchers, policymakers, and other health system actors in attaining a better understanding of the intricacies of public trust in electronic health records.

The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.

Digitizing nursing: A theoretical and holistic exploration to understand the adoption and use of digital technologies by nurses

BACKGROUND

With healthcare undergoing rapid digitalization, the effective integration of new technologies is crucial for nursing professionals, who form the largest group in the healthcare workforce. However, barriers within the nursing profession may impede digitalization efforts, leading to under utilization of available technologies and missed opportunities for enhancing healthcare quality and population health.

AIMS

This article aims to investigate the adoption and use of digital technologies by nurses, considering how key demographics, such as gender, age, and voluntariness of technology use, interact to influence their acceptance and utilization of these technologies.

METHODS

Employing the Unified Theory of Acceptance and Use of Technology (UTAUT) as a framework, we conducted a discursive exploration, supplemented by a literature review from diverse academic sources. Keywords related to UTAUT, digitalization, nursing practice and technology adoption were searched on PubMed, CINAHL and Google Scholar. Additionally, UK government and professional regulator reports were examined to understand current recommendations concerning digital technologies in nursing practice and the profession's demography. Searches focused on moderating factor domains, and the last search was conducted on 26 April 2023.

RESULTS

The study revealed that the successful implementation of digital technologies in nursing practice requires a nuanced understanding of the nursing workforce's characteristics and preferences. Gender, age and voluntariness of technology use were found to intersect and influence nurses' acceptance and utilization of digital tools.

DISCUSSION

By applying UTAUT in the context of nursing, this study highlights the importance of tailored implementation strategies for digital technologies. A technologically deterministic perspective is insufficient; instead, consideration of social factors specific to nursing is essential for successful adoption.

CONCLUSION

To maximize the benefits of digitalization in healthcare, it is imperative to address the barriers faced by nursing professionals. A comprehensive understanding of how key demographics impact technology adoption will inform targeted strategies, enhancing the engagement of nurses with digital tools and fostering innovation in healthcare practices. Further research and primary data are needed, but this study lays the foundation for future advancements in digital healthcare integration for nursing professionals.

RELEVANCE TO CLINICAL PRACTICE

The issues highlighted in this article are relevant to nurse leaders and those responsible for implementing technologies within nursing contexts. They are also relevant to technology developers who may benefit from considering the evidence associated with the moderating demographic factors highlighted in this article. Without a holistic approach to the implementation of technology, challenges associated with the use of digital technology by nurses are likely to persist. By considering the moderating demographic factors highlighted within the UTAUT (age, gender, voluntariness of use and experience) nurse leaders and technology developers may have greater success obtaining greater clinical outcomes from digital technology. This work was completed in 2022.

NO PATIENT OR PUBLIC CONTRIBUTION

Due to the focus of this article being one on professional challenges within the nursing profession, no involvement from patients or the public was sought.

Development and usability of a hospital standardized ADL ratio (HSAR) for elderly patients with cerebral infarction: a retrospective observational study using administrative claim data from 2012 to 2019 in Japan

BACKGROUND

Maintenance of activities of daily living (ADL) during acute hospitalization is an important treatment goal, especially for elderly inpatients with diseases that often leave disabilities, such as cerebral infarction. However, studies assessing risk-adjusted ADL changes are limited. In this study, we developed and calculated a hospital standardized ADL ratio (HSAR) using Japanese administrative claims data to measure the quality of hospitalization care for patients with cerebral infarction.

METHODS

This study was designed as a retrospective observational study using the Japanese administrative claim data from 2012 to 2019. The data of all hospital admissions with a primary diagnosis of cerebral infarction (ICD-10, I63) were used. The HSAR was defined as the ratio of the observed number of ADL maintenance patients to the expected number of ADL maintenance patients multiplied by 100, and ratio of ADL maintenance patients was risk-adjusted using multivariable logistic regression analyses. The c-statistic was used to evaluate the predictive accuracy of the logistic models. Changes in HSARs in each consecutive period were assessed using Spearman's correlation coefficient.

RESULTS

A total of 36,401 patients from 22 hospitals were included in this study. All variables used in the analyses were associated with ADL maintenance, and evaluations using the HSAR model showed predictive ability with c-statistics (area under the curve, 0.89; 95% confidence interval, 0.88-0.89).

CONCLUSIONS

The findings indicated a need to support hospitals with a low HSAR because hospitals with high/low HSAR were likely to produce the same results in the subsequent periods. HSAR can be used as a new quality indicator of in-hospital care and may contribute to the assessment and improvement of the quality of care.

Interorganizational health information exchange-related patient safety incidents: A descriptive register-based qualitative study

PURPOSE

The current literature related to patient safety of interorganizational health information is fragmented. This study aims to identify interorganizational health information exchange-related patient safety incidents occurring in the emergency department, emergency medical services, and home care. The research also aimed to describe the causes and consequences of these incidents.

METHODS

A total of sixty (n = 60) interorganizational health information exchange-related patient safety incident free text reports were analyzed. The reports were reported in the emergency department, emergency medical services, or home care between January 2016 and December 2019 in one hospital district in Finland.

RESULTS

The identified interorganizational health information exchange-related incidents were grouped under two main categories: "Inadequate documentation"; and "Inadequate use of information". The causes of these incidents were grouped under the two main categories "Factors related to the healthcare professional " and "Organizational factors", while the consequences of these incidents fell under the two main categories "Adverse events" and "Additional actions to prevent, avoid, and correct adverse events".

CONCLUSION

This study shows that the inadequate documentation and use of information is mainly caused by factors related to the healthcare professional and organization, including technical problems. These incidents cause adverse events and additional actions to prevent, avoid, and correct the events. The sociotechnical perspective, including factors related to health care professionals, organization, and technology, should be emphasized in patient safety development of inter-organizational health information exchange and it will be the focus of our future research. Continuous research and development work is needed because the processes and information systems used in health care are constantly evolving.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.

Implementation of Health Information Systems to Improve Patient Identification

Wellbeing can be ensured in society through quality healthcare, a minimum of medical errors, and the improved performance of healthcare professionals. To this end, health information systems have been implemented in hospitals, with this implementation representing progress in medicine and information technologies. As a result, life expectancy has significantly increased, standards in healthcare have been raised, and public health has improved. This progress is influenced by the process of managing healthcare organizations and information systems. While hospitals tend to adapt health information systems to reduce errors related to patient misidentification, the rise in the occurrence and recording of medical errors in Lebanon resulting from failures to correctly identify patients reveals that such measures remain insufficient due to unknown factors. This research aimed to investigate the effect of health information systems (HISs) and other factors related to work-related conditions on reductions in patient misidentification and related consequences. The empirical data were collected from 109 employees in Neioumazloum Hospital in Lebanon. The results revealed a correlation between HISs and components and the effects of other factors on patient identification. These other factors included workload, nurse fatigue, a culture of patient safety, and lack of implementation of patient identification policies. This paper provides evidence from a Lebanese hospital and paves the way for further studies aiming to explore the role of information technologies in adopting HISs for work performance and patient satisfaction. Improved care for patients can help achieve health equality, enhance healthcare delivery performance and patient safety, and decrease the numbers of medical errors.

Evaluating the Hospital Standardized Home-Transition Ratios for Cerebral Infarction in Japan: A Retrospective Observational Study from 2016 through 2020

Discharge to home is considered appropriate as a treatment goal for diseases that often leave disabilities such as cerebral infarction. Previous studies showed differences in risk-adjusted in-hospital mortality and readmission rates; however, studies assessing the rate of hospital-to-home transition are limited. We developed and calculated the hospital standardized home-transition ratio (HSHR) using Japanese administrative claims data from 2016–2020 to measure the quality of in-hospital care for cerebral infarction. Overall, 24,529 inpatients at 35 hospitals were included. All variables used in the analyses were associated with transition to another hospital or facility for inpatients, and evaluation of the HSHR model showed good predictive ability with c-statistics (area under curve, 0.73 standard deviation; 95% confidence interval, 0.72–0.73). All HSHRs of each consecutive year were significantly correlated. HSHRs for cerebral infarction can be calculated using Japanese administrative claims data. It was found that there is a need for support for low HSHR hospitals because hospitals with high/low HSHR were likely to produce the same results in the following year. HSHRs can be used as a new quality indicator of in-hospital care and may contribute to assessing and improving the quality of care.

Perceived Value of the Electronic Health Record and Its Association with Physician Burnout

BACKGROUND

There is a common belief that seniority and gender are associated with clinicians' perceptions of the value of electronic health record (EHR) technology and the propensity for burnout. Insufficient evidence exists on the relationship between these variables.

OBJECTIVE

The aim of this study was to investigate how seniority/years of practice, gender, and screened burnout status are associated with opinions of EHR use on quality, cost, and efficiency of care.

METHODS

We surveyed ambulatory primary care and subspecialty clinicians at three different institutions to screen for burnout status and to measure their opinions (positive, none, negative, don't know) on how EHR technology has impacted three important attributes of health care: quality, cost, and efficiency of care. We used chi-square tests to analyze association between years of practice (≤10 years or 11+ years), gender, and screened burnout status and the reported attributes. We used a Bonferroni-corrected α = 0.0167 for significance to protect against type I error among multiple comparisons.

RESULTS

Overall, 281 clinicians responded from 640 that were surveyed with 44% overall response rate. There were no significant associations of years in practice (≤10 years or 11+ years) or gender (p > 0.0167 for both) with any of the health care attributes. Clinicians who screened burnout negative (n = 154, 55%) were more likely to indicate that EHR technology has a positive impact on both the quality (p = 0.0025) and efficiency (p = 0.0003) health care attributes compared with those who screened burnout positive (n = 127, 45%).

CONCLUSION

Burnout status is significantly associated with clinicians' perceived value of EHR technologies, while years of practice and gender are not. This contests the popular notion that junior clinicians view EHR technology more favorably than their more senior counterparts. Hence, burnout status may be an important factor associated with the overall value clinicians ascribe to EHR technologies.

The Impact of Structured and Standardized Documentation on Documentation Quality; a Multicenter, Retrospective Study

The reuse of healthcare data for various purposes will become increasingly important in the future. To enable the reuse of clinical data, structured and standardized documentation is conditional. However, the primary purpose of clinical documentation is to support high-quality patient care. Therefore, this study investigated the effect of increased structured and standardized documentation on the quality of notes in the Electronic Health Record. A multicenter, retrospective design was used to assess the difference in note quality between 144 unstructured and 144 structured notes. Independent reviewers measured note quality by scoring the notes with the Qnote instrument. This instrument rates all note elements independently using and results in a grand mean score on a 0–100 scale. The mean quality score for unstructured notes was 64.35 (95% CI 61.30–67.35). Structured and standardized documentation improved the Qnote quality score to 77.2 (95% CI 74.18–80.21), a 12.8 point difference (p < 0.001). Furthermore, results showed that structured notes were significantly longer than unstructured notes. Nevertheless, structured notes were more clear and concise. Structured documentation led to a significant increase in note quality. Moreover, considering the benefits of structured data recording in terms of data reuse, implementing structured and standardized documentation into the EHR is recommended.

Registered Nurses' and Medical Doctors' Experiences of Patient Safety in Health Information Exchange During Interorganizational Care Transitions: A Qualitative Review

OBJECTIVE

This systematic review aimed to identify, critically appraise, and synthesize the best available literature on registered nurses' and medical doctors' experiences of patient safety in health information exchange (HIE) during interorganizational care transitions.

METHODS

The review was conducted according to the JBI methodology for systematic reviews of qualitative evidence. A total of 5 multidisciplinary databases were searched from January 2010 to September 2020 to identify qualitative or mixed methods studies. The qualitative findings were pooled using JBI SUMARI with the meta-aggregation approach.

RESULTS

The final review included 6 original studies. The 53 distinct findings were aggregated into 9 categories, which were further merged into 3 synthesized findings: (1) HIE efficiency and accuracy support patient safety during interorganizational care transitions; (2) inaccuracies in content and structure, along with poor HIE usability, jeopardize patient safety during interorganizational care transitions; and (3) health care professionals' (HCP) actions in HIE are associated with patient safety during interorganizational care transitions.

CONCLUSIONS

The results of this review identified several advantages of HIE, namely, improvements in patient safety based on reduced human error. Nevertheless, a lack of usability and functionality can amplify the effects of human error and increase the risk of adverse events. In addition, HCPs' individual actions in HIE were found to influence patient safety. Hence, the cognitive and sociotechnical perspectives of work related to HIE should be studied. In addition, HCPs' experiences of each stage of HIE deployment should be clarified to ensure a high standard of patient safety. Registration: PROSPERO CRD42020220631, registered on November 13, 2020.

How Do Different Health Record Systems Affect Home Health Care? A Cross-Sectional Study of Electronic- versus Manual Documentation System

Objective

To investigate electronic health record (EHR) systems compared to manual systems (MS) in home health care and how documentation and reporting activities are impacted regarding time use, variation, and accuracy.

Methods

This is a cross-sectional study of two municipalities (M1 and M2) that use statistical process control charts and interview with caregivers to discuss the issue. Regarding reporting, 309 observations were used for the control charts in M1 and 572 for those in M2. Concerning documentation, 831 observations were used for M1 and 572 for M2. In addition, interviews were conducted with four caregivers from each municipality.

Results

The municipality with EHR system use 3% of their total time for documentation and 7% for reporting. The municipality with the MS uses 7% of their total time in documentation and 12% for reporting. There is less variation in the charts for the municipality with the EHR system, than for the municipality using an MS.

Conclusion

The municipality using the EHR system uses less time for documentation and reporting than the other municipality. This is probably due to the standardization of information in M1, and that M2 needs to record documentation twice. The standardization arising from EHR use system may cause less variation in the process than the MS, but less variation might also negatively affect information accuracy. Reduced time for oral reporting also affects information accuracy.

Predicting short and long-term mortality after acute ischemic stroke using EHR

OBJECTIVE

Despite improvements in treatment, stroke remains a leading cause of mortality and long-term disability. In this study, we leveraged administrative data to build predictive models of short- and long-term post-stroke all-cause-mortality.

METHODS

The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. We used patient-level data from electronic health records, three algorithms, and six prediction windows to develop models for post-stroke mortality.

RESULTS

We included 7144 patients from which 5347 had survived their ischemic stroke after two years. The proportion of mortality was between 8%(605/7144) within 1-month, to 25%(1797/7144) for the 2-years window. The three most common comorbidities were hypertension, dyslipidemia, and diabetes. The best Area Under the ROC curve(AUROC) was reached with the Random Forest model at 0.82 for the 1-month prediction window. The negative predictive value (NPV) was highest for the shorter prediction windows - 0.91 for the 1-month - and the best positive predictive value (PPV) was reached for the 6-months prediction window at 0.92. Age, hemoglobin levels, and body mass index were the top associated factors. Laboratory variables had higher importance when compared to past medical history and comorbidities. Hypercoagulation state, smoking, and end-stage renal disease were more strongly associated with long-term mortality.

CONCLUSION

All the selected algorithms could be trained to predict the short and long-term mortality after stroke. The factors associated with mortality differed depending on the prediction window. Our classifier highlighted the importance of controlling risk factors, as indicated by laboratory measures.

Medical Documentation in Low- and Middle-income Countries: Lessons Learned from Implementing Specialized Charting Software

Background:

The implementation of electronic health record (EHR) software at healthcare facilities in low- and middle-income countries (LMICs) is limited by financial and technological constraints. Smile Train, the world’s largest cleft charity, developed a cleft treatment EHR system, Smile Train Express (STX), and distributed it to their partnered institutions. The purpose of this study was to investigate trends in medical documentation practices amongst Smile Train-partner institutions to characterize the impact that specialized EHR software has on medical documentation practices at healthcare facilities in LMICs.

Methods:

Surveys were administered electronically to 843 Smile Train-partnered institutions across 68 LMICs. The survey inquired about institutions’ internet connection, documentation methods used during patient encounters, rationale for using said methods, and documentation methods for cloud-based storage of healthcare data. Institutions were grouped by economic and geographic subgroups for analysis.

Results:

A total of 162 institutions (19.2%) responded to the survey. Most institutions employed paper charting (64.2%) or institutional EHR software (25.9%) for data entry during a patient encounter with the latter’s use varying significantly across geographical subgroups (P = 0.01). STX was used by 18 institutions (11.1%) during a patient encounter. Workflow was the most frequently cited reason for institutions to employ their entry method during a patient encounter (51.4%).

Conclusions:

The provision of STX to partnered institutions influenced medical documentation practices at several institutions; however, regulations and guidelines have likely limited its complete integration into clinical workflows. Further studies are needed to characterize trends in medical documentation in LMICs at a more granular level.

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

BACKGROUND

Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

OBJECTIVES

This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

METHODS

This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

RESULTS

Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

CONCLUSION

For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Association between Electronic Medical Records and Healthcare Quality

The implementation of electronic medical records (EMRs) has generally been thought to improve medical efficiency and safety, but consistent evidence of improved healthcare quality due to EMRs in population-based studies is lacking. We assessed the relationship between the degree of EMR adoption and patient outcomes.We performed an observational study using discharge data from Tri-service General Hospital from 2013 to 2018. The levels of EMR utilization were divided into no EMRs, partial EMRs and full EMRs. The primary healthcare quality indicators were inpatient mortality, readmission within 14 days, and 48-hour postoperative mortality. We performed a Cox proportional hazards regression analysis to evaluate the relationship between the EMR utilization level and healthcare quality.In total, 262,569 patients were included in this study. Compared with no EMRs, full EMR implementation led to lower inpatient mortality [adjusted hazard ratio (HR) 0.947, 95% confidence interval (CI): 0.897-0.999, P = ..049] and a lower risk of readmission within 14 days (adjusted HR 0.627, 95% CI: 0.577-0.681, P < .001). Full EMR implementation was associated was a lower risk of 48-hour postoperative mortality (adjusted HR 0.372, 95% CI: 0.208-0.665, P = .001) than no EMRs. Partial EMR implementation was associated with a higher risk of readmission within 14 days than no EMRs (HR 1.387, 95% CI: 1.298-1.485, P < .001).Full EMR adoption improves healthcare quality in medical institutions treating severely ill patients. A prospective study is needed to confirm this finding.

Improved efficiency of patient admission with electronic health records in neurosurgery

BACKGROUND

Electronic health records (EHRs) may be controversial but they have the potential to improve patient care. We investigated whether the introduction of an electronic template-based admission form for the collection of information about the patient's medical history and neurological and clinical state at admission in the neurosurgical unit might have an impact on the quality of documentation in a discharge record and the amount of time taken to produce this documentation.

METHOD

A new digital template-based admission form (EHR) was developed and assessed with QNOTE, an assessment tool of medical notes with standardised criteria and the possibility to benchmark the quality of documentations. This was compared to 30 prior paper-based handwritten documentations (HWD) regarding the utilisation of these medical notes for dictation of medical discharge records.

RESULTS

Implementation of the EHR significantly improved the quality of patient admission documentation with a QNOTE mean grand score of 87 ± 22 (p < 0.0001) compared to prior HWD with 44 ± 30. The mean documentation time for HWD was 8.1 min ± 4.1 min and the dictation time for discharge records was 10.6 min ± 3.5 min. After implementation of EHR, the documentation time increased slightly to 9.6 min ± 2.3 min (n.s.), while the time for dictation of discharge records was reduced to 5.1 min ± 1.2 min (p < 0.0001). There was a clear correlation between a higher quality of documentation and a higher needed documentation time as well as higher quality of documentation and lower dictation times of discharge records.

CONCLUSION

Implementation of the EHR improved the quality of patient admission documentation and reduced the dictation time of discharge records.

IMPLICATIONS

It is crucial to involve stakeholders and users of EHRs in a timely manner during the stage of development and implementation phase to ensure optimal results and better usability.