Adoption of electronic health records in Korean tertiary teaching and general hospitals

Basic electronic health record (EHR) adoption in **Türkiye is nearly complete but challenges persist

BACKGROUND

The digitalization studies in public hospitals in Türkiye started with the Health Transformation Program in 2003. As digitalization was accomplished, the policymakers needed to measure hospitals' electronic health record (EHR) usage and adoptions. The ministry of health has been measuring the dissemination of meaningful usage and adoption of EHR since 2013 using Electronic Medical Record Adoption Model (EMRAM). The first published study about this analysis covered the surveys applied between 2013 and 2017. The results showed that 63.1% of all hospitals in Türkiye had at least basic EHR functions, and 36% had comprehensive EHR functions. Measuring the countrywide EHR adoption level is becoming popular in the world. This study aims to measure adoption levels of EHR in public hospitals in Türkiye, indicate the change to the previous study, and make a benchmark with other countries measuring national EHR adoption levels. The research question of this study is to reveal whether there has been a change in the adoption level of EHR in the three years since 2018 in Türkiye. Also, make a benchmark with other countries such as the US, Japan, and China in country-wide EHR adoption in 2021.

METHODS

In 2021, 717 public hospitals actively operating in Türkiye completed the EMRAM survey. The survey results, deals with five topics (General Stage Status, Information Technology Security, Electronic Health Record/Clinical Data Repository, Clinical Documentation, Closed-Loop Management), was reviewed by the authors. Survey data were compared according to hospital type (Specialty Hospitals, General Hospitals, Teaching and Research Hospitals) in terms of general stage status. The data obtained from the survey results were analyzed with QlikView Personal Edition. The availability and prevalence of medical information systems and EHR functions and their use were measured.

RESULTS

We found that 33.7% of public hospitals in Türkiye have only basic EHR functions, and 66.3% have extensive EHR functions, which yields that all hospitals (100%) have at least basic EHR functions. That means remarkable progress from the previous study covering 2013 and 2017. This level also indicates that Türkiye has slightly better adoption from the US (96%) and much better than China (85.3%) and Korea (58.1%).

CONCLUSIONS

Although there has been outstanding (50%) progress since 2017 in Turkish public hospitals, it seems there is still a long way to disseminate comprehensive EHR functions, such as closed-loop medication administration, clinical decision support systems, patient engagement, etc. Measuring the stage of EHR adoption at regular intervals and on analytical scales is an effective management tool for policymakers. The bottom-up adoption approach established for adopting and managing EHR functions in the US has also yielded successful results in Türkiye.

Clinical information system (CIS) implementation in developing countries: requirements, success factors, and recommendations

OBJECTIVE

Clinical Information System (CIS) usage can reduce healthcare costs over time, improve the quality of medical care and safety, and enhance clinical efficiency. However, CIS implementation in developing countries poses additional, different challenges from the developed countries. Therefore, this research aimed to systematically review the literature, gathering and integrating research findings on Success Factors (SFs) in CIS implementation for developing countries. This helps to integrate past knowledge and develop a set of recommendations, presented as a framework, for implementing CIS in developing countries.

MATERIALS AND METHODS

A systematic literature review was conducted, followed by qualitative data analysis on the published articles related to requirements and SF for CIS implementation. Eighty-three articles met the inclusion criteria and were included in the data analysis. Thematic analysis and cross-case analysis were applied to identify and categorize the requirements and SF for CIS implementation in developing countries.

RESULTS

Six major requirement categories were identified including project management, financial resources, government involvement and support, human resources, organizational, and technical requirements. Subcategories related to SF are classified under each major requirement. A set of recommendations is provided, presented in a framework, based on the project management lifecycle approach.

CONCLUSION

The proposed framework could support CIS implementations in developing countries while enhancing their rate of success. Future studies should focus on identifying barriers to CIS implementation in developing countries. The country-specific empirical studies should also be conducted based on this research's findings to match the local context.

The Integrated Electronic Health System in Iranian health centers: benefits and challenges

BACKGROUND

Electronic Health Records (EHRs) were introduced to all Iranian medical universities in 2015 with the launch of Integrated Electronic Health System (which is known as SIB: a Persian backronym in Persian meaning apple), and a number of studies were conducted on SIB. However, most of these studies did not consider the benefits and challenges of adopting SIB in Iran. Therefore, the present study aimed to identify the benefits and challenges of SIB in health centers of Khuzestan Province, Iran.

METHODS

This was a qualitative study using qualitative conventional content analysis conducted on 6 experts and 24 users of SIB in six health centers of three cities in Khuzestan province, Iran. The participants were selected using a purposeful sampling method. Maximum variation was considered in selecting the group of users, and snowball sampling was used in the group of experts. Data collection tool was semi-structured interview. Data analysis was performed using thematic analysis.

RESULTS

Overall, 42 components (24 for benefits and 18 for challenges) were extracted from the interviews. Common sub-themes and themes were identified for challenges and benefits. The components formed 12 sub-themes, and they were placed in 3 main themes, namely structure, process and outcome. 1) Structure included four sub-themes of Financial resources, Human resources, Facilities, and Access to the Internet; 2) Process involved three sub-themes of Training, Providing services, and Time and workload; and 3) Outcome incorporated five sub-themes of Quality of health services, Access, Safety and personal distance, Screening and evaluation, and Research.

CONCLUSIONS

In the present study, the benefits and challenges of adopting SIB were examined in three themes: structure, process, and outcome. Most of the identified benefits were related to the theme of outcome, and most of the identified challenges were related to the theme of structure. Based on the identified factors, by strengthening the benefits of SIB and also trying to eliminate or reduce its challenges, it is possible to institutionalize and use it more effectively in order to solve health problems.

Factors affecting the adoption of e-health system in the Kingdom of Saudi Arabia

Background

The Saudi government is trying to implement the e-health system throughout Saudi Arabia to promote accessible health services for its population. However, adoption of the e-health system has not been effective. Thus the objective of this study was to investigate the factors that influence the adoption of e-health in this country.

Methods

To carry out this research, a questionnaire was designed to obtain information on how people in Saudi Arabia use the e-health system and the problems they face when using this technology. The questionnaire was initially viewed by 438 people and 130 of them answered the survey.

Results

The results of this research on the adoption of the e-health system in Saudi Arabia indicated that the main factors preventing the implementation of this system were mainly related to the lack of a relationship between doctors and patients, fears about the possibility of violating data privacy and a lack of government regulations. In addition, there are certain demographic factors such as age, gender, residence, income, education and culture that create obstacles in the adoption of the e-health system.

Conclusions

This study suggests that professionals should contribute to modifying the e-health system and adding more government regulatory bodies to increase adoption. This will encourage end-users to trust the system. By modifying existing strategies, the results of this study can contribute to the successful implementation of the e-health system in Saudi Arabia.

Adoption of Electronic Health Records (EHRs) in China During the Past 10 Years: Consecutive Survey Data Analysis and Comparison of Sino-American Challenges and Experiences

Background

The adoption rate of electronic health records (EHRs) in hospitals has become a main index to measure digitalization in medicine in each country.

Objective

This study summarizes and shares the experiences with EHR adoption in China and in the United States.

Methods

Using the 2007-2018 annual hospital survey data from the Chinese Health Information Management Association (CHIMA) and the 2008-2017 United States American Hospital Association Information Technology Supplement survey data, we compared the trends in EHR adoption rates in China and the United States. We then used the Bass model to fit these data and to analyze the modes of diffusion of EHRs in these 2 countries. Finally, using the 2007, 2010, and 2014 CHIMA and Healthcare Information and Management Systems Services survey data, we analyzed the major challenges faced by hospitals in China and the United States in developing health information technology.

Results

From 2007 to 2018, the average adoption rates of the sampled hospitals in China increased from 18.6% to 85.3%, compared to the increase from 9.4% to 96% in US hospitals from 2008 to 2017. The annual average adoption rates in Chinese and US hospitals were 6.1% and 9.6%, respectively. However, the annual average number of hospitals adopting EHRs was 1500 in China and 534 in the US, indicating that the former might require more effort. Both countries faced similar major challenges for hospital digitalization.

Conclusions

The adoption rates of hospital EHRs in China and the United States have both increased significantly in the past 10 years. The number of hospitals that adopted EHRs in China exceeded 16,000, which was 3.3 times that of the 4814 nonfederal US hospitals. This faster adoption outcome may have been a benefit of top-level design and government-led policies, particularly the inclusion of EHR adoption as an important indicator for performance evaluation and the appointment of public hospitals.

Public Health Innovation through Cloud Adoption: A Comparative Analysis of Drivers and Barriers in Japan, South Korea, and Singapore

Governments are increasingly using cloud computing to reduce cost, increase access, improve quality, and create innovations in healthcare. Existing literature is primarily based on successful examples from developed western countries, and there is a lack of similar evidence from Asia. With a population close to 4.5 billion people, Asia faces healthcare challenges that pose an immense burden on economic growth and policymaking. Cloud computing in healthcare can potentially help increase the quality of healthcare delivery and reduce the economic burden, enabling governments to address healthcare challenges effectively and within a short timeframe. Advanced Asian countries such as Japan, South Korea, and Singapore provide successful examples of how cloud computing can be used to develop nationwide databases of electronic health records; real-time health monitoring for the elderly population; genetic database to support advanced research and cancer treatment; telemedicine; and health cities that drive the economy through medical industry, tourism, and research. This article examines these countries and identifies the drivers and barriers of cloud adoption in healthcare and makes policy recommendations to enable successful public health innovations through cloud adoption.

Adoption rates of electronic health records in Turkish Hospitals and the relation with hospital sizes

BACKGROUND

Nation-wide adoption of electronic health records (EHRs) in hospitals has become a Turkish policy priority in recognition of their benefits in maintaining the overall quality of clinical care. The electronic medical record maturity model (EMRAM) is a widely used survey tool developed by the Healthcare Information and Management Systems Society (HIMSS) to measure the rate of adoption of EHR functions in a hospital or a secondary care setting. Turkey completed many standardizations and infrastructural improvement initiatives in the health information technology (IT) domain during the first phase of the Health Transformation Program between 2003 and 2017. Like the United States of America (USA), the Turkish Ministry of Health (MoH) applied a bottom-up approach to adopting EHRs in state hospitals. This study aims to measure adoption rates and levels of EHR use in state hospitals in Turkey and investigate any relationship between adoption and use and hospital size.

METHODS

EMRAM surveys were completed by 600 (68.9%) state hospitals in Turkey between 2014 and 2017. The availability and prevalence of medical information systems and EHR functions and their use were measured. The association between hospital size and the availability/prevalence of EHR functions was also calculated.

RESULTS

We found that 63.1% of all hospitals in Turkey have at least basic EHR functions, and 36% have comprehensive EHR functions, which compares favourably to the results of Korean hospitals in 2017, but unfavorably to the results of US hospitals in 2015 and 2017. Our findings suggest that smaller hospitals are better at adopting certain EHR functions than larger hospitals.

CONCLUSION

Measuring the overall adoption rates of EHR functions is an emerging approach and a beneficial tool for the strategic management of countries. This study is the first one covering all state hospitals in a country using EMRAM. The bottom-up approach to adopting EHR in state hospitals that was successful in the USA has also been found to be successful in Turkey. The results are used by the Turkish MoH to disseminate the nation-wide benefits of EHR functions.

Computer-based clinical coding activity analysis for neurosurgical terms

Background

It is not possible to measure how much activity is required to understand and code a medical data. We introduce an assessment method in clinical coding, and applied this method to neurosurgical terms.

Methods

Coding activity consists of two stages. At first, the coders need to understand a presented medical term (informational activity). The second coding stage is about a navigating terminology browser to find a code that matches the concept (code-matching activity). Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) was used for the coding system. A new computer application to record the trajectory of the computer mouse and record the usage time was programmed. Using this application, we measured the time that was spent. A senior neurosurgeon who has studied SNOMED CT has analyzed the accuracy of the input coding. This method was tested by five neurosurgical residents (NSRs) and five medical record administrators (MRAs), and 20 neurosurgical terms were used.

Results

The mean accuracy of the NSR group was 89.33%, and the mean accuracy of the MRA group was 80% (p=0.024). The mean duration for total coding of the NSR group was 158.47 seconds, and the mean duration for total coding of the MRA group was 271.75 seconds (p=0.003).

Conclusion

We proposed a method to analyze the clinical coding process. Through this method, it was possible to accurately calculate the time required for the coding. In neurosurgical terms, NSRs had shorter time to complete the coding and higher accuracy than MRAs.

Diffusion dynamics of electronic health records: A longitudinal observational study comparing data from hospitals in Germany and the United States

BACKGROUND

While aiming for the same goal of building a national eHealth Infrastructure, Germany and the United States pursued different strategic approaches - particularly regarding the role of promoting the adoption and usage of hospital Electronic Health Records (EHR).

OBJECTIVE

To measure and model the diffusion dynamics of EHRs in German hospital care and to contrast the results with the developments in the US.

MATERIALS AND METHODS

All acute care hospitals that were members of the German statutory health system were surveyed during the period 2007-2017 for EHR adoption. Bass models were computed based on the German data and the corresponding data of the American Hospital Association (AHA) from non-federal hospitals in order to model and explain the diffusion of innovation.

RESULTS

While the diffusion dynamics observed in the US resembled the typical s-shaped curve with high imitation effects (q = 0.583) but with a relatively low innovation effect (p = 0.025), EHR diffusion in Germany stagnated with adoption rates of approx. 50% (imitation effect q = -0.544) despite a higher innovation effect (p = 0.303).

DISCUSSION

These findings correlate with different governmental strategies in the US and Germany of financially supporting EHR adoption. Imitation only seems to work if there are financial incentives, e.g. those of the HITECH Act in the US. They are lacking in Germany, where the government left health IT adoption strategies solely to the free market and the consensus among all of the stakeholders.

CONCLUSION

Bass diffusion models proved to be useful for distinguishing the diffusion dynamics in German and US non-federal hospitals. When applying the Bass model, the imitation parameter needs a broader interpretation beyond the network effects, including driving forces such as incentives and regulations, as was demonstrated by this study.

Rate of electronic health record adoption in South Korea: A nation-wide survey

OBJECTIVE

The adoption rate of electronic health record (EHR) systems in South Korea has continuously increased. However, in contrast to the situation in the United States (US), where there has been a national effort to improve and standardize EHR interoperability, no consensus has been established in South Korea. The goal of this study was to determine the current status of EHR adoption in South Korean hospitals compared to that in the US.

METHODS

All general and tertiary teaching hospitals in South Korea were surveyed regarding their EHR status in 2015 with the same questionnaire as used previously. The survey form estimated the level of adoption of EHR systems according to 24 core functions in four categories (clinical documentation, result view, computerized provider order entry, and decision supports). The adoption level was classified into comprehensive and basic EHR systems according to their functionalities.

RESULTS

EHRs and computerized physician order entry systems were used in 58.1% and 86.0% of South Korean hospitals, respectively. Decision support systems and problem list documentation were the functions most frequently missing from comprehensive and basic EHR systems. The main barriers cited to adoption of EHR systems were the cost of purchasing (48%) and the ongoing cost of maintenance (11%).

DISCUSSION

The EHR adoption rate in Korean hospitals (37.2%) was higher than that in US hospitals in 2010 (15.1%), but this trend was reversed in 2015 (58.1% vs. 75.2%). The evidence suggests that these trends were influenced by the level of financial and political support provided to US hospitals after the HITECH Act was passed in 2009.

CONCLUSIONS

The EHR adoption rate in Korea has increased, albeit more slowly than in the US. It is logical to suggest that increased funding and support tied to the HITECH Act in the US partly explains the difference in the adoption rates of EHRs in both countries.

Nursing Informatics Certification Worldwide: History, Pathway, Roles, and Motivation

INTRODUCTION

Official recognition and certification for informatics professionals are essential aspects of workforce development.

OBJECTIVE

To describe the history, pathways, and nuances of certification in nursing informatics across the globe; compare and contrast those with board certification in clinical informatics for physicians.

METHODS

(1) A review of the representative literature on informatics certification and related competencies for nurses and physicians, and relevant websites for nursing informatics associations and societies worldwide; (2) similarities and differences between certification processes for nurses and physicians, and (3) perspectives on roles for nursing informatics professionals in healthcare Results: The literature search for 'nursing informatics certification' yielded few results in PubMed; Google Scholar yielded a large number of citations that extended to magazines and other non-peer reviewed sources. Worldwide, there are several nursing informatics associations, societies, and workgroups dedicated to nursing informatics associated with medical/health informatics societies. A formal certification program for nursing informatics appears to be available only in the United States. This certification was established in 1992, in concert with the formation and definition of nursing informatics as a specialty practice of nursing by the American Nurses Association. Although informatics is inherently interprofessional, certification pathways for nurses and physicians have developed separately, following long-standing professional structures, training, and pathways aligned with clinical licensure and direct patient care. There is substantial similarity with regard to the skills and competencies required for nurses and physicians to obtain informatics certification in their respective fields. Nurses may apply for and complete a certification examination if they have experience in the field, regardless of formal training. Increasing numbers of informatics nurses are pursuing certification.

CONCLUSIONS

The pathway to certification is clear and wellestablished for U.S. based informatics nurses. The motivation for obtaining and maintaining nursing informatics certification appears to be stronger for nurses who do not have an advanced informatics degree. The primary difference between nursing and physician certification pathways relates to the requirement of formal training and level of informatics practice. Nurse informatics certification requires no formal education or training and verifies knowledge and skill at a more basic level. Physician informatics certification validates informatics knowledge and skill at a more advanced level; currently this requires documentation of practice and experience in clinical informatics and in the future will require successful completion of an accredited two-year fellowship in clinical informatics. For the profession of nursing, a graduate degree in nursing or biomedical informatics validates specialty knowledge at a level more comparable to the physician certification. As the field of informatics and its professional organization structures mature, a common certification pathway may be appropriate. Nurses, physicians, and other healthcare professionals with informatics training and certification are needed to contribute their expertise in clinical operations, teaching, research, and executive leadership.

Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research

OBJECTIVES

A distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).

METHODS

We transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.

RESULTS

Patient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.

CONCLUSIONS

We successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.

Using Electronic Health Records for Population Health Research: A Review of Methods and Applications

The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.

A normalization method for combination of laboratory test results from different electronic healthcare databases in a distributed research network

PURPOSE

Distributed research networks (DRNs) afford statistical power by integrating observational data from multiple partners for retrospective studies. However, laboratory test results across care sites are derived using different assays from varying patient populations, making it difficult to simply combine data for analysis. Additionally, existing normalization methods are not suitable for retrospective studies. We normalized laboratory results from different data sources by adjusting for heterogeneous clinico-epidemiologic characteristics of the data and called this the subgroup-adjusted normalization (SAN) method.

METHODS

Subgroup-adjusted normalization renders the means and standard deviations of distributions identical under population structure-adjusted conditions. To evaluate its performance, we compared SAN with existing methods for simulated and real datasets consisting of blood urea nitrogen, serum creatinine, hematocrit, hemoglobin, serum potassium, and total bilirubin. Various clinico-epidemiologic characteristics can be applied together in SAN. For simplicity of comparison, age and gender were used to adjust population heterogeneity in this study.

RESULTS

In simulations, SAN had the lowest standardized difference in means (SDM) and Kolmogorov-Smirnov values for all tests (p < 0.05). In a real dataset, SAN had the lowest SDM and Kolmogorov-Smirnov values for blood urea nitrogen, hematocrit, hemoglobin, and serum potassium, and the lowest SDM for serum creatinine (p < 0.05).

CONCLUSION

Subgroup-adjusted normalization performed better than normalization using other methods. The SAN method is applicable in a DRN environment and should facilitate analysis of data integrated across DRN partners for retrospective observational studies.

Acceptability and feasibility of the Leapfrog computerized physician order entry evaluation tool for hospitals outside the United States

BACKGROUND

Computerized physician order entry (CPOE) with clinical decision support is expected to deliver many benefits in terms of patient safety. The Leapfrog tool was developed to allow hospitals to assess their medication-safety related decision support. To explore the approach's generalizability, we examined its acceptability and feasibility through an evaluation using this tool in four Korean hospital systems.

METHODS

Four hospitals with locally developed CPOE systems participated, and a cross-sectional study design was used with the approval of the Leapfrog Group and the institutional review board at each hospital site. The hospitals were tertiary and academic institutions with long experience of advanced CPOE. From January 21 to 28, 2014, web-based tests were conducted at each site following the given instructions, and the results were self-reported. We measured each system's response rate, category completion rate, and time to complete the evaluation. Additionally, we compared the evaluation results of the four systems with scores from five US systems, as was reported in another study.

RESULTS

The four systems underwent the tests, and the overall category completion rates ranged from 67.9% to 75.5%. The times to finish the tests were tolerable and within the allowed test timeframe. One system did not pass the deception analysis, which checks for false positives, due to a conflict with another type of alert checking for the presence of a medical diagnosis for documentation purposes. The other three systems scored at the completed the evaluation stage, with scores ranging from 21.6% to 36.5%. Of the nine error categories, Drug-Allergy was an area of strength for all systems, whereas the categories of Therapeutic duplication, Drug-Labs, and Drug-Age were areas of weakness for all. In comparison with the US systems, gaps were identified, and further improvement is needed.

CONCLUSIONS

The acceptability of the CPOE evaluation tool was moderate, but the feasibility was sufficient to operate the test outside the US, and the results revealed many opportunities for improvement in the Korean systems, as was the case when this application was introduced in the US.

Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service

Objectives

To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability.

Methods

We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functional services, and mobile services. Microsoft's Azure cloud computing for Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) was used.

Results

The functional and software views of an HSP were designed in a layered architecture. External systems can be interfaced with the HSP using SOAP and REST/JSON. The multi-tenancy model of the HSP was designed as a shared database, with a separate schema for each tenant through a single application, although healthcare data can be physically located on a cloud or in a hospital, depending on regulations. The CDS services were categorized into rule-based services for medications, alert registration services, and knowledge services.

Conclusions

We expect that cloud-based HSPs will allow small and mid-sized hospitals, in addition to large-sized hospitals, to adopt information infrastructures and health information technology with low system operation and maintenance costs.

Some correlates of electronic health information management system success in nigerian teaching hospitals

Nowadays, an electronic health information management system (EHIMS) is crucial for patient care in hospitals. This paper explores the aspects and elements that contribute to the success of EHIMS in Nigerian teaching hospitals. The study adopted a survey research design. The population of study comprised 442 health information management personnel in five teaching hospitals that had implemented EHIMS in Nigeria. A self-developed questionnaire was used as an instrument for data collection. The findings revealed that there is a positive, close relationship between all the identified factors and EHIMS’s success: technical factors (r = 0.564, P < 0.05); social factors (r = 0.616, P < 0.05); organizational factors (r = 0.621, P < 0.05); financial factors (r = 0.705, P < 0.05); and political factors (r = 0.589, P < 0.05). We conclude that consideration of all the identified factors was highly significant for the success of EHIMS in Nigerian teaching hospitals.

A De-identification method for bilingual clinical texts of various note types

De-identification of personal health information is essential in order not to require written patient informed consent. Previous de-identification methods were proposed using natural language processing technology in order to remove the identifiers in clinical narrative text, although these methods only focused on narrative text written in English. In this study, we propose a regular expression-based de-identification method used to address bilingual clinical records written in Korean and English. To develop and validate regular expression rules, we obtained training and validation datasets composed of 6,039 clinical notes of 20 types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules were constructed using the development dataset and those rules achieved 99.87% precision and 96.25% recall for the validation dataset. Our de-identification method successfully removed the identifiers in diverse types of bilingual clinical narrative texts. This method will thus assist physicians to more easily perform retrospective research.

What Are Complex eHealth Innovations and How Do You Measure Them? Position Paper

OBJECTIVES

eHealth and innovation are often regarded as synonyms - not least because eHealth technologies and applications are new to their users. This position paper challenges this view and aims at exploring the nature of eHealth innovation against the background of common definitions of innovation and facts from the biomedical and health informatics literature. A good understanding of what constitutes innovative eHealth developments allows the degree of innovation to be measured and interpreted.

METHODS

To this end, relevant biomedical and health informatics literature was searched mainly in Medline and ACM digital library. This paper presents seven facts about implementing and applying new eHealth developments hereby drawing on the experience published in the literature.

RESULTS

The facts are: 1. eHealth innovation is relative. 2. Advanced clinical practice is the yardstick. 3. Only used and usable eHealth technology can give birth to eHealth innovatio. 4. One new single eHealth function does not make a complex eHealth innovation. 5. eHealth innovation is more evolution than revolution. 6. eHealth innovation is often triggered behind the scenes; and 7. There is no eHealth innovation without sociocultural change.

CONCLUSIONS

The main conclusion of the seven facts is that eHealth innovations have many ingredients: newness, availability, advanced clinical practice with proven outcomes, use and usability, the supporting environment, other context factors and the stakeholder perspectives. Measuring eHealth innovation is thus a complex matter. To this end we propose the development of a composite score that expresses comprehensively the nature of eHealth innovation and that breaks down its complexity into the three dimensions: i) eHealth adoption, ii) partnership with advanced clinical practice, and iii) use and usability of eHealth. In order to better understand the momentum and mechanisms behind eHealth innovation the fourth dimension, iv) eHealth supporting services and means, needs to be studied. Conceptualising appropriate measurement instruments also requires eHealth innovation to be distinguished from eHealth sophistication, performance and quality, although innovation is intertwined with these concepts. The demanding effort for defining eHealth innovation and measuring it properly seem worthwhile and promise advances in creating better systems. This paper thus intends to stimulate the necessary discussion.

Factors affecting electronic medical record system adoption in small korean hospitals

OBJECTIVES

The objective of this paper is to investigate the factors affecting adoption of an Electronic Medical Record (EMR) system in small Korean hospitals.

METHODS

This study used survey data on adoption of EMR systems; data included that from various hospital organizational structures. The survey was conducted from April 10 to August 3, 2009. The response rate was 33.5% and the total number of small general hospitals was 144. Data were analyzed using the generalized estimating equation method to adjust for environmental clustering effects.

RESULTS

The adoption rate of EMR systems was 40.2% for all responding small hospitals. The study results indicate that IT infrastructure (OR, 1.48; 95% CI, 1.23 to 1.80) and organic hospital structure (OR, 1.86; 95% CI, 1.07 to 3.23) rather than mechanistic hospital structure or the number of hospitals within a county (OR, 1.08; 95% CI, 1.01 to 1.17) were critical factors for EMR adoption after controlling for various hospital covariates.

CONCLUSIONS

This study found that several managerial features of hospitals and one environmental factor were related to the adoption of EMR systems in small Korean hospitals. Considering that health information technology produces many positive health outcomes and that an 'adoption gap' regarding information technology exists in small clinical settings, healthcare policy makers should understand which organizational and environmental factors affect adoption of EMR systems and take action to financially support small hospitals during this transition.

Contrast-induced nephropathy in patients undergoing intravenous contrast-enhanced computed tomography in Korea: a multi-institutional study in 101487 patients

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 ± 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.

A quantitative method for assessment of prescribing patterns using electronic health records

Background

Most available quality indicators for hospitals are represented by simple ratios or proportions, and are limited to specific events. A generalized method that can be applied to diverse clinical events has not been developed. The aim of this study was to develop a simple method of evaluating physicians' prescription patterns for diverse events and their level of awareness of clinical practice guidelines.

Methods and Findings

We developed a quantitative method called Prescription pattern Around Clinical Event (PACE), which is applicable to electronic health records (EHRs). Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event. Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. We calculated the PCIs of 10 drugs related to hyperkalemia, categorized them into prescription patterns, and then compared the resulting prescription patterns with the known standards for hyperkalemia treatment. The hyperkalemia knowledge of physicians was estimated using a questionnaire and compared to the prescription pattern. Prescriptions for CDAD were also determined and compared to clinical knowledge. Clinical data of 1698, 348, and 1288 patients were collected from EHR data. The physicians prescribing behaviors for hyperkalemia and CDAD were concordant with the standard knowledge. Prescription patterns were well correlated with individual physicians' knowledge of hyperkalemia (κ = 0.714). Prescribing behaviors according to event severity or clinical condition were plotted as a simple summary graph.

Conclusion

The algorithm successfully assessed the prescribing patterns from the EHR data. The prescription patterns were well correlated with physicians' knowledge. We expect that this algorithm will enable quantification of prescribers' adherence to clinical guidelines and be used to facilitate improved prescribing practices.

The effects of an electronic medical record on the completeness of documentation in the anesthesia record

OBJECTIVES

The purpose of this study is to evaluate the completeness of anesthesia recording before and after the introduction of an electronic anesthesia record.

METHODS

The study was conducted in a Korean teaching hospital where the EMR was implemented in October 2008. One hundred paper anesthesia records from July to September 2008 and 150 electronic anesthesia records during the same period in 2009 were randomly sampled. Thirty-four essential items were selected out of all the anesthesia items and grouped into automatically transferred items and manual entry items. 1, .5 and 0 points were given for each item of complete entry, incomplete entry and no entry respectively. The completeness of documentation was defined as the sum of the scores. The influencing factors on the completeness of documentation were evaluated in total and by the groups.

RESULTS

The average completeness score of the electronic anesthesia records was 3.15% higher than that of the paper records. A multiple regression model showed the type of the anesthesia record was a significant factor on the completeness of anesthesia records in all items (β=.98, p<.05) and automatically transferred items (β=.56, p<.01). The type of the anesthesia records had no influence on the completeness in manual entry items.

CONCLUSIONS

The completeness of an anesthesia record was improved after the implementation of the electronic anesthesia record. The reuse of the data from the EMR was the main contributor to the improved completeness.

Monitor preference for electronic medical record in outpatient clinic

Objectives

The objective of this paper is to assess which wide type monitor configurations are preferred when physicians use an Electronic Medical Record (EMR) system in an outpatient clinic setting.

Methods

We selected three kinds of monitor configurations available for adoption at outpatient clinics with reference to monitor market trends. Fifteen attending physicians of the Seoul National University Bundang Hospital used each monitor configuration in their outpatient clinics. After completing the outpatient sessions, they selected the best monitor configuration for criteria described in five questionnaire items. We counted the number of votes and reviewed opinions of participants.

Results

The Wide Quad High Definition (WQHD) 27-inch single monitor configuration was most preferred for all questionnaire items. All participants answered that the WQHD 27-inch single monitor configuration was the best for desk space utilization. Eleven out of fifteen participants chose the WQHD 27-inch single monitor configuration as the most suitable monitor for outpatient practice.

Conclusions

This study found that physicians preferred the WQHD 27-inch single monitor configuration in outpatient clinic settings. Healthcare organizations need to consider this finding when they purchase wide type monitors for EMR systems instead of the standard type monitor.

How to measure the QoS of a web-based EHRs system: development of an instrument

The quality of service (QoS) can be treated as a set of concepts whose satisfaction/dissatisfaction generates a global positive/negative vision about the service provided by any application. The different nature of the services and its features require an analysis of the factors that have the greatest influence on the users' opinion and, therefore, measuring the quality of service in each application requires a specific instrument. This paper will introduce an instrument to measure the QoS offered to users by a general Web application for Electronic Health Records (EHRs). The collection of opinions from a pilot sample and the performance of an explanatory factor analysis will bring together the factors that best sum up the quality of an EHRs application. Subsequently, a confirmatory factor analysis will be performed to make the study reliable and, as its name suggests, to confirm that indeed the structure of the instrument developed measures the QoS in accordance with the requirements of the users.