Does Computerized Physician Order Entry Benefit from Dynamic Structured Data Entry? A Quasi-Experimental Study

Linn, George; Ying, Yung Hsiang; Chang, Koyin

doi:10.1186/s12911-018-0709-4

Cited by 5 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since prospective clinical research is more demanding and difficult to perform, retrospective research is an important means of obtaining clinical evidence. EMR data can be not only used as independent data, but also tied to administrative data for retrospective research [ 13 , 16 , 17 ], saving both time and money for medical institutions wishing to carry out such research studies with limited resources [ 18 , 19 ]. Thus, steps within the hospital should be taken to promote the awareness of this type of available research data, along with the encouragement to carry our medical research using these systems.…”

Section: Discussionmentioning

confidence: 99%

“…As such, EMR data are often used for research purposes in many universities and organizations around the world [ 14 , 15 ]. Using EMR data for medical research [ 16 , 17 ] has several benefits, such as being low cost, having a large volume of data, and saving time because there is no need to recruit and retain participants [ 18 - 21 ]. Thus, it is believed that using EMRs to obtain clinical information has the potential to revolutionize medical research in the coming years [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

Niu²,

Scott³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China. Objective This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data. Methods For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests. Results From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff. Conclusions The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

Niu²,

Scott³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…where we would create a 'perfect inaccurate problem list'. The patients were selected because 1) they have diagnoses that are categorized in the DT, 2) the diagnoses are contraindications with certain medical treatment in healthcare, which thus may harm a patient if incorrect decisions are made and 3) the diagnoses can be unambiguously differentially diagnosed and interpreted [53]. The expert panel together with researcher ESK also created two optimized correctly-structured problem lists for these two patient records, which was de ned according to our hospital problem list policy, with details in problem list notes or display elds where needed.…”

Section: Study Interventionmentioning

confidence: 99%

To assess the impact of problem lists' accuracy in electronic health records on clinical decision-making: protocol for a randomized controlled trial

Klappe

Heijmans

Groen

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Problem-oriented electronic health record (EHR) systems, with complete, coded and up-to-date (i.e. accurate) problem lists, aid healthcare providers to track patient’s health status and make better decisions, as problem lists can provide useful summaries of important health issues. Unfortunately, problem lists are still incomplete and out-of-date (i.e. inaccurate). That is, among others, because providers are under time pressure, prefer writing free-text notes and are often unwilling to update problem lists, unless receiving returns for their effort. This study aims to assess the impact of accuracy of problem lists in EHRs on clinical decision-making. Methods In a laboratory setting we will perform a crossover randomized controlled trial in which we will recruit individual Dutch healthcare providers on-site at Amsterdam University Medical Centers. Participants will be presented with two records of two patients (A and B): one with an accurate and one with an inaccurate problem list, created in a training environment of the EPIC EHR in agreement with clinical experts. Randomization determines which record will have the accurate problem list. Participants are informed that EHR-usage is investigated and do not know which record has an accurate or inaccurate problem list. Participants will provide a motivated (Yes/No) answer on whether prescribing medication X and medication Y is appropriate. Medication Y cannot be prescribed in both patient records, due to contraindicated diagnosis (A) and diagnosis-related medical history (B). Medication X serves as a control question, and is contraindicated based on an allergy, which is equally documented in the records. The primary outcome measure is the correctness of the motivation for the correct answer of medication Y. Secondary outcome measures are: correctness of X (and Y) with the right motivation(s); total time to answer X and Y where the motivation for Y (and X) is correct. Time stamps are registered between opening the question and confirming two Yes/No answers. Proportion of retrieved correct answers and time to answer(s) retrieval will be compared by Chi-square, McNemar tests and Log Rank survival analyses. Alternative analytical models will be applied if necessary. Discussion If accurate problem lists lead to faster and better decision-making resulting in better patient outcomes, one may be motivated to develop future policies for this area and healthcare providers may be persuaded to use and update problem lists, also leading to improved data quality and opportunities for reuse.

show abstract

“…As such, EMR data are often used for research purposes in many universities and organizations around the world [14,15]. Using EMR data for medical research [16,17] has several benefits, such as being low cost, having a large volume of data, and saving time because there is no need to recruit and retain participants [18][19][20][21]. Thus, it is believed that using EMRs to obtain clinical information has the potential to revolutionize medical research in the coming years [22,23].…”

Section: Introductionmentioning

confidence: 99%

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study (Preprint)

Li¹,

Niu²,

Scott³

et al. 2020

Preprint

View full text Add to dashboard Cite

BACKGROUND With the proliferation of electronic medical records systems (EMRs), there is an increasing interest in utilizing EMRs data for medical research, yet there is no quantitative research on EMRs data utilization for medical research purposes. OBJECTIVE Understand the current status of clinical data utilization in clinical research activities, including trends in recent years and differences between different populations, to find out the present problems in the use of EMR data for research, and provide a reference for promoting the utilization of EMR data in scientific research. METHODS For this descriptive, cross-sectional study, the utilization of EMRs data by staff at Xuanwu Hospital in Beijing, China between 2016 and 2019 was analyzed. The utilization of EMRs data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. The comparison by year, professional title, and age was conducted by double-sided chi square test. RESULTS From 2016 to 2019, EMRs data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times / person / year. The frequency per capita gradually slowed and more older, senior level staff used EMRs data compared to younger staff. CONCLUSIONS The value of using EMRs data for research purposes does not get enough attention among researchers in Chinese hospitals. Ensuring equal availability of EMRs data and highlighting the benefits of such systems can help promote its use in research settings. Future research should focus on mechanisms that encourage data utilization, ensure fair data availability, and promote data sharing.

show abstract

Does Computerized Physician Order Entry Benefit from Dynamic Structured Data Entry? A Quasi-Experimental Study

Cited by 5 publications

References 27 publications

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

To assess the impact of problem lists' accuracy in electronic health records on clinical decision-making: protocol for a randomized controlled trial

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study (Preprint)

Contact Info

Product

Resources

About