The Impact Of Electronic Health Records On Patient Outcomes

The Impact Of Electronic Health Records On Patient Outcomes

Methodology

This research report is based on the topic use of technology in health care and how this technology impacts patient outcomes. It has been seen that technology has held a significant promise in health care and till now it has achieved some great achievements that helped to trigger a revolution in the healthcare industry (Thimbleby, 2013). An electronic health record is one such discovery of modern health science that significantly impacted the nursing practice as well as the quality of patient care. An electronic health record is one tool or platform that allows the nurses and other professionals to save the patient data rapidly which helps them to improve their practice as well as improve the patient outcome (Evans, 2016). In this study, the focus would be on finding the evidence in support of the selected research topic after formulating the research question by using the PICO format (Schiavenato & Chu, 2021). In the PICO format the P stands for population/the patient, intervention is denoted as I, C is the comparison compared with the intervention group and lastly, O stands for the outcome after applying the intervention (Schiavenato & Chu, 2021)The Impact Of Electronic Health Records On Patient Outcomes. The PICO question in this context could be: “What is the effectiveness of electronic health records in reducing medical error compared to standard technique?”

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Figure 1: PICO formulation used to develop the clinical question

CINAHL Plus with full text (Ebsco) is a comprehensive index of nursing and allied health literature, hence it was chosen for this study. The CINAHL Complete database enables the researchers in searching for articles in hundreds of nursing publications. CINAHL is an abbreviation for Cumulative Index to Nursing and Allied Health Literature. Evidence-based nursing sheets, short tutorials, proceedings, clinical studies and more can be found along with journal articles.  For searching out the research paper it is very crucial for the researcher to formulate a proper search strategy that would help them to find the exact article that they are searching for. Nursing researchers must establish a proper search strategy based on the research question or study purpose by developing relevant key phrases or search terms and a method for searching evidence by inputting those phrases into the database. The researchers will be able to obtain the essential evidence supporting the research subject if they construct search terms or similar phrases (Ho et al., 2016). In this context, the keywords or search terms that would be appropriate would be, “health technology’, ‘Electronic health record”, “electronic medical record”, “EHR”,” EMR”, “Positive outcome”, “medical error”.  To effectively utilize the search words in the search engine of the selected database chosen, the Boolean operator will be used in the next step of the search phrase creation. Furthermore, the Boolean operator helps the investigators in finding out specific research papers, which allows them to quickly implement the evidence-based practice (Ho et al., 2016)The Impact Of Electronic Health Records On Patient Outcomes. The Boolean operators ‘AND’, ‘IN’, “AMONG” and ‘OR’ may be utilized to efficiently search the results in the database. All of the articles were reviewed or filtered for selecting one particular piece of evidence, by applying a specific inclusion and exclusion criteria. The search terms were used by using the synonyms and by applying the Boolean operator and the search terms can be seen in the search history where S1, S2, S3, S4, S5, S6 and S7 could be seen.

Results

It is critical to have defined inclusion and exclusion criteria for choosing acceptable and legitimate research papers for evidence-based practice. The following are the inclusion requirements for this evidence-based study: the research paper must be published between 2016 and 2022 since current evidence is far more preferable because it is more useful and accepted in practical situations. The following conditions for admission are that the research study must be written in English. The research studies that were conducted before 2016 and research studies that were published in languages other than English were excluded from this analysis. The papers that fulfilled the inclusion criteria were chosen for this report’s review. The search phrases were typed into the library’s search box, and about 44,139 matches were returned. The inclusion standards have been developed, stating that only articles published after 2016 would be evaluated, yielding a total of 40,006 publications. All of the proofs were carefully analyzed and found to be relevant to the keywords. A total of 118 publications were appeared after applying the inclusion criteria and 13 articles were evaluated to check if they were suitable for this study. In this review of the literature, a single study has been chosen out of the ten suitable articles from the library search.

Figure 2: CINAHL Plus with full text (Ebsco) search history

(After entering the search terms S1-S7 and applying the full text, peer reviewed filter along with searching the evidences published after 2016, the research article: Impact of an electronic health record-integrated personal health record on patient participation in health care: development and randomized controlled trial of MyHealthKeeper)

The relevance of the paper selected in this context “Impact of an Electronic Health Record- Integrated Personal Health Record on Patient Participation in Health Care: Development and Randomized Controlled Trial of MyHealthKeeper” (Ryu et al., 2017) is relevant to the PICO question developed in this context. The intervention of the developed PICO question was electronic health records and in this research paper, the focus was also on PHR which was tethered with electronic health records that prove the relevance of the paper to the research question.

In this context, the article which has been selected was published by Ryu and colleagues in 2017, where the researchers stated that health care management systems that are based on personal health records can significantly improve the engagement of the patients as well as a data-driven clinical diagnosis in a health care setting that in turn improve the patient outcome The Impact Of Electronic Health Records On Patient Outcomes.

The purpose of the research was to illustrate the development of MyHealthKeeper which is a personal health record app that is tethered to an electronic health record. The researchers assessed if the app can retrieve information from a wearable device as well as deliver this information to a system of hospital electronic health records (EHR), as well as to find out the efficacy of a PHR data-triggered clinical intervention with the results of the clinical trial. By analyzing the research methodology of this study, it can be known that in an attempt to optimize the traditional EHR-tethered PHR, researchers conducted a co-creation workshop to determine clinicians’ unfulfilled needs in terms of PHR functionality and data often utilized in the field. The criteria were implemented within the MyHealthKeeper PHR module’s system design as well as architecture. By completing a 4-week clinical trial, researchers were able to build the app and evaluate the usefulness of the PHR module. Researchers collected individual physical activity data using an activity tracker (Misfit) which is a commercially available tool. The researchers moreover designed the MyHealthKeeper mobile phone application for tracking the daily food intake of the participants as well as their activity diaries. The trial lasted eight months in total. Two months were needed for system planning as well as an interaction design workshop to obtain physicians’ perspectives. The clinical study lasted four weeks just after the program was launched, and it took three months for implementing. 80 individuals were randomly assigned to 1 of the 2 groups: the first one was the PHR-based intervention group (n=51) and the second one is the control group (n=29)The Impact Of Electronic Health Records On Patient Outcomes. Each participant in this randomized controlled trial finished a laboratory test, a paper-based survey, a physical assessment, as well as an interview about their opinion. It was seen that the researchers collected all the mobile data related to the health during the study period of 4 weeks. The participants of the study also visited the outpatient clinic two times and got clinical diagnoses based on the PHR information as well as recommendations.

From the research findings of the study, it can be stated that the researchers included a total of 80 participants in the study. However, due to withdrawal and other factors, the participant number was reduced and 68 total participants finally complete the study where the researchers divided 44 into the intervention group as well as 24 participants into the control group. The researchers conducted the statistical analysis of the data collected and the findings of the study were presented as mean (SD). The researchers used the chi-square test to examine differences in several characteristics between the group of PHR-based intervention compared with the control. The researchers employed a paired t-test to compare the two groups’ primary and secondary findings. IBM SPSS version 18.0 (IBM Corporation) was used for statistical analysis, and P<.05 was found to be statistically significant. The overall findings of the research stated that the intervention group who used PHR showed higher weight loss significantly compared to the control group which was derived from the result of the 4th-week data (average 1.4 kg, 95% CI 0.9-1.9; P<.001). Furthermore, the result indicated that the levels of triglyceride were greatly lower at the completion of the study period that can be obtained from the result: mean of 2.59 mmol/L, 95% CI 17.6-75.8; P=.002.

From the aspect of bias of the study, it can be stated that any deviation in outcomes from the truth is referred to be biased. When evaluating therapeutic techniques, a Randomized Controlled Trial (RCT) is less prone to bias compared to designs of other studies (Pannucci & Wilkins, 2010). In this context, the selected study conducted an RCT and from that aspect, it can be stated that the chance of bias is less in this study. Although, just because this is a randomized trial it does not mean that it is unbiased. From the aspect of imbalance in baseline prognostic variables, Lack of intention to treat analysis, or missing data it can be stated that the researcher tried to cover all possible aspects of effective research and that reduced the risk of biasedness and made the research study a valid one (Deaton & Cartwright, 2018). In terms of sample size, the small size of the sample in this research might enhance the risk of bias and make the research less valid. As per the research of Blackford (2017), for exact, repeatable, as well as valid outcomes, the right sample size is very significant. Evidence derived from small sizes of samples is particularly prone to mistakes, both in terms of false negatives (type II errors) as well as false-positive rates (type I errors) owing to biased samples The Impact Of Electronic Health Records On Patient Outcomes.

In this research, it can be seen that the researchers structured the research into an EHR-tethered PHR module and they gave the name as MyHealthKeeper. The research was mainly focused on the implementation of this software, which in turn represents the usage of the technology in health care that suits the research topic selected in this context. Moreover, the application is tethered to an electronic health record framework which was the specific focus of this study. The implementation of the program was based on an EHR-friendly hospital and having a 12-year experience of the EHR use. The patient’s involvement is necessary for patient-generated health information, which is necessary for PMI adoption, according to this study. The researchers used a smartphone application and an activity tracking gadget to collect this patient-generated, and lifestyle-associated health information, then transmitted it to the PHR data server for making a summary view dependent on the physicians’ practical requirements. The MyHealthKeeper system was built with these needs in mind. They also conducted a 4-week clinical experiment to confirm the system’s efficacy. The trial’s findings revealed that PHR usage was associated with considerably bigger improvements in body weight as well as clinical markers, indicating a higher health condition than standard care (Ryu et al., 2017).

Every clinical research has its own strength as well as limitations (Evans, 2010). The strength of one research study indicated how well the researchers conducted the study and, in this context, the strength or one strong major point is that the researchers carried out the clinical trial for validating the efficacy of MyHealthKeeper, the PHR system mentioned throughout the study. The research was able to use the physical activity data from the PHR system collected from the activity tracker and that enhanced the validity of the research. On other hand, there are a few limitations of the study as well. The first thing is that the sample size of the research study was very small and it has been considered that small sample size may create it very challenging to measure if a specific outcome is a true finding (Faber & Fonseca, 2014). Furthermore, this study was not able to offer a longitudinal assessment of the EHR-tethered PHR system due to practical restrictions. It was impossible to establish a causative association due to the short clinical trial length, and the research did not give information about the particular increase in PHR users’ health outcomes.

In this study, the research question which was developed was: “What is the effectiveness of electronic health records in reducing medical error compared to standard technique?”

In this selected research article, the researchers focused on effectiveness of the personal health records tethered to electronic health records which completely justified the intervention selected in this context. The personal health record which saves the data electronically is more advantageous than paper documentation. From the aspects of outcome, it was seen that PHR also increase the patient participation that in turn not reduce the medical error that justified the PICO question. By assessing the report’s methodology, design of the study, as well as findings, including validity, limitations, and bias, the user may assess the data’s quality and determine its application to clinical practice. The ability of the researchers to exactly interpret the information described in the report determines the efficacy of clinical research (Bargaje et al., 2011)The Impact Of Electronic Health Records On Patient Outcomes.

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In the research, Ryu et al. (2017) stated that the personal health record is one of the most useful technological platforms that empower the patient population to track their health status and take the required actions. This in turn significantly improves the engagement of the patient in leading a healthy life as well as improves the patient outcome. From that aspect, it fulfilled the outcome section of the PICO question which was developed in this study. From the clinical applicability aspect of this research which means personal health record use it can be stated that PHRs are increasingly popular currently since they are a globally accessible, everlasting source of health information that individuals retain. Improved patient-provider interactions, more patient participation, and improved care safety, effectiveness, collaboration, and quality are just a few of the possible benefits of PHRs (Vance et al., 2015). From the research of Tang et al. (2006), it can be found that PHRs serves as a storehouse for patient information, but they may also incorporate decision-making tools to help patients manage chronic diseases. Because most clients and patients seek treatment from many healthcare personnel, their health information is spread among multiple institutions’ paper- and EHR-based record systems (Evans, 2016). A disjointed system for storing and accessing critical patient data makes it difficult to provide the best possible treatment.

Other research studies that are related to the personal health records and fulfill the clinical practice relevance of the same topic is one of the types of research conducted by Kahn et al. (2010), where the researchers performed a research study on the same technology platform PHRs which recognized that 80 percent of the users who use PHR are believed to get a significant help that assisted them to better manage the health issues of their own. Besides this clinical practice, another clinical research conducted by Archer et al. (2011) where the research was done on the application of personal health records and the research found that 74 percent of the physicians considered and accepted that personal health records could largely enhance the patient safety. According to the same survey, 40 percent of doctors believe personal health records might enhance productivity and save healthcare costs.

Reference

Archer, N., Fevrier-Thomas, U., Lokker, C., McKibbon, K. A., & Straus, S. E. (2011). Personal health records: a scoping review. Journal of the American Medical Informatics Association, 18(4), 515-522. https://doi.org/10.1136/amiajnl-2011-000105

Bargaje C. (2011). Good documentation practice in clinical research. Perspectives in clinical research, 2(2), 59–63. https://doi.org/10.4103/2229-3485.80368

Blackford, J. U. (2017). Leveraging statistical methods to improve validity and reproducibility of research findings. JAMA psychiatry, 74(2), 119-120. doi:10.1001/jamapsychiatry.2016.3730

Deaton, A., & Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine, 210, 2-21. https://doi.org/10.1016/j.socscimed.2017.12.005

Evans R. S. (2016). Electronic Health Records: Then, Now, and in the Future. Yearbook of medical informatics, Suppl 1(Suppl 1), S48–S61. https://doi.org/10.15265/IYS-2016-s006

Evans S. R. (2010). Clinical trial structures. Journal of experimental stroke & translational medicine, 3(1), 8–18. https://doi.org/10.6030/1939-067x-3.1.8

Faber, J., & Fonseca, L. M. (2014). How sample size influences research outcomes. Dental press journal of orthodontics, 19(4), 27–29. https://doi.org/10.1590/2176-9451.19.4.027-029.ebo

Ho, G. J., Liew, S. M., Ng, C. J., Hisham Shunmugam, R., & Glasziou, P. (2016). Development of a Search Strategy for an Evidence Based Retrieval Service. PloS one, 11(12), e0167170. https://doi.org/10.1371/journal.pone.0167170 The Impact Of Electronic Health Records On Patient Outcomes