The management of patient data has been a primary concern in hospital settings due to the growing number of patients and lack of expertise a few decades before. Information regarding history, present clinical conditions and body mass index (BMI), age, and other valuable data is central in understanding and treating the disease. Failures in retrieving the information stored in paper format are common errors surrounding most hospitals.
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This was gradually circumvented by the intervention of a rapid tool that facilitates efficient patient data storage, retrieval, and overall maintenance. This tool is ‘Electronic Medical Records (EMR)’. With the adoption of emerging technologies into practice in Health care, a streamlined approach was expected to further modernize EMR.
Most often information stored in hospital audit records may also be subjected to privacy issues. This could raise doubts regarding the utility of audit records stored in electronic formats.
As such, there is a need to explore the reliability and validity of chart audits through a review of research literature. The main aim of the study was to assess the efficacy of possible measurement tools employed for patient data management with special emphasis on diabetic individuals. It was recently reported that the management of diabetes mellitus has improved due to the exploitation of electronic patient registries. This was revealed when information on a large number of diabetic patients from qualified health centers was entered into an electronic patient registry (Pollard et al., 2009).
The clinical outcome measurements were cholesterol and HbA1c, LDL, HDL, cholesterol, triglycerides, and blood pressure (Pollard et al., 2009). Apart from that, the other data included annual exams, screens to promote wellness, education, and self-management goal-setting. This setting helped the researchers to find significant improvements in cholesterol, LDL, and HbA1c levels (Pollard et al., 2009). This study is important for the present description as it highlights the importance of basic registry utilization in electronic format. In addition, it may help to better assess the association between cholesterol levels and cardiovascular problems in diabetic patients.
The limitation of this study is that there is limited information on the use of biochemical markers. So there is a need to further modify the electronic medical records with updated information on novel markers that provide more efficient clinical status.
This has strengthened an earlier report that evaluated the validity of measures for coronary artery disease (CAD) using an ambulatory Electronic health record (Persell et al., 2006). Here a retrospective chart review was made by comparing the automated measurement with a 2-step process of automated measurement using a commercial EHR. The measures involved low-density lipoprotein cholesterol control, antiplatelet drug, a lipid-lowering drug, beta-blocker following myocardial infarction, blood pressure measurement, lipid measurement, and angiotensin-converting enzyme inhibitor or angiotensin receptor blocker for patients with diabetes mellitus or left ventricular systolic dysfunction (Persell et al., 2006). This has resulted in the variation of performance from 81.6% for lipid measurement to 97.6% for blood pressure measurement based on automated measurement (Persell et al., 2006).
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Although this study appeared significant, its limitations were about the profiling strategies on the quality of outpatient CAD care using data from an EHR. Therefore, there is a need of improving the accuracy of quality measurement using this EHR too about the changes in the daily recording methodologies of data (Persell et al., 2006).
The areas where the above tools need to be modified were the use of the latest versions of robust database systems like MYSQL server and employing personnel on a contract basis till the system of patient data management produces good outcomes.
Earlier, computer assistance was believed to be needful in providing a reliable clinical decision- support in respiratory care (Gardner, 2004). This was in agreement with the concept of medical informatics where health care professionals objective was that electronic health records must be able to ameliorate computerized clinical decision-support(Gardner, 2004). This tool has beneficial content for this study as it presents a pyramid of progress concepts.
This involves gathering electronic health data into a standardized and coded format, validating the quality of that electronic health data, optimizing the presentation of electronic health data and exploring computerized decision-support, developing and sharing computerized knowledge bases that are based on clinical evidence, and tailoring and implementing the computerized strategies such that they are applied for direct patient care (Gardner, 2004).
Hence the tools involving computer aid appeared to provide better insights into electronic health information.
Next, the advancements in the field of molecular biology have brought forth increased attention on electronic medical records with special emphasis on genomic information. This could be because the completion of the human genome project has captured the interest of medical geneticists who would be exploring a vast number of hereditary disorders. As diabetes has a genetic link, there is a need to gain much information from the practice of genomic medicine and its implementation with EMR.
Cancer prognostics were considered to be the priority area for a genomic medicine paradigm, provided it is caused by genomic instability and microarrays have been developed to assess patients’ entire expressed genomes (Willard, Angrist & Ginsburg, 2005). The integration of clinical data with gene expression profiles, imaging, metabolomic profiles, and proteomic data, and the overall prospect for developing individual care could achieve progress in reality (Willard, Angrist & Ginsburg, 2005). As this success relies on analyzing health care economic and outcome data, EMR would appear a needful tool. The timely intervention of electronic medical records appears to hasten the retrieval of huge volumes of genomic data in a short period. Hoffman (2007) described that the genomic information of patients needs to be integrated into the electronic medical record (EMR) as it has implications to create a plethora of opportunities to ameliorate patient care.
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This in turn relies on the modification of laboratory information systems with the state of art infrastructure that has the potential to format molecular diagnostic and cytogenetic findings in the EMR (Hoffman, 2007).
This tool has better suitability to the present study as it has a modern outlook on patient data. It has better capability to dissect the relationship between the underlying cause and a clinical manifestation.
The limitations of this approach were considered to be the lack of granular genomics-related content in the existing medical vocabularies (Hoffman, 2007). The adoption of new standards for describing clinically relevant genomic information would become a critical stage toward recognizing the genome-enabled EMR. Hence, suitable capture of patient-specific genomic information in the EMR would more probably give rise to novel opportunities to exploit this information in clinical decision support, automated response to pharmacogenomic-based risks (Hoffman, 2007).
The area that needs modification is the system of efficient collaboration between various hospital settings and molecular biology researchers with special emphasis on EMRs that might ensure a smooth channel of genetic information exchange. This strengthens an earlier description that highlighted the impact of large-scale analysis of the genome, transcriptome, proteome, and metabolome on clinical medicine (Willard, Angrist & Ginsburg, 2005). Hence, it may indicate that the above two studies on genomic medicine are intended to focus on the specific population who are individuals with various genetic disorders.
The next aspect of this study is about the standards of electronic health records that play a vital role during implementation. It was reported that to ensure quality and low-cost health care without medical mistakes there is a need for stability of person-centric healthcare information in various healthcare settings (Westra et al.,2008).
To this end, widespread adoption of electronic health records (EHRs) was called to strengthen National Health Information Network by 2014 (Westra et al., 2008).
This is because the intervention of modern technology would support the development, exchange, and communication of nursing data (Westra et al., 2008).
As a result, vital components of quality care such as nurse-sensitive data, information, and knowledge would facilitate smooth interoperable clinical information (Westra et al., 2008).
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This tool of standardization is important in the present study as it furnishes better insights on the management of specific populations such as individuals with cancer, diabetes, cardiovascular and respiratory problems where vast data storage appears central to health care professionals.
Hence, nurses would gain better access to clinical information systems and could minimize the errors during data retrieval. This might help to obtain significant statistical information like mean, standard deviation, regression analysis which ultimately could enable to conduct of metanalysis and random studies.
Carek et al (2009) described that clinical practice guidelines would serve as tools to minimize variation and enhance clinical outcomes in performance-improvement settings. This could be better facilitated by relevant educational interventions for specific medical conditions and services on the specific quality of care indicators(Carek et al., 2009).
Medical residency programs rely on the assistance of chart reviews with a scope of evaluating quality indicators for selected disease conditions (Carek et al., 2009). This was reported to significantly improve some of the presentations like documentation of oral examination in children and in patients with chronic illnesses, efforts to reduce medications in patients with anxiety disorders, compliance with measuring HgbA1C in patients with diabetes mellitus (Carek et al., 2009).
The limitation of this tool is that chart audits that reflect specific quality indicators concerned with frequent family medical problems had an inconsistent effect on the practice behaviors of family medicine residents (Carek et al., 2009). Therefore, there is a need to further promote education interventions to enhance the utility of clinical practice guidelines as valuable tools.
This study is suitable for the present review because there was limited literature on the usefulness of clinical guidelines as tools and the need for educational interventions.
As such, it appears worth supporting the present description. Further, there is a need to know about the barriers that obstruct the smooth progress of EMRs especially in developed countries like the U.S., Canada, Australia, New Zealand, and those Europe (Anderson, 2007). These barriers are better connected to social, ethical, and legal issues(Anderson, 2007). This is because EMRs and the associated electronic prescribing and decision support systems were already recognized as essential tools in these nations (Anderson, 2007). It was revealed that although physicians gain access to the advantages of information technology, the interference of the above-mentioned barriers blocks the implementation strategies in their practices(Anderson, 2007). The outcome is that there would be no access to the capital by the health care providers and complex systems, and a blockage of data standards that facilitate the exchange of clinical data, privacy concerns, and legal barriers (Anderson, 2007). Hence, much emphasis was given to barrier prevention strategies that rely on subsidies and pay for performance incentives by payers and government, certification and standardization of vendor applications that ensure exchange of clinical data, removal of legal barriers, and greater security of medical data to persuade the practitioners and patients of the value of EMRs(Anderson, 2007).
This study appears suitable to the present description as it has provided reliable information about the barriers that induce the problems in reality in clinical practice. Given the above information, the advent of technology in the medical field has incorporated changes with the inception of electronic medical records (EMRs) or electronic health records (EHRs). It has implications to influence specific population groups presenting diabetes in association with cardiovascular problems. Further studies that emphasize the transformation of large volumes of chart audits into electronic information are recommended.
Pollard, C., Bailey, K.A., Petitte, T., Baus, A., Swim, M., Hendryx, M. (2009). Electronic patient registries improve diabetes care and clinical outcomes in rural community health centers. J Rural Health, 25, 77-84.
Persell, S.D., Wright, J.M., Thompson, J.A., Kmetik, K.S., Baker, D.W. (2007).Assessing the validity of national quality measures for coronary artery disease using an electronic health record. Arch Intern Med, 167,971-2.
Gardner, R.M. (2004). Computerized clinical decision-support in respiratory care. Respir Care, 49,378-86.
Hoffman, M.A. (2007). The genome-enabled electronic medical record. J Biomed Inform, 40, 44-6.
Willard, H.F., Angrist, M., Ginsburg, G.S. (2005). Genomic medicine: genetic variation and its impact on the future of health care. Philos Trans R Soc Lond B Biol Sci, 360, 1543-50.
Westra, B.L., Delaney, C.W, Konicek, D., Keenan, G. (2008). Nursing standards to support the electronic health record. Nurs Outlook, 56,258-266.
Carek, P.J., Dickerson, L.M., Boggan, H., Diaz, V. (2009). A limited effect on performance indicators from resident-initiated chart audits and clinical guideline education. Fam Med, 41,249-54
Anderson, J.G. (2007). Social, ethical and legal barriers to e-health. Int J Med Inform,5-6,480-3
J Rural Health. 2009 ;25(1):77-84. Links
Electronic patient registries improve diabetes care and clinical outcomes in rural community health centers.Pollard C, Bailey KA, Petitte T, Baus A, Swim M, Hendryx M. Office of Health Services Research, West Virginia University, Morgantown, W VA 26506-9190, USA.
CONTEXT: Diabetes care is challenging in rural areas. Research has shown that the utilization of electronic patient registries improves care; however, improvements generally have been described in combination with other ongoing interventions. The level of basic registry utilization sufficient for positive change is unknown. PURPOSE: The goal of the current study was to examine differential effects of basic registry utilization on diabetes care processes and clinical outcomes according to level of registry use in a rural setting. METHODS: Patients with diabetes (N = 661) from 6 Federally Qualified Health Centers in rural West Virginia were entered into an electronic patient registry. Data from pre- and post-registry were compared among 3 treatment and control groups that had different levels of registry utilization: low, medium, or high (for example, variations in the use of registry-generated progress notes examined at the point-of-care and in the accuracy of registry-generated summary reports to track patients’ care). Data included care processes (annual exams, screens to promote wellness, education, and self-management goal-setting) and clinical outcomes (HbA1c, LDL, HDL, cholesterol, triglycerides, blood pressure). FINDINGS: The registry assisted in significantly improving 12 of 13 care processes and 3 of 6 clinical outcomes (HbA1c, LDL, cholesterol) for patients exposed to at least medium levels of registry utilization, but not for the controls. For example, the percent of patients who had received an annual eye exam at follow-up was 11%, 34%, and 38% for the low, medium, and high utilization groups, respectively; only the latter groups improved. CONCLUSIONS: As an initial step to achieving control of diabetes, basic registry utilization may be sufficient to drive improvements in provider-patient care processes and in patient outcomes in rural clinics with few resources.
Arch Intern Med. 2006 Nov 13; 166(20):2272-7. Links
Arch Intern Med. 2007 May 14;167(9):971-2.
Assessing the validity of national quality measures for coronary artery disease using an electronic health record.Persell SD, Wright JM, Thompson JA, Kmetik KS, Baker DW.
Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611-2927, USA. email@example.com
BACKGROUND: Nationally endorsed, clinical performance measures are available that allow for quality reporting using electronic health records (EHRs). To our knowledge, how well they reflect actual quality of care has not been studied. We sought to evaluate the validity of performance measures for coronary artery disease (CAD) using an ambulatory EHR. METHODS: We performed a retrospective electronic medical chart review comparing automated measurement with a 2-step process of automated measurement supplemented by review of free-text notes for apparent quality failures for all patients with CAD from a large internal medicine practice using a commercial EHR. The 7 performance measures included the following: antiplatelet drug, lipid-lowering drug, beta-blocker following myocardial infarction, blood pressure measurement, lipid measurement, low-density lipoprotein cholesterol control, and angiotensin-converting enzyme inhibitor or angiotensin receptor blocker for patients with diabetes mellitus or left ventricular systolic dysfunction. RESULTS: Performance varied from 81.6% for lipid measurement to 97.6% for blood pressure measurement based on automated measurement. A review of free-text notes for cases failing an automated measure revealed that misclassification was common and that 15% to 81% of apparent quality failures either satisfied the performance measure or met valid exclusion criteria. After including free-text data, the adherence rate ranged from 87.5% for lipid measurement and low-density lipoprotein cholesterol control to 99.2% for blood pressure measurement.
CONCLUSIONS: Profiling the quality of outpatient CAD care using data from an EHR has significant limitations. Changes in how data are routinely recorded in an EHR are needed to improve the accuracy of this type of quality measurement. Validity testing in different settings is required.
1: Am J Med Qual. 2006 Jan-Feb;21(1):13-7. Links
Quality of outpatient care for diabetes mellitus in a national electronic health record network.Gill JM, Foy AJ Jr, Ling Y.
Christiana Care Health System, Family and Community Medicine, Wilmington, DE, USA. firstname.lastname@example.org
This retrospective cohort study examined quality of care for diabetes in a large national network of electronic health record users. Of 10572 patients with diabetes included in the study, 55% had at least 2 hemoglobin A1c (HbA1c) tests, 95% had at least 1 systolic and diastolic blood pressure test, and 52% had at least 1 low-density lipoprotein (LDL) cholesterol test over a 1-year period. Of those tested, 41% had an HbA1c<7.0, 28% had a blood pressure<130/80 mm Hg, and 44% had an LDL cholesterol level<100 mg/dL. Of those not adequately controlled, 99% were prescribed hypoglycemic medications, 85% were prescribed antihypertensive medications, and 71% were prescribed lipid-lowering medications. These results suggest that there is significant room for improvement in testing and control of risk factors for persons with diabetes and that the electronic health record has a significant potential for conducting practice-based quality-of-care studies across large numbers of outpatient practices. PMID: 16401701 Ann Fam Med. 2005 Jul-Aug;3(4):300-6. Links Impact of an electronic medical record on diabetes quality of care.O'Connor PJ, Crain AL, Rush WA, Sperl-Hillen JM, Gutenkauf JJ, Duncan JE. HealthPartners Medical Group and HealthPartners Research Foundation, Minneapolis, MN 55440-1524, USA. email@example.com PURPOSE: This study was designed to evaluate the impact of electronic medical record (EMR) implementation on quality of diabetes care. METHODS: We conducted a 5-year longitudinal study of 122 adults with diabetes mellitus at an intervention (EMR) clinic and a comparison (non-EMR) clinic. Clinics had similarly trained primary care physicians, similar patient populations, and used a common diabetes care guideline that emphasized the importance of glucose control. The EMR provided basic decision support, including prompts and reminders for diabetes care. Preintervention and postintervention frequency of testing for glycated hemoglobin (HbA1c) and low-density lipoprotein (LDL) levels were compared with and without adjustment for patient age, sex, comorbidity, and baseline HbA1c level. RESULTS: Frequency of HbA1c tests increased at the EMR clinic compared with the frequency at the non-EMR clinic (P <.001). HbA1c levels improved in both clinics (P <.05) with no significant differences between clinics 2 years (P =.10) or 4 years (P =.27) after EMR implementation. Similar results were observed for LDL levels. CONCLUSIONS: In this controlled study, EMR use led to an increased number of HbA1c and LDL tests but not to better metabolic control. If EMRs are to fulfill their promise as care improvement tools, improved implementation strategies and more sophisticated clinical decision support may be needed. PMID: 16046561