Improving and Expanding Electronic Health Records in a Rural Setting

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IKP Center for Technologies in Public Health both additions as well as the methodology specific to ICTPH’s model; section four describes data collection and use; and section five concludes with limitations and future directions. Background Electronic Health Records EHR is a growing concept in healthcare IT. The first EHRs began to appear in the 1960s. The International Organization for Standardization defines EHR as a “repository of patient data in digital form, stored and exchanged securely, and accessible by multiple authorized users. It contains retrospective, concurrent, and prospective information and its primary purpose is to support continuing, efficient and quality integrated health care” (ISO definition, 2004). EHR is used primarily to assist the institution in setting objectives, planning patient care, documenting the delivery of care and assessing the outcomes of care (Hayrinen et al., 2008). There are numerous EHR software programs that health organizations can utilize to document patient information. All software differs somewhat in the information the EHRs may gather and maintain about patients. EHR software can contain combinations of medical data components such as referral information, present complaint (symptoms), past medical history, lifestyle, physical examination, diagnoses, tests, procedures, treatment, medication and discharge information (Valdes et al., 2004). The benefit of utilizing EHR in health care facilities is the belief that it will improve the quality of healthcare. EHR provides an improvement in legibility of clinical notes, easier and quicker access to patient information during appointments, reminders to prescribe drugs and administer vaccines, easier management of chronic diseases to name a few (Center for Health IT at the AAFP, 2010). EHR systems propose to help reduce medical errors by providing healthcare professionals with decision support. This can be achieved by various functions in the EHR system such as incorporating warnings alerting health professionals regarding problems of incompatible drugs (Fraser et al., 2005). Implementing EHR in a Rural Setting Development of EHR systems is still a new and challenging task even in resource-rich developed countries. EHR implementation requires extensive research regarding local needs and available resources and technologies. Developing countries have additional issues such as a lack of infrastructure and trained and experienced staff, which prohibit them from providing proper and needed healthcare to rural populations. It is believed that the implementation and utilization of EHR in a rural setting will improve health care decisions made (Fraser et al., 2005). This is especially important for rural India, where according to a 2001 census, 74% of the country’s population reside. There are various concerns sensitive to rural regions that must be addressed before implementing EHR systems in remote clinics. A lack of resources and technology is seen in many of these regions. There is also a greater lack of experience in creating EHR systems for developing countries; rural priorities, constraints and requirements are less well understood (Fraser et al., 2005). A few pilot projects have been conducted that test EHR implementation and effectiveness in developing countries. Projects tested in rural regions of Kenya, Haiti and Malawi show positive results in the use of these systems by healthcare workers in a poor country with virtually no infrastructure and with limited IT skills (Fraser et al., 2005). Clinical Decision Support System CDSS are computer systems designed to impact clinician decision-making about individual patients at the point in time that these decisions are made (Berner & La Lande, 2007). Characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician for a decision (Sim et al., 2001). Most CDSS exist as part of an EHR system or computer order entry system rather than as a stand-alone entity (Berner & La Lande, 2007). CDSS have been developed to assist with a variety of decisions, including diagnosis. In such a system, the patient’s signs and symptoms are entered either by the clinician or are imported directly from the EHR (Berner & La Lande, 2007). The system contains a knowledge-base with information 18 www.ictph.org.in
Improving and Expanding Electronic Health Records in a Rural Setting: Clinical Decision Support System & Public Health Component about diseases derived from the medical literature or expert consultation, allowing the inference engine to map the patients’ symptoms to those diseases and ultimately generate a diagnosis for the clinician to consider (Berner & La Lande, 2007). CDSS: Benefits and Drawbacks The evidence for the role of these systems in improving the safety, quality and efficiency of health care is growing. CDSS have been shown to improve the quality of medical care by helping clinicians comply with ever-changing guidelines and standards (National Electronic Decision Support Taskforce, 2003). This type of “evidence-adaptive” CDSS is one in which the clinical knowledge base of the CDSS is derived from and continually reflects the most up-to-date evidence from the research literature (Sim et al., 2001). Electronic decision support systems can also play a role in preventive care. A systematic review of 18 decision support systems within the primary care setting concluded that preventive care reminder systems are a reasonably effective intervention (Hunt et al., 1998). Likewise, of 21 trials evaluating reminder systems for prevention, CDSS was found to be beneficial in 76% of trials as measured by rates of screening, counseling vaccination, testing, medication use, or identification of at-risk behaviors (Garg et al., 2005). In studies of cardiovascular prevention in which physician performance was measured by blood pressure measurement, cholesterol assessment, identification of smoking, and use of cardio protective medications, 5 of 13 trials reported improvements with use of CDSS (Garg et al., 2005). CDSS used to assist in diagnosis can lead to significant improvements in diagnostic accuracy and improvement in decision confidence. One study that using a CDSS to diagnose acute chest pain reduced the number of unnecessary referrals to the coronary care unit from 35 per cent to 19 per cent (Jonsbu et al., 1993). Similarly, Friedman et al (1999) found that clinicians’ diagnostic performance improved from 39.5 per cent to 45.4 per cent when a CDSS was used to assist in the diagnosis of 36 difficult cases with known diagnoses. However, in 6 per cent of cases the clinician actually changed from the correct diagnosis to an incorrect diagnosis after consulting the system. Of 10 trials evaluating the effect of a diagnostic system on practitioner performance, the system was found to be beneficial in 40% of the trials (Garg et al, 2005). Among controlled trials testing CDSS performance in reducing unnecessary health care utilization as measured by frequency of redundant testing and unnecessary hospital admissions, 3 of 4 trials reported improvements (Garg et al., 2005). The effectiveness of CDSS is highly dependent on documentation within the EHR, which can affect the functionality and accuracy of the recommendations prompted by the decision support system. For example, one study found that due to important gaps in patients’ medical records, a CDSS designed to improve safe prescribing would have failed to warn about patients’ high-risk status over 77% of the time (Berner et al., 2005). Most studies of CDSS concern physicians to the near exclusion of other clinicians. A rare review of CDSS use among nurses reported that evidence-adaptive CDSS may be effective in assisting nurses with guideline-adherent care and in improving patient outcomes (Anderson & Willson, 2008). This same review cited such nurse-identified benefits of CDSS as improved interdisciplinary communication, increased access to information on best practice, and increased consistency in quality of care. Barriers to use included deficiencies in the EHR system, lack of administrative support, and the time required to learn and implement the new technology (Anderson & Willson, 2008). Implementing CDSS in a Rural Setting Controlled, systematic studies of the effectiveness of CDSS are a near anomaly in low-resource settings. The two CDSS that have been implemented in rural India and undergone review include the Early Diagnosis and Prevention System in Salem, Tamil Nadu (Peters et al., 2006) and RightChoice in Almora, Uttarakhand (Arora, 2010). The CDSS in rural Tamil Nadu proved to be a conservative screening tool, with a high sensitivity (80%) and low specificity (22%) (Peters et al., 2006). Utilization of services increased at intervention sites (those using CDSS) over control sites and CDSS was associated with consistent and statistically significant improvements in patient satisfaction (Peters et al., 2006). Use of CDSS seemed to have little impact on health workers (Peters et al., 2006). The CDSS in Almora is populated by data collected from a baseline survey. The author described problems in implementation related to the collection of this data, wherein most of the villagers’ chief complaints did not fit into pre-stated categories but www.ictph.org.in 19
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Improving and Expanding Electronic Health Records in a Rural Setting

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