Medical Information System

Medical Informatics

Introduction :

The intention of this paper is to critically analyze important aspects of Medical Information System, particularly Medical Informatics. In the course of discussion, it is proposed to analyze topics such as information retrieval, the computer -based patient records and medical terminology relating the subject matter dealt with in the paper. Medical records are maintained in different forms such as paper based, document-based, concept-based, computer based and so on. In this paper it is the “problem oriented” medical report about diabetes treatment that is considered. The choice of diabetic management as an illustrative subject of medical informatics is given in the ‘order topic' of this paper itself. To keep track of this treatment regime the patients' medical records need to be monitored and also retrieved. In the process of information retrieval, both “recall' and “precision” are necessary elements to adhere to; these will be explained subsequently. Here a few key words are defined to show relatively clarity to them.

Medical Informatics:

A simplistic definition of medical informatics is that it is the use of computers in the practice of medicine, ranging from clinical systems, information resources, telemedicine, etc. Computers are extensively used in medical informatics in areas such as ‘Signal processing - ECG, electroencephalography (EEG), electromyography (EMG) analysis; Image processing - Radiography, US, CT scanning, MRI/magnetic resonance angiography (MRA), single photon emission computed tomography (SPECT) scanning/positron emission tomography (PET) scanning, cerebral angiography; Computerized patient records; Decision support systems; Telemedicine; and Internet and web-based medical communications' (Dick et al 1997). Particular relevance for this paper is that of computerized patient records (CPRs) for effective medical care. In 1991, the Institute of Medicine (IOM) brought out the report entitled The Computer-Based Patient Record: An Essential Technology for Health Care, which described the CPR as electronically maintained information about an individual's lifetime health status and health care. “This system is used to capture, storage, processing, communication, security, and presentation of non-redundant health information and provides accurate patient data, clinical reminders and alerts, decision support, and links to bodies of related data and knowledge bases. CPRs or electronic health records (EHRs) are a very important part of medical practice” (Dick et al 1997).

Some of the other advantages of the CPR system relate to simultaneous, remote access to patient data by many clinicians from many locations; clarity of records than in handwritten data, more reliable and less prone to data loss than paper-based records; linking the patient's data in electronic form to reference information stored and maintained locally or, via the internet, half-way around the world; and finally the data is always up to date. (Powsner et al 1998).

Definitions of other terms, if found necessary, would be provided at the appropriate places. Electronically stored medical informatics need to be retrieved whenever it is required to be acted upon. I discuss the issue if information retrieval in the next section.

Information Retrieval:

Information retrieval (IR) is ‘the science of searching for documents, for information'. The information may be embedded within a document or many documents, the search may be for data about data or about documents [metadata], and/or about related databases and information search in the Internet, the World Wide Web. IR seeks the help of many disciplines such as computer science, mathematics, library science, information science, information architecture, linguistics, statistics, and physics, as it is an inter-disciplinary science. Many works are available relating to medical information retrieval, normally supported by a full or a semi-automated adjustment of documents helping the acquisition of information, but not the evaluation of the data acquired.

The process of an information retrieval starts into the system when a user enters a query, for example entering a key-word or a relevant query in web search engines. The IR system matches the query with the database and ranks the documents according to a value it has generated from the query. ‘Data' is presented according to the ranking, the top ranking ones leading the rest. If the user refines the query, the process may be reiterated (Frakes 1992).

A measure that is often used to evaluate the effectiveness of information retrieval systems is that of ‘recall and precision'. This measure requires a query and the resulting collection of documents. Precision may be said to be a quotient of the relevant documents to the retrieved documents; that is: Precision = number of documents retrieved and relevant / number of documents retrieved. Precision takes all retrieved documents as the denominator and the relevant document as the numerator and the resulting value of the division gives the precision value. It is always a positive value. Recall is the proportion of the relevant documents contained in the retrieved documents; that is, it may be said to be the quotient of retrieved documents to relevant documents: Recall = number of documents retrieved and relevant/ number of relevant documents in database. Due to this the probability of a relevant document is being restored by the query. If there is a single query and if the retrieval result generated for the query is a linear ordering, the ‘recall and precision' of IR is easy to gauge. But, if the retrieval results are ‘weakly ordered' in the sense that many documents have an equal retrieval status value with respect to a query, some probabilistic idea of precision has to be introduced. Terms such as relevance probability and expected precision are among some terms used in the literature for this purpose.

Computer software is available for an effective monitoring of medical information retrieval. For instance, Peng Dong and associates describe the use of one such software. According to them, they found that a web-based application, namely the CAT Crawler, was developed by Singapore's Bioinformatics Institute to allow physicians to access available appraised topics on the Internet. Its operation was found to improve precision due to inherent filters which underlines the practical usefulness of this tool for clinicians” (Dong et al 2004).

Diabetes and Medical informatics:

In the above paragraphs a few important aspects of medical informatics have been discussed. In the remaining paragraphs of the paper it is proposed to illustrate these medical informatics aspects being applied to the pathology of diabetes.

Diabetes is a polygenic disease characterized by abnormally high glucose levels in the blood due to a deficiency in insulin production or utilization. Many instances of use of medical informatics in the management of diabetes have been recorded and are available in the literature on the subject. Five such case studies are given in summary in this section.

First is a study on the management of blood pressure in diabetic patients. It is well recognized that in the in the management of diabetes, one of the goals is to achieve a blood pressure level of less than or equal to 130/80 mm Hg in the patient. Two physicians, George L. Bakris, MD and Matthew R. Weir, MD report their findings on the treatment of diabetic patients with blood pressure reducing drugs to achieve the target level of 130/80 mm Hg blood pressure. They found significant difference in the length of time taken to achieve the target level BP when treated with different drugs meant for high blood pressure. Their report says that the average time taken to achieve the goal of BP <130/85 mm Hg was shorter in the case of those who received fixed-dose combination therapy with amlodipine/ benazepril than those who received the conventional approach (enalapril monotherapy): 5.3±3.1 weeks vs. 6.4±3.8 weeks, respectively; p=0.0001. The median time to target BP was 4 weeks in the amlodipine/benazepril group and 6 weeks in the enalapril group. Successful ttreatment success was defined as the first achievement of the target blood pressure of <130/85 mm Hg. The percentage of amlodipine/benazepril-treated participants who achieved the target level of BP exceeded that of enalapril-treated participants at every assessment. By week 12, the percentage of participants achieving treatment goal was 63% (n=64/106) among those receiving amlodipine/benazepril combination therapy, compared with 37% (n=35/108) among participants receiving enalapril monotherapy (p=0.0002).The percentage of participants who achieved a BP of 130/80 mm Hg at week 4 was 36% in the amlodipine/ benazepril group, compared with 8% in the enalapril group, rising to 59% and 19%, respectively, at week 8, and 70% and 31% at week 12. With respect to change in serum triglyceride levels from baseline to week 12 ( of participants in both treatment groups who received hydrochlorothiazide [HCTZ] at week 8), the amlodipine/benazepril group participants had a mean decrease of 21.7 mg/dL, compared with a mean increase of 14.8 mg/dL among the enalapril group participants, a statistically significant difference (Bakris & Weir, n.d.).

The second study is a report on evaluating the impact of an integrated patient-specific electronic clinical reminder system on diabetes and coronary artery disease (CAD) care and to assess physician attitudes toward this reminder system by Dr Sequist and associates (2005). The target group for the study was 194 primary care physicians caring for 4549 patients with diabetes and at 20 ambulatory clinics 2199 patients with CAD. Within the patients electronic medical record physicians were used to supply with evidence-based electronic reminders. In case of diabetes care five reminders and in case of CAD four reminders were recorded and the final result was used for diabetes and CAD. The researchers created a summary outcome to assess the probability of progressed compliance with diabetes care based on five measurement criteria and overall CAD care based on four measurement criteria. These results showed that “baseline adherence rates to all quality measures were low. Electronic reminders increased the chances of recommended diabetes care (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.01-1.67) and CAD (OR 1.25, 95% CI 1.01-1.55), the impact of individual reminders was variable. Out of nine reminders three reminders are effectively increased by the rates of recommended care for diabetes or CAD. On this basis the researchers concluded that an “an integrated electronic reminder system resulted in variable improvement in care for diabetes and CAD” (Sequist et al 2005).

The provocation for this study was their conviction that Electronic health records play an important role in improving health care quality through progressing an access to patient information at the point of care and standardizing clinical decision making. This system is used to progressed rates of cancer screening and adult immunizations. Electronic reminders are used to provide patient-specific recommendations within the electronic record that have immediate response to new or updated information for example a new diagnosis of diabetes or coronary artery disease, a new laboratory result, or new medication prescriptions. The authors “developed an electronic reminder system for diabetes and coronary artery disease management within an existing electronic health record with the goals of (1) assessing the impact of this system on quality of care and (2) understanding physician attitudes toward this system” (Sequist et al 2005).

. The third is a summary of a research report by Anthony Heymann and associates about implementing large preferred provider Organization for medical informatics which is used for diabetes. The background of their research study was based on the demonstration by meta analysis if the standard of primary care could be better than hospital outpatient care when the review of patients was guaranteed. And the data that is suggested that compliance with diabetes clinical practice were inadequate in primary care and by using disease management principles in a Preferred Provider Organization (PPO) working on a country-wide basis it describes the reorganization of diabetes care in which every diabetes clinic was responsible for the overall care of all patients with diabetes. In a large public-funded PPO there will be detailed description of pre and post change study was undertaken insuring over one and half million individuals. Due to the use of a centralized electronic disease registry this study is used to gather all patient data. For diabetic patient to access the quality of care many indicators are used such as HbA1C and LDC-cholesterol levels. The study results demonstrated that mean HbA1C results of the patients showed continuous decrease from 8.1% (SD = 1.55) in 1999 to 7.68% (SD = 1.47) in 2002 and to 7.79 (SD = 1.54) in 2004. The results which were more better than compare to others were also recorded for LDL-C 126.37 (SD = 35.16) in 1999 to 114.74 (SD = 34.49) in 2002 and to 113.39 (SD = 33.8) in 2004. By the specialist physician (diabetologist) the number of diabetic patients progressed by 62% over this period, whereas there will be despite increase in diabetologist work hours by only 23%. The researchers concluded from their study improves quality of care where the “reorganization of health delivery for diabetic patients within a country-wide PPO, based on the principles of disease management and supported by medical informatics.” (Heymann et al 2006).

The fourth is ‘an audit of diabetic patients suffering from ischemic heart disease in a primary care setting' reported by Ivo Dukic. A clinical audit is a process to improve patient care and outcome through systematic review of care against unambiguous criteria and the implementation of change. People who have been diagnosed with diabetes mellitus (DM) and ischemic heart disease (IHD) and provided health care in the Kingsway Group Practice, Manchester, United Kingdom were the subjects of the audit, whose objective was to identify areas of care that need improving and recommend changes if needed. The audit related to blood pressure measurement, monitoring the body mass index (BMI) of the population, assessing the percentage of patients who have good blood glucose control and measurement of HbA1c levels, assessing blood lipid control and management and adequacy of recording. The audit was based on the computer data available for each patient. The audit was done for 5646 patients of which 173 had DM [type 1 and 2] (3.06%), 274 had IHD (4.85%) and 42 had both DM and IHD (0.74%). 26% of patients did not have a recorded BMI, height or weight on the computer system. With respect to blood pressure, 7% of the patients had no record about it in the computer system; 62% had a systolic blood pressure above 140 mmHg; and 46% had a diastolic pressure above 80 mmHg; which implied that the majority of patients had a blood pressure above the recommended level. Hemoglobin A1c is a measure of the nonenzymatic glycosylation of hemoglobin A in the blood stream. “It is a long term measure of glucose control in the blood stream. In this audit 42.5% of patients had HbA1c below 6.5%. In 37.5% of patients the HbA1c was above 6.5% but below 7.5% and in 20% it was above 7.5%. 5% had not had their HbA1c levels checked or recorded on the computer system. The audit found 80% of patients had HbA1c levels below 7.5% suggesting good glucose control. IHD is the major cause of morbidity and mortality for diabetic patients (type 1 and type 2) (Dukic, n. d.).

The last in this series of reports is an account of a reported by Dr. Adrian Zai and associates (2008) population management (RPM) application, and a number of points that include information before building an electronic diabetes registry. What prompted them to devise such a registry was the “shortcomings surrounding the care of patients with diabetes (which) have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease” (Zai et al 2008). In order to solve this problem they developed a diabetes registry population management application to alter the physicians manage patients with diabetes. A registry is generally a list of number of patients where it is used to share some common characteristics and belong to a practice of one or more physicians. They claim that registries can provide clinical reminders and identify patients not meeting specific clinical metrics. Registries are used in five ways: (i) On clinical metrics or end points it is used to generate performance feedback reports to physicians(ii) to provide physicians with exception reports that identify patients who are noncompliant; (iii) to create point-of-care clinician reminders that summarize a patient's care and identify any tasks that are due; (iv) to generate patient reminders that are mailed to the patient when a specific task must be done; and (v) to identify high-risk patients who require health care resources for more intensive management. On the basis of who performed what aspects of care, how often, in what context, and in what order, they designed a new workflow for managing diabetes care at hospitals. The authors claim that their new RPM has rectified some of the deficiencies of the existing system such as the inefficient tracking and managing of pay-for-performance patient data; the unclear effectiveness of interventions; the ‘no tracking and fewer interventions for non-pay-for-performance patients; and the poor information exchange between the General Physicians Organizations and practices regarding patient contact. The authors claim that helped to “revamp the established workflow for diabetes management by building the RPM application and by integrating the diabetes registry into the workflow they helped to increase efficiency and thereby successfully decreased the time needed to notify patients for laboratory work by approximately ten-fold” (Zai et al 2008)..


In concluding this paper on medical informatics, it is recalled that I have discussed the important aspects of this subject including its need and relevance for an effective intervention in the administration of health care. The requirements of recall and precision as essential elements in medical informatics have been adequately explained. Different aspects of application of informatics have been illustrated in respect of treatment of diabetes. Medical informatics has become an inherent ingredient of modern therapeutic practice.


Bakris, George L. and Matthew R. Weir, Achieving Goal Blood Pressure in Patients With Type 2 Diabetes: Results, viewed 5 February 2010, at < ->

Dick, Richard S., Elaine B. Steen, and Don E. Detmer, Editors, 1997: The Computer-based Patient Record: An Essential Technology for Health Care, Committee on Improving the Patient Record, Division of Health Care Services, Institute of Medicine; Washington, D.C., National Academy Press

Dong, Peng, Ling Ling Wong, Sarah Ng, Marie Loh and Adrian Mondry, 2004: Quantitative evaluation of recall and precision of CAT Crawler, a search engine specialized on retrieval of Critically Appraised Topics; BMC Medical Informatics and Decision Making, 2004, 4:21; viewed 3 February 2010, at <>

Dukic, Ivo: An audit of diabetic patients suffering from ischemic heart disease in a primary care setting; viewed 6 February 2010, at < ->

Frakes, William B. (1992). Information Retrieval Data Structures & Algorithms, Prentice-Hall, Inc.. viewed 3 February 2010, at <>

Heymann, A, G. Chodick, V. Shalev, E. Kokia, 2006: The implementation of managed care for diabetes using medical informatics in a large Preferred Provider Organization
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Powsner SM, Wyatt JC, Wright P, 1998:. Opportunities for and challenges of computerization; The Lancet 352:1617-1622; 1998 viewed 4 February 2010, available at

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Sequist,TD, Gandhi, TK, Karson, AS, Fiskio, JM, Michael, DB, and others, 2005: A Randomized Trial of Electronic Reminders to Improve Qyality of Care for Diabetes and Coronary Artery Disease; Journal of the American Medical Informatics Association Volume 12 Number 4 Jul / Aug 2005

Zai, Adrian H., Richard W. Grant, Greg Estey, William T. Lester, Carl T. Andrews, Ronnie Yee, Elizabeth Mort, and Henry C. Chueh, 2008: “Lessons from Implementing a Combined Workflow-Informatics System for Diabetes Management”, Journal of American Medical Informatics Association, 2008 Jul-Aug; 15(4): 524-533., available at

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