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      Modeling return on investment for an electronic medical record system in Lilongwe, Malawi

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          Abstract

          Objective

          To model the financial effects of implementing a hospital-wide electronic medical record (EMR) system in a tertiary facility in Malawi.

          Materials and Methods

          We evaluated three areas of impact: length of stay, transcription time, and laboratory use. We collected data on expenditures in these categories under the paper-based (pre-EMR) system, and then estimated reductions in each category based on findings from EMR systems in the USA and backed by ambulatory data from low-income settings. We compared these potential savings accrued over a period of 5 years with the costs of implementing the touchscreen point-of-care EMR system at that site.

          Results

          Estimated cost savings in length of stay, transcription time, and laboratory use totaled US$284 395 annually. When compared with the costs of installing and sustaining the EMR system, there is a net financial gain by the third year of operation. Over 5 years the estimated net benefit was US$613 681.

          Discussion

          Despite considering only three categories of savings, this analysis demonstrates the potential financial benefits of EMR systems in low-income settings. The results are robust to higher discount rates, and a net benefit is realized even under more conservative assumptions.

          Conclusions

          This model demonstrates that financial benefits could be realized with an EMR system in a low-income setting. Further studies will examine these and other categories in greater detail, study the financial effects at different levels of organization, and benefit from post-implementation data. This model will be further improved by substituting its assumptions for evidence as we conduct more detailed studies.

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          Most cited references28

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          A cost-benefit analysis of electronic medical records in primary care.

          Electronic medical record systems improve the quality of patient care and decrease medical errors, but their financial effects have not been as well documented. The purpose of this study was to estimate the net financial benefit or cost of implementing electronic medical record systems in primary care. We performed a cost-benefit study to analyze the financial effects of electronic medical record systems in ambulatory primary care settings from the perspective of the health care organization. Data were obtained from studies at our institution and from the published literature. The reference strategy for comparisons was the traditional paper-based medical record. The primary outcome measure was the net financial benefit or cost per primary care physician for a 5-year period. The estimated net benefit from using an electronic medical record for a 5-year period was 86,400 US dollars per provider. Benefits accrue primarily from savings in drug expenditures, improved utilization of radiology tests, better capture of charges, and decreased billing errors. In one-way sensitivity analyses, the model was most sensitive to the proportion of patients whose care was capitated; the net benefit varied from a low of 8400 US dollars to a high of 140,100 US dollars . A five-way sensitivity analysis with the most pessimistic and optimistic assumptions showed results ranging from a 2300 US dollars net cost to a 330,900 US dollars net benefit. Implementation of an electronic medical record system in primary care can result in a positive financial return on investment to the health care organization. The magnitude of the return is sensitive to several key factors. Copyright 2003 by Excerpta Medica Inc.
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            Early infant diagnosis of HIV infection in Zambia through mobile phone texting of blood test results

            OBJECTIVE: To see if, in the diagnosis of infant infection with human immunodeficiency virus (HIV) in Zambia, turnaround times could be reduced by using an automated notification system based on mobile phone texting. METHODS: In Zambia's Southern province, dried samples of blood from infants are sent to regional laboratories to be tested for HIV with polymerase chain reaction (PCR). Turnaround times for the postal notification of the results of such tests to 10 health facilities over 19 months were evaluated by retrospective data collection. These baseline data were used to determine how turnaround times were affected by customized software built to deliver the test results automatically and directly from the processing laboratory to the health facility of sample origin via short message service (SMS) texts. SMS system data were collected over a 7.5-month period for all infant dried blood samples used for HIV testing in the 10 study facilities. FINDINGS: Mean turnaround time for result notification to a health facility fell from 44.2 days pre-implementation to 26.7 days post-implementation. The reduction in turnaround time was statistically significant in nine (90%) facilities. The mean time to notification of a caregiver also fell significantly, from 66.8 days pre-implementation to 35.0 days post-implementation. Only 0.5% of the texted reports investigated differed from the corresponding paper reports. CONCLUSION: The texting of the results of infant HIV tests significantly shortened the times between sample collection and results notification to the relevant health facilities and caregivers.
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              The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests.

              We studied the effect of informing physicians of the charges for outpatient diagnostic tests on their ordering of such tests in an academic primary care medical practice. All tests were ordered at microcomputer workstations by 121 physicians. For half (the intervention group), the charge for the test being ordered and the total charge for tests for that patient on that day were displayed on the computer screen. The remaining physicians (control group) also used the computers but received no message about charges. The primary outcomes measured were the number of tests ordered and the charges for tests per patient visit. In the 14 weeks before the study, the number of tests ordered and the average charge for tests per patient visit were similar for the intervention and control groups. During the 26-week intervention period, the physicians in the intervention group ordered 14 percent fewer tests per patient visit than did those in the control group (P less than 0.005), and the charges for tests were 13 percent ($6.68 per visit) lower (P less than 0.05). The differences were greater for scheduled visits (17 percent fewer tests and 15 percent lower charges for the intervention group; P less than 0.01) than for unscheduled (urgent) visits (11 percent fewer tests and 10 percent lower charges; P greater than 0.3). During the 19 weeks after the intervention ended, the number of tests ordered by the physicians in the intervention group was only 7.7 percent lower than the number ordered by the physicians in the control group, and the charges for tests were only 3.5 percent lower (P greater than 0.3). Three measures of possible adverse outcomes--number of hospitalizations, emergency room visits, and outpatient visits during the study period and the following six months--were similar for the patients seen by the physicians in both groups. We conclude that displaying the charges for diagnostic tests significantly reduced the number and cost of tests ordered, especially for patients with scheduled visits. The effects of this intervention did not persist after it was discontinued.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                amiajnl
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1067-5027
                1527-974X
                July 2013
                9 November 2012
                9 November 2012
                : 20
                : 4
                : 743-748
                Affiliations
                [1 ]Department of Health Policy & Management, University of Pittsburgh , Pittsburgh, Pennsylvania, USA
                [2 ]Baobab Health Trust, Lilongwe, Malawi
                [3 ]Kamuzu Central Hospital , Lilongwe, Malawi
                [4 ]Center for Health Informatics for the Underserved, Department of Biomedical Informatics, University of Pittsburgh , Pittsburgh, Pennsylvania, USA
                Author notes
                [Correspondence to ] Julia Driessen,Crabtree Hall A614, Graduate School of Public Health, University of Pittsburgh,130 De Soto Street, Pittsburgh, PA 15261, USA; driessen@ 123456pitt.edu
                Article
                amiajnl-2012-001242
                10.1136/amiajnl-2012-001242
                3721156
                23144335
                4460ef9f-7b4f-46fc-9d97-ceeec05d509b
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/

                History
                : 2 August 2012
                : 16 October 2012
                Categories
                1506
                Focus on Human Factors and System Utilization
                Research and applications
                Custom metadata
                unlocked

                Bioinformatics & Computational biology
                electronic medical records,low-income population,malawi,economics,cost savings

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