IEEE - Personalized Electronic Health Record System for Monitoring Patients With Chronic Disease

Proceedings of the 2013 IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 26, 2013

978-1-4673-5663-3/13/$31.00 © 2013 IEEE

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This research involves the development of a personalized electronic health record system for monitoring patients with chronic conditions, that (a) allow for relevant data to be entered by the patient, (b) make relevant data available to SDWLHQW¶V FDUH SURYLGHU DW UHDO-WLPH DQG DW GRFWRU¶V YLVLW F will generate reports and graphs for the data and (d) will provide secure storage of the data. The app is a HIPAA-compliant medical research tool able to collect data from patients, store it conveniently for review, and send it to healthcare providers; it is not limited to chronic disease. Therefore, the app created has a potential as a much larger commercial system that would help companies, hospitals and clinics provide a more effective care for their patients by controlling costs while improving the quality of life and patient satisfaction. A. Literature Review Many studies performed within US health-care system have repeatedly shown that web-based technology can be used to assist patients in taking an active role in monitoring their health and self-manage their condition [3]. These webbased apps have the potential of increasing patient selfmanagement knowledge, skills in disease control and their confidence in managing their health; especially in the case of patients with chronic disease [3]. Furthermore, these apps actively monitor the progress of diseases and provide feedback to the care giving team that effectively reduces medical errors and costs [4]. Prior to designing a new product, research needs to be conducted to analyze the availability of comparative products. Many versatile, customized applications (apps) are currently used for monitoring patient care. Most apps are first used on a small scale for testing the design and usability, and then provide background for the design of a new, improved app. Researchers at Stanford used Tele-health technology to improve coordination of care [5]. Researchers examined two clinics in the Northeast US that had implemented the telehealth Buddy program using an experimental and control group, and studied the effect of the program on quality and spending for patients. When the tele-health Care Buddy Program was compared to a control group, the tele-health program had reduced spending of 7.7% to 13.3% or $312 $540 for each person per quarter. Mortality differences were also noted in treatment and control groups. Utilizing both tele-health technology and managed care has potential for reducing health care cost and improving care for persons who suffer from chronic diseases. This study suggests that carefully designed tele-health programs that are properly implemented can reduce the cost of health care and significantly reduce mortality rate. New Technology has also been used effectively in diabetes management [6]. Patient monitoring, particularly among patients on insulin is important in limiting disease progression, but can be labor intensive, costly and

cumbersome therefore there is a need for improved monitoring systems. In a recently conducted study published in Journal of Diabetes Science and Technology, Dr. Rao and his colleagues compared the effects of three iPhone diabetes data management applications; the Diamedic diabetes logbook, the blood sugar diabetes control and the wavesense diabetes manager [7]. Surveys sampled 23 individuals, who entered data manually based on self-reports on variables like ease of life, time to enter, request for help, data sharing, and application trustworthiness. Patients found the WDM application easiest to use, fastest and most trustworthy. The study provides important guidelines for monitoring symptoms of patients; however, the manual data entry was cited as a weakness. Another study conducted by Roy et al., [8] researchers in the area of ParkinsoQ¶V GLVHDVH XVHG D wearable sensor-based system to assist with assessment of motion disorders. This system was used to continuously monitor PD disorders to distinguish between normal tremors DQG WKRVH WKDW UHVXOW IURP WKH 3DUNLQVRQ¶V GLVHDVH )LQGLQJV from their study indicated that sensor technology and software processes could be used to improve the function of wearable sensor-based application used for monitoring PD disorders during unrestricted activities. In another study, researchers from the University of Denmark in Vienna, Austria, have been testing a new wireless medical sensor to study fatigue in MS patients [9]. The researchers wanted to prove that they could find more indicators and warning signs of fatigue using their wireless sensor than those perceived after the usual measurement tests for physiological parameters in MS patients. They planned to recruit three groups (10 fatigued MS patients, 10 nonfatigued MS patients, 10 age-matched healthy control) of ten persons each between ages 20-65. Groups were to be monitored continuously for 24 hours after undergoing memory and fitness test. A wireless data acquisition system converted signals and send them to a wireless router, then to a computer. Devices were used to measure ECG signs continuously, EMG modules for measuring activity of muscles, body skin temperatures, eye movement and motions signals. Data accumulated is to be related to physiological differences in the groups. However, no final results were reported as researchers are still conducting trials. The same researchers from the University of Denmark also recently tested a wireless body measurement system designed to study fatigue in MS patients [10]. Their aim was to set boundaries and to define the functions that are related to fatigue in patients suffering from MS. Their wireless system used to measure fatigue is called FAMOS. It is designed to identify fatigued MS patients from subjects who are healthy, and to provide feedback continuously on ECG, body-skin temperatures, EMG and feet motions. The study presented the design of the hardware and the procedures utilized. The researchers concluded that the FAMOS could determine healthy subjects from fatigued MS patients and provide data continuously on the capabilities and limitation

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made using the feedback gained from the results of the first iteration.

™ Implementation and possible expansion of the app ACKNOWLEDGMENT

IV. CONCLUSION Monitoring chronic illness presents a huge challenge for health care workers since the effects of chronic illness have been identified as the most important health care problem of the 21st century. Previously, focus has been placed on monitoring individual episodes of an ailment that includes phone calls to the patient, paper surveys of health status and on-site examinations instead of constant monitoring chronic conditions. This method of treating illness is outdated, as it does not present a clear picture of the symptoms of a patient suffering from chronic illness. Rather than an episodic approach, chronic care needs a preventative approach that can actively involve the patient, monitor multiple conditions in a long-term or continuous manner, and provide real time information and timely feedback DERXW D SDWLHQW¶V KHDOWK condition. Managed home care visits are costly, time consuming and involves only one patient at a time. With the explosion of chronic conditions more health care monitoring needs to be done in the home and the needed information has to be available to the right people at the precise time. The Personalized Electronic Health Record System for Monitoring Patients with Chronic Disease (PEHRS-MPCD) is innovative as it is designed to continually monitor the patient as he can provide feedback whenever he wishes to. Furthermore it is interactive, flexible and provides a comprehensive solution to the meet the needs of persons with multiple chronic diseases. The goal of the application is to improve the efficiency of the diagnosis and the monitoring of information and ensure quality of care. Reports consisting of graphs and charts are generated and available to be accessed by the patient, a physician on the care team, a nurse, case manager or anyone authorized who my need the information. Once the relevant data is available WR WKH SDWLHQW¶V FDUH WHDP useful feedback can be given. This feedback may include the effects of lifestyle changes, drug/medication and diet changes on the progression of the disease and its symptoms. Our goal is to improve and enhance the lives of persons who suffer from chronic disease and assist them in meeting their health goals. The personalized electronic health record system will improve the efficiency of the diagnosis and the information and ensure quality of care provided thus improving the patient¶s quality of life. A. Future Work Future work on the application would focus on the following 3 main areas: ™ Fully developing and modifying the functionalities of the app ™ Performing usability and robustness testing for both bugs and efficiency of algorithm of the app

The author would like to thank Dr. Stephen D. Patek, Systems & Information Engineering Associate Professor at University of Virginia and Mr. Douglas E. Barton, Vice President and Chief Solutions Architect at SAIC. The author takes full responsibility for the ideas, concepts, and opinions presented here, which do not necessarily reflect those held by Dr. Patek and Mr. Douglas. REFERENCES [1] Anderson, G. (2010). Chronic Care ± Making the case for ongoing care. Princeton, NJ. Robert Wood Johnson Foundation. [2] Intel Corporations (2008). Addressing the challenges of chronic illness with personal health system technology. White Paper. Intel Digital Health Group. ZZZ JWVL FRP «3HUVRQDO+HDOWK6\VWHP7HFKQRORJ\ SGI [3] Solomon, M., Wagner, S., & Goes, J. (2012). Effects of a web-based intervention for adults with chronic conditions on patient activation: Online randomized controlled trial. Journal of Medical Internet Research, 14(1), e.32. [4] Ahmed, S., Bartlett, S., Emst, P., Pare, G., Kanter, M., Perreault, R., Grad, F., Taylor, L. 7 Tamblyn, R. (2011). Effect of a web-based chronic disease management system on asthma control and healthrelated quality of life: Study protocol for a randomized controlled trial. School of Physical and Occupational Therapy, Mc. Gill University. DOI: 10.1186/1745-6215-12-260. [5] Baker, L., Johnson, S., Macaulay, D., & Brnbaum, H. (2011). Integrated tele-health and care management programs for Medicare beneficiaries with chronic disease linked to savings. Health Affairs, Vol. 30(9), 1690-1697. DOI: 10.1377/hlthaff.2011.0216 [6] Ciemins, E., Coon, P., & Sorli, C. (2010). An analysis of data management tools for diabetes self-management; Can smart phone technology keep up? Journal of Diabetes Science and Technology, Vol. 4(4), 958-960. [7] Rao, A., Hou, P., Golnik, T., Flaherly, J., & Vu, S. (2010). Evolution of data management tools for managing self-monitoring of blood glucose results: A survey of iPhone applications. Journal of Diabetes Science & Technology. 4(4), 949-57. [8] Roy, S., Cole, B., Gilmore, L., DeLuca, C., Thomas, C., Saint-Hilaire, M. Nawab, S. (2013). High-resolution tracking of motor disorders in 3DUNLQVRQ¶V GLVHDVH GXULQJ XQFRQVWUDLQHG DFWLYLW\ 0RYHPHQW Disorder, Mar. 20. Doi:10.1002/mds.25391. [Epubaheadofprint] [9] Yu, F. & Bilberg, A. (2010). A study of fatigue in multiple sclerosis using a new wireless medical sensor measurements system. Annals of DAAAM for 2010 and Proceedings of 21 st International DAAAM Symposium. 21(1), 1167-1168. [10] Yu, F., Bilberg, A., Stenager, E., Rabotti, C., Zhang, B., & Mischi, M. (2012). A wireless body measurement system to study fatigue in multiple sclerosis. Physiological Measurement, 33(12), 2033-2048. [11] Navaneethan, S., Jolly, S., Sharp, J., Jain, A., Schold, J., Schreiber, M., & Nally, J. (2013). Electronic Health Records: a new tool to combat chronic kidney disease? Clinical Nephrology, 79(3), 175-183. Doi:10.5414/cn107757.PMID:23320972(PubMed-in process). [12] Von Bargen, T., Gletzett, M., Britten, M., Song, B., Wolf, K., Kohlmann, M., & Haux, R. (2013). Design and Implementation of the Standards-Based Personal Intelligent Self-Management System. (PICS). Stud Health Technicol Information, 186:135-139. PMID:23542984 (PubMed-in progress). [13] Lee, l., Chou, Y., Huang, E., & Liou, D. (2013). Designing of a Personal Health Record and health Knowledge Sharing System Using IHE-XDS and Owl. Journal of Medical Systems, 37(2), 9921-4. Doi: 10.1007/s10916-012-9921-4 [14] Symptom Journal (2013). Track your symptoms. Find Answers. Live Well. http://www.symptomjournal.com. San Francisco Bay Area Industry Health Wellness & Fitness

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