Wireless blood pressure device integration with electronic medical records: Case Study of University of Nairobi Health Services

Abstract

Medical devices offer invaluable information to clinicians on a patient’s illness, making them a crucial component in the provision of safe, effective and efficient patient care. However, the entry of the most output data from various associated medical gadgets into Electronic Medical Records (EMR) remains manual. These outputs are often presented and kept in the form of paper reports. It means, therefore, that when clinicians and patients have a need to access the information at any given time, it is only possible by looking at the paper files which is a process that is tedious, error-prone, inefficient and not accessible remotely. In this study, we describe a demonstration in which available Electronic Medical Records system (EMR) was successfully integrated with a wireless Blood Pressure Monitor (BPM). This was implemented by adopting the use of RESTful Application Programming Interface (API) technologies and commonly established standards designed for medical devices interoperability. We have implemented a solution named BP-Easy to capture data from the device and synchronize seamlessly we used with a local database. Before deploying the prototype, we conducted pilot tests at the University nursing station to get feedback on the time spent using the conventional blood pressure data capture methods and the newly integrated application. Clinical data from the device was exchanged adhering to the HL7/XML standard communication protocol. Data stored can be retrieved and shared between clinicians and the patients to aid in making clinical decisions. We used quantitative data collecting methods and the data were analyzed using the SPSS software and Microsoft Excel 2010. According to the data observed for the period blood pressure data was gathered, there was a measurement differential in time for both before pre and after the device integration was found. Suffice to say, the duration the blood pressure cuff was on the patient was an average of 58 seconds before the integration of the device, and 38 seconds after. Additionally, there was an observable substantial reduction in the average time that the medical assistant spent at the intake section from 326 seconds before the medical device was integrated and an average of 204 seconds after the integration. The Findings indicate that a positive outcome was availed on the time taken for the Blood Pressure readings, time spent by the patient at the nursing station, as well as the data accuracy fed in the EHR system.