Wavelet Transform Compression of Clinical Data to Support Rapid Storage and Retrieval

As medical devices are increasingly required to provide for information from the point of care to enterprise electronic health record systems, more patient data collected at the point of care will become available for remote viewing and analysis. In this paper a discrete wavelet transform method is presented for automatically filtering and analyzing data obtained from high acuity bedside physiologic monitors using discrete wavelet transforms, the purpose being to reduce the total amount of data transmitted into the electronic health record, and to facilitate analysis of time-based trends in physiologic data. Introduction As information technology has been brought into the healthcare enterprise, much of the paper-based record is being supplanted by an electronic record, in which clinicians either record information manually or, in addition, automatically from clinical systems. The electronic medical record is maintained by the health care enterprise and follows the patient throughout all phases of diagnosis and treatment. Furthermore, this medical record is accessible to all authorized clinical personnel. An obvious benefit of this approach is that, unlike the paper record, the electronic medical record can be accessed from many different locations without physically retrieving the patient’s hard copy information from particular departments. Loss of information is nullified, and use of the electronic medical record establishes a standard approach for recording of patient information, so that each department must conform to specific standards in terms of the types and quality of information recorded on each patient. Also, with Web-browser-based medical record viewing, convenience in terms of viewing, together with the reduced delays associated with retrieving the paper recording ensure that clinicians can readily obtain patient information when required. In addition, two-way communication between the enterprise information system and clinical systems enable the error-free retrieval of patient demographic and administrative information (such as medical record number and insurance information) without adding further delay or introducing errors into the patient’s record within the departmental system. One key difference between the legacy paper record and the electronic medical record is that the paper record remained an intimate device by which the attending nurse monitored and recorded status on the patient: it remained with the patient and the nurse until the patient left the unit. With the introduction of the electronic medical record, data transmitted to that record and viewable by authorized individuals outside of the unit can lack the context of the actual situation in interpreting patient flow sheet results. As society and healthcare move toward a completely automated and electronic medical record environment, it must be mindful of the fact that the introduction of new technologies must never impede quality healthcare [1]. In this paper the author describes a process for augmenting the basic transmission of information from the clinical environment to the medical record by capturing more detailed information that may not be captured during the course of standard flow sheet recording. Patient telemetry is normally recorded within the flow or assessment sheet on regular intervals. Typical intervals range from 15 minutes to an hour, depending on the particular acuity of the patient. However, bedside monitors typically can record very detailed information in fractions of a second. Most of this information is discarded, and much of it can be of no clinical value at these short intervals. However, a trade-off exists in terms of the size of the interval and the capturing of relatively important data from these bedside monitors: make the recording interval too large, and events of relatively short duration but high importance (such as heart rate spikes, or respiratory rate increases) will be missed and never recorded within the electronic medical record. On the other hand, make these cording intervals too small, and the health care enterprise, including the hospital computing network and the size of the medical record, will become cumbersome and filled with much useless information, possibly even rendering the system un-useable. One approach to solving this problem is to provide the capability to record detailed information when necessary, but omit when not. Copyright © 2015 John R. Zaleski, Ph.D., CPHIMS Page 2 of 20 Discrete Wavelet Transform Calculation 5 -2 3 -1 s0=3/2 s1=1 d0=7/2 d1=2