Health Information System for Medical Laboratory Science PDF

# Health Information System for Medical Laboratory Science ## Introduction Nowadays, most institutions have existing Clinical Data Repositories (CDR), in electronic or written format, to represent an aggregated database of clinical information. It usually houses a multitude of laboratory results,...

# Health Information System for Medical Laboratory Science ## Introduction Nowadays, most institutions have existing Clinical Data Repositories (CDR), in electronic or written format, to represent an aggregated database of clinical information. It usually houses a multitude of laboratory results, diagnostic reports, and various clinical documentation. The data is readily searchable and exportable, often because the information is gathered from standard clinical care procedures (Robertson and Williams, 2016). The CDR integrates physician-entered data with data from different existing information systems including laboratory, radiology, admission, and pharmacy, among others. It is a location where both clinical data and other data of interest, such as external data sources and financial data, are assimilated (Carter, 2001). A clinical data repository can successfully depict the same sample across different points in time, from varying sources both within and outside the health institution. Common kinds of available information in the CDR are listed below: - Patient Demographics - Patient's Primary Care Provider - Medication List - Allergies - Clinical - direct observation - af Portal - norta - Hospital Inpatient Visits - farct & stats - collected - Emergency Department Encounters - Outpatient Practice Visits - Immunizations - Diagnoses - Procedures - Lab Results - Social History - Vitals Maintaining a CDR poses a lot of advantages, particularly in making more informed patient care decisions for healthcare providers. The longitudinal view of a patient's medical record can assist in improving patient experience, and having information about prior test results and procedures helps avoid redundant treatment. ## Data Repositories Wade (2014) emphasizes that the longitudinal nature of the CDR requires a way of linking various observations of the same identified subject. Most repositories usually contain personally-identified data, however, due to privacy issues, they only release de-identified data, which can lead to the omission of some data in a dataset. The lack of identifiers could also prevent the linking of data for some patients. Presented below (Table 13.1) are the different types of clinical data repositories that Wade has classified according to factors described above. This material focuses on the discussion of Electronic Health Records, in the context of Healthcare Information Systems. While clinical data repositories are beneficial in consolidating patient information, a disadvantage is that most CDRs are only integrated with clinical data. Lab results, diagnoses, and demographics might be available for use in one platform, but overall patient satisfaction, the amount of time a patient had to wait before being treated, and other information not related directly to his care might be unavailable. ### Multiple Views for Patient Medical Record Patient information is typically scattered across multiple subsystems. A clinical data repository standardizes data from disparate sources into a cohesive format. It comprises numerous tables, each offering a partial view of patient information (Gensinger, 2014). The structure of clinical data repositories allows data to be extracted along dimensions such as time (by year, month, week, or day), location, or diagnosis among many others. This data can often be accessed in smaller units within the same dimension. For instance, a user can view the number of patients with having a certain type of diagnosis, lab result, or prescription within a year, then a month in that year, and further into a day in that month. One can also access how many times a particular procedure has been performed at all locations within a health system, and then see the aggregate amount per region, and then by facility. Clinical data repositories help organizations transform large amounts of information from distinct transactional files into a unitary decision-support database (Wager, Lee, and Glaser, 2013). Ball and Douglas (2013) elaborate that: - A well-deployed clinical repository has multiple advantages. One example is the CDR function to provide longitudinal views of patient information. Data repositories are often organized primarily around patients and secondly around visits or encounters-a method that easily accommodates views that span multiple visits. - This allows clinicians to trend and chart results independent of the visits and test panel organization. For example, a clinician could study the trend of a patient's blood sodium levels over the past six months independent of other factors. - CDRs also provide access to information where it is needed. Since they receive information from a multitude of feeder systems, well-deployed CDRs can create a "one-stop shopping" environment. This is done by allowing the clinical staff to access a variety of patient-focused information through a consistent and easy-to-use graphical user interface (GUI). The GUI access can be deployed through handheld devices, bedside computing devices, computers in physician offices, or computing devices deployed at nursing stations. In any case, this wide variety of information access moves far closer to deployment of information at the point of care. - Finally, CDRs offer a cross-continuum view of information, since they allow information to be gathered and viewed from sources other than an acute setting. This type of ambulatory-focused information combines with the acute information to give clinicians a new level of insight into the wellness of their patients. ### Graphical Representation of Lab Results and Vitals Data collected through an electronic health record system may be retrieved at the request of an authorized user, whether a physician, medical technologist, nurse, or radiologist. The EHR may present patient care information as text, tables, graphs, sounds, images, full-motion video, or signals on an electronic screen, phone, pager, or paper (Bronzino and Peterson, 2014). Unfortunately, analyzing trends and patterns from large data sets can be a challenging process. This is where data visualization , the art of representing data in a pictorial or graphical format, becomes useful. Data visualization helps in simplifying a wide array of information, and it allows decision-makers to derive analytical results from information presented visually. Correlations, patterns, and trends, which might be undetected from text-based clinical data, can be revealed and recognized with more ease because of data visualization. ## Key Points to Remember - The CDR integrates physician-entered data with data from different existing information systems including laboratory, radiology, admission, and pharmacy among others. - A clinical data repository as a structured and systematically gathered "storehouse" of patient-specific data, which is usually mirrored from a clinical application, or supplemented with data from other clinical systems. - Repository types include: Study, Electronic Health Record, Registry, Warehouse, Collection, and Federation. - CDRs offer a cross-continuum view of information, since they allow information to be gathered and viewed from sources other than an acute setting. - Data visualization helps in simplifying a wide array of information, and it allows decision-makers to derive analytical results from information presented visually. ## References - Ball, M., & Douglas, J. (2013). “Clinical Data Repositories: A Powerful Tool for Healthcare's Brighter Future.” Journal of AHIMA, 84(1), 54–59. - Bergeron, B. (2013). “Clinical Data Repositories: Data Mining in the Trenches.” Healthcare Informatics, 30(11), 14–17. - Bronzino, J., & Peterson, L. (2014). Biomedical Engineering Handbook (5th ed.). - Carter, J. (2001). “From Data to Knowledge.” The Journal of The American Medical Informatics Association, 8(5), 418–426. - Gensinger, R. (2014). “Clinical Data Repositories: An Overview for Data Management.” In, S. K. Gupta (Ed.), Data Warehousing: Theory and Practice (pp. 484–509). - Mehta, A. (2002). “Data Analysis: Importance, Tools, Types and Benefits.” In, W. A. G. Van Der Aalst, J. Desel, & A. Oberweis (Eds.), Business process management: Models, techniques, and empirical studies (pp. 3–28). - Rains, C., & McCuistion, K. (2018). “Data Warehousing for Healthcare: A Strategic Approach to Retrieval, Analysis, and Decision Making.” In, K. D. E. J. Allen, & P. A. H. Jones (Eds.), Informatics for Healthcare (pp. 491–508). - Robertson, S., & Williams, D. (2016). “Clinical Data Repositories: A Valuable Resource for Research and Quality Improvement.” Journal of AHIMA, 87(9), 14 –17. - Wade, T. (2014). “Clinical Data Repositories: A Primer.” In, A. M. McClean, & M. Y. L. K. M. Wong (Eds.), Biomedical data informatics (pp. 11 –20). - Wager, J., Lee, K., & Glaser, J. (2013). “Data Warehousing and Data Marts.” Health Services Research, 48(1), 345–362.