Introduction
Informatics in healthcare helps to work with data, which are results of clinical trials, medical examinations, treatment results, and other outcomes. Computer systems organize, compute, compare, and show them to clients and patients as they describe their health state. Those outcome data are the basis for medical decisions, which directly influence patients’ lives. For example, Agency for Healthcare Research and Quality has used natural language processing technologies to examine patients with clinical depression and decide their treatment. In that way, proper processing of medical data is a deal of great importance. As information technologies help and facilitate decision-making, they should be adopted and learned extensively in medical and nursing organizations.
Informatics and Data Science in Health
Information technologies are used widely in modern clinical practice, and there are many reasons for that. First, they free up a lot of time for nurses and patients (MOS, 2020). They enable medical specialists to proceed with data for minutes instead of hours and days and rescue from the routine paperwork. Patients can use internet services to join the digital queue instead of waiting for admission hours.
Second, they help make hard decisions, which become data-driven and, thus, much more actual and beneficial for patients (Gadd et al., 2020). Information technologies should be patient-centered at first, as the healthcare industry is essentially a service that works to improve people’s lives (Strudwick et al., 2019). Internet portals and other services for patients, for example, enable them to see the results of their treatments, their outcome data, and their interpretations and communicate with doctors more efficiently (Barfield, 2020). In that way, information technologies can be used in all healthcare fields and are very beneficial for them.
However, there are gaps in current information technology usage: many clinicians, for example, underestimate its importance or overestimate their own knowledge of it. According to the study of Strudwick et al. (2019), modern nurse leaders cannot fully appreciate all prospects of information technologies. They do not see its potential in solving medical problems and usually do not have enough knowledge to use IT throughout their medical practice.
According to the research of Kleib and Nagle (2018), a similar situation is in Canada: while most nurses understand the importance of IT, they are not used fully. While electronic medical records and documentation are already widespread, there is much less usage of monitoring devices, systems for decision-making, and patient Internet portals. Electronic records and storage systems facilitate the data proceeding, making them much quicker, but their full potential cannot be realized without decision-making systems, which help make concise decisions based on those data (Fridsma, 2019). Those gaps should be resolved to enhance the development of the healthcare sector and increase its productivity.
Why It is Necessary
The usage of information technologies helps solve many healthcare problems, which become more accessible and understandable for clients. Those technologies make clinical practice more convenient and transparent for both doctors and patients. It increases transparency: the patient’s ability to see medical data in an understandable form and, thus, to understand how doctors treat them and what to expect (MOS, 2020). It is essential for them during clinical trials, when they are usually uncertain and stressed about the outcome. Mentioned electronic queue systems and patient care services increase their well-being, which is an integral part of success for medical organizations. Data-driven decisions are much more beneficial and accurate and thus should be implemented in clinics (Gadd et al., 2020). Therefore, IT is necessary for healthcare and should be used there whenever possible.
Examples of the Usage of Informatics in Healthcare
Patients are happier and more satisfied when facing good medical decisions and a clear understanding of their problems. The case with AHRQ and its project with natural language processing of depression patients’ speeches is a prominent real-life example of how IT can help solve mental health issues (Leavy et al., 2020). They use the results of their speech processing to evaluate their states and to prescribe medications. It is a novel example of depression treatment, and it has great prospects.
The other simple example is in the spreadsheet: information technologies, together with measuring devices, help monitor outcome data in real time and make computations with them. In this example, there are two parameters evaluated: blood pressure rates and blood sugar levels. Average blood pressure during the day is an important parameter to evaluate the patient’s health: as one can see, several of the ten patients have lower or higher pressure. The dispersion of blood sugar levels during the day shows how stable is the sugar level, which is extremely important for patients with diabetes. If the variations are huge, it is a sign that something is wrong and medical actions are necessary.
Conclusion
One can see that familiarity with IT is essential for modern nurses and doctors, while its levels are still insufficient. They tend to overestimate their knowledge of this topic, despite often being unable to formulate clearly why they use informatics. Meanwhile, its usage helps patients be more satisfied due to increased transparency and better medical decisions based on clinical data. High technologies, such as natural language processing, as in the AHRQ project, are sometimes used unexpectedly: to process depressed people’s speeches to make conclusions about their states. In that way, implementing informatics into healthcare is an integral part of its development.
References
Barfield, N. (2020). Importance of data capture, analysis, and reporting. mdgroup. Web.
Fridsma, D. B. (2019). Strengthening our profession by defining clinical and health informatics practice. Journal of the American Medical Informatics Association, 26(7), 585. Web.
Gadd, C. S., Steen, E. B., Caro, C. M., Greenberg, S., Williamson, J. J., & Fridsma, D. B. (2020). Domains, tasks, and knowledge for health informatics practice: Results of a practice analysis. Journal of the American Medical Informatics Association, 27(6), 845–852. Web.
Kleib, M., & Nagle, L. (2018). Factors associated with Canadian nurses’ informatics competency. CIN: Computers, Informatics, Nursing, 36(8), 406–415. Web.
Leavy, M. B., Cooke, D., Hajjar, S., Bickelman, E., Egan, B., Clarke, D., Gibson, D., Casanova, B., & Gliklich, R. (2020). Registry configuration: Outcome measure harmonization and data infrastructure for Patient-Centered outcomes research in depression. Agency for Healthcare Research and Quality. Web.
MOS. (2020). Why automated data capture solutions are essential for healthcare providers. Managed Outsource Solutions. Web.
Strudwick, G., Nagle, L., Kassam, I., Pahwa, M., & Sequeira, L. (2019). Informatics competencies for nurse leaders. JONA: The Journal of Nursing Administration, 49(6), 323–330. Web.