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How are mathematical calculations measured? What communication tools are utilized to express research findings? Statistics depict the quantitative perspective of research and specific terminology is used to express understanding and interpretation of results. The more common terms associated with statistics include: mean, median, mode, standard deviation, and variance. Also investigated with regards to research are the concepts of reliability and validity and correlation and causation. Also recall the use of a sample and population from statistics course(s) and the necessity of sample derivation for the purpose of generalizability to a population of which the sample is representative. A basic understanding and simple application of these keys terms facilitate their implications in health care information system processing with the use of statistics for measurement and HIM systems for collection and processing.
Some vital statistics health care is concerned with include morbidity and mortality. Morbidity is the expression of the state of a disease and mortality is the number of deaths. Morbidity can be regarded as the extent of the state of health a patient possesses. By extension, often the word comorbidities is used to depict the presence of more than one disease state such as hypertension and diabetes or anxiety and fibromyalgia. Documentation will include the use of comorbid states for each specific patient. While it is important to distinguish between these two terms, it is equally important to know when each is used in documentation, application, and interpretation. Patient information along with disease information are combined with the use of electronic health records and electronic medical record providing a comprehensive view of a patient whose information can be used for treatment plans that benefit more than one single patient. This information is also used to create models for future research studies as well as clinical care management.
Vital statistics specifically include births, deaths, marriages, and divorces while demographic statistics consist of age, ethnicity, health status, and income. These statistics provide a meaningful interpretation of the information inherent as the individual statistics are viewed together. Their use, along with other advanced statistics, assist in answering pertinent clinical and health care business questions (McWay, 2014). Reliable collection methods lead to validity of the overall obtained results.
Statistical analyses are performed to determine optimal patient outcomes and reduce overall operating costs of outpatient and inpatient centers providing care. Salemia, Comins, Chandler, Mogos, and Salihu (2013) used descriptive statistics to describe the variation in cost-to-charge ratios (CCRs) across hospital departments to determine cost estimation. More information and future studies can help advance the creation of uniform cost measures. In addition, the understanding and economic analyses are critical for answering complex cost issues. What does this mean for health care information management? There is a genuine need for deciphering, investigating, and understanding already accessible health care information. Personnel trained in this discipline are central to unlocking the meaning of information already acquired and aiding in progression of the field.
Another facet of statistical analysis concerns selecting the correct test for your research purposes. Research focuses on reliability, measuring the consistency of the instrument, and on validity, that is the accurate measuring of what the research intends to measure (McWay, 2014). Research often starts with the research question that poses the purpose of the study. Referral back to this question throughout the development of the research is imperative to maintain the focus and guide the evolution of the results.
Research pertaining to health information management encompasses a vast body of knowledge. Research is necessary to advance practice especially in the health care field. When embarking on research, universities and academic medical centers pay close attention to research conducted by its students. With the installation of institutional review boards (IRBs), these bodies oversee research projects with the goal of protecting the rights of the subjects or participants. Two classic examples of not protecting the rights of participants include the medical experimentation performed on humans when concentration camp victims and participation in the Tuskegee Experiment. These horrific examples underscore the absolute importance of these boards. Typically the members of an IRB are comprised of people with different backgrounds with experience dealing with vulnerable populations to protect potential subjects from any harm (McWay, 2014). Meeting with an IRB prior to inception of any research project is required at many, if not all, institutions.
Researchers decide between various types of studies including qualitative and quantitative studies using descriptive or numerical analyses and choose between testing including t-tests (one- or two-tailed), z-tests, analysis of variance (ANOVA) testing etc. Other terminology to become reacquainted with include null hypotheses, p-values, accepting or rejecting the null hypotheses, retrospective studies, and cohort studies. Recall that the null hypothesis in a statistical test is the statement that says no effect or relationship exists between what one is trying to prove and the outcome. Therefore, acceptance of the null hypothesis means no relationship has been proven to exist and alternately, rejection of the null hypothesis means that a relationship may exist and the likelihood that this exists relates to the P value.
P values evaluate how well the data from the sample chosen supports the null hypothesis (as close to the actual population that one is trying to generalize to can be). When researchers begin, they are trying to prove that a relationship does in fact exist and that the null hypothesis is false so that extrapolation of information can be used to further impact targeted populations. Common examples of P values are p <.01 or p <.05 interpreted as the probability of the null hypothesis being true meaning that the probability of the null hypothesis being true is < 1 % (p < .01) or < 5% (p < .05) respectively. This means the likelihood of the null hypothesis not being true is 99% and 95% respectively, which is most commonly the goal proving a relationship exists with a high probability, if the study is reliable and valid and bias does not exist. Researchers disclose potential biases and cite limitations of studies and point out ways to improve future studies.
Health information resource management and other departments will be involved in identification of problems that cost an institution a high percentage of money. Along with risk management departments, HIM may be charged with data collection and analyzation used to change the direction of the outflow of cash spent on liability issues and payouts associated with these issues, such as falls in an acute care setting (i.e. a hospital). These useful and sometimes pivotal interpretations of the data guide resource allocation and utilization to benefit institutions both from a public relations perspective and from a monetary perspective.
HIM systems help recognize when to collect data related to specific conditions, what types of data should be collected, and how this data can help control current costs and potentially reduce future costs for both hospitals and other care centers and individual patients. For example, Calciolari, Torbica, and Tarricone (2013) discuss the health care costs associated with intracranial aneurysm management. By accurate estimation and inclusion of the variability of costs, management is in a better position to make decisions based on accurate data. Costs in this study were determined using regression analyses and calculated the large expenditures needed for patients requiring medical care related to their diagnoses of aneurysms. While this study evaluated the direct costs associated for patients treated with aneurysms, it was further determined that by reducing patient disability, future related health care costs could also be reduced related to necessary post-acute care. In the current climate of rising health care costs, it is mandatory to increase reasonable preventative cost expenditures and decrease current acute and chronic care expenditures, which it is expected, theoretically, to decrease future expenditures by ensuring a healthier population of peoples.
Another area of impact for the HIM professional manager includes knowledge acquisition in the field of epidemiology. This science “refers to the determination of the occurrence and distribution of human health problems, the establishment of their root causes, and the factors that influence their distribution” (McWay, 2014). Why is this important from an HIM perspective? The overarching assumption in epidemiology is that diseases and their patterns are predictable, meaning that the understanding of this premise and presenting characteristic patterns can be used to understand and project future occurrences.
The implications for epidemiology and HIM extend into virtually every area within the health care arena. Monitoring of a plethora of factors can be input into information systems that can interpret relative patterns, connections, and both quality and cost-cutting measures. In Burgard and Hawkins (2014), the connections between economic downturns and reduced seeking of health care were explored. Results concluded that groups with more financial advantages maintained an advantage in terms of health care access during a recessionary period. The importance of this aspect is critical in health care. Epidemiology searches for the causes of diseases and follows disease progression and links this with research (McWay, 2014). The triangularity of the agent (disease or pathogen), host, and environment ultimately determine the manifestation and extent of the disease process. By systematically documenting the data of these three elements, a well-designed system can enhance health interventions and subsequent plans of care.
Lecture and Research Update Bibliography
Burgard, S. A., & Hawkins, J. M. (2014). Race/Ethnicity, Educational Attainment, and Foregone Health Care in the United States in the 2007-2009 Recession. American Journal of Public Health, 104(2), e134-40.
Calciolari, S., Torbica, A., & Tarricone, R. (2013). Explaining the Health Costs Associated with Managing Intracranial Aneurysms in Italy. Applied Health Economics and Health Policy, 11(4), 427-35.
McWay, D. C. (2014). Today’s Health Information Management: An Integrated Approach (2nd ed.). Clifton Park, NY: Delmar Cengage Learning.
Salemi, J. L., Comins, M. M., Chandler, K., Mogos, M. F., & Salihu, H. M. (2013). A Practical Approach for Calculating Reliable Cost Estimates from Observational Data: Application to Cost Analyses in Maternal and Child Health. Applied Health Economics and Health Policy, 11(4), 343-57.
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