According to (Cange, 2016), 1 out of 10 US hospital patients contract an infection during their hospital stay, resulting in thousands of unnecessary deaths and billions of dollars in unnecessary costs. Centers for Medicare and Medicaid Services (CMS) uses two domains to measure the incidence of conditions for this program—the Agency for Healthcare Research and Quality’s Patient Safety Indicators (PSI-90) (Domain 1) and the National Healthcare Safety Network (NHSN) hospital-associated infection measures (Domain 2) (Sheetz & Ryan, 2020). Domain 2 constitutes 85% of the total score used to levy financial penalties against hospitals (Sheetz & Ryan, 2020). The PSI-90 composite is derived from Medicare claims; the NHSN measures are derived from an electronic registry managed by the Centers for Disease Control and Prevention (Sheetz & Ryan, 2020).
Almost all hospital acquired infections (HAI) are preventable, which is why CMS no longer reimburse hospitals for the costs to treat HAIs (Cange, 2016). These infections result in 100,000 deaths per year, making HAIs one of the top five causes of death in the United States. HAIs also cost US hospitals between $28 billion and $45 billion per year to treat (Cange, 2016). In 2016, not all hospitals were required to report HAIs, and some were excluded due to not performing the specific procedure that led to an HAI (Cange, 2016). These omissions limit the quantity and the completeness of the data, requiring researchers to make assumptions about HAI incidence which in turn continues to negatively impact the quality of patient outcomes (Cange, 2016).
Under the CMS reimbursement policy, eleven preventable adverse outcomes were identified: foreign objects retained after surgery, air embolism, blood incompatibility, stages III and IV pressure ulcers, falls and trauma, manifestations of poor glycemic control, catheter-associated urinary tract infections, vascular catheter-associated infections, surgical site infections, deep vein thrombosis, and iatrogenic pneumothorax with venous catheterization (Bae, 2016). Of these 11 patient outcomes, four (severe pressure ulcers, falls and trauma, catheter-associated urinary tract infections, and vascular catheter-associated infections) are considered nursing-sensitive quality outcomes that can be decreased with greater and better nursing care (Bae, 2016). Health care facilities are expected to make appropriate changes in patient care to improve these outcomes (Bae, 2016). Quality improvement initiatives can focus, for example, on patient safety or on care processes, and working conditions can be improved (Bae, 2016).
Bae, S. (2016). The center for medicare & medicaid services reimbursement policy and nursing-sensitive adverse patient outcomes. Nursing Economics, 34(4), 161-171, 181.
Cange, J. R. (2016). Preventing hospital-acquired infections starts with data collection. Journal of AHIMA, 87(1), 44-46.
Sheetz, K. H., & Ryan, A. (2020). Accuracy of quality measurement for the hospital acquired conditions reduction program. BMJ Quality & Safety, 29(7), 605-607.
Measures of Quality
Quality health care is a high priority for the President, the Department of Health and Human Services (HHS), and the Centers for Medicare & Medicaid Services (CMS) (Centers for Medicare & Medicaid Services, 2021). CMS implements quality initiatives to assure quality health care for Medicare Beneficiaries through accountability and public disclosure (CMS, 2021). CMS uses quality measures in its various quality initiatives that include quality improvement, pay for reporting, and public reporting (CMS, 2021). Quality measures are tools that help us measure or quantify health care processes, outcomes, patient perceptions, and organizational structure and/or systems that are associated with the ability to provide high-quality health care and/or that relate to one or more quality goals for health care (CMS, 2021). These goals include: effective, safe, efficient, patient-centered, equitable, and timely care and CMS uses quality measures in its quality improvement, public reporting, and pay-for-reporting programs for specific health care providers (CMS, 2021). Data on quality measures are collected or reported in a variety of ways, such as claims, assessment instruments, chart abstraction, registries (CMS, 2021).
A basic principle of quality measurement is: if you cannot measure it, you cannot improve it (Johnson & Magnan, 2019). Therefore, fall rates and fall prevention practices must be counted and tracked as one component of a quality improvement program. By tracking performance, then care improvement will show, staying the same, or worsening in response to efforts to change practice and continued monitoring will help understand where the starting point is from and whether improvement gains are being sustained (Johnson & Magnan, 2019). Fall and fall-related injury rates are the most direct measure of success in making patients safer related to falls. If rates are improving, then you are likely doing a good job in preventing falls and fall-related injuries (Johnson & Magnan, 2019). Conversely, if the fall and fall-related injury rates are getting worse, then there might be areas in which care can be improved znd you can use these data to make a case for initiating a quality improvement effort and monitoring progress to sustain your improvements (Johnson & Magnan, 2019).
The Hospital-Acquired Condition (HAC) Reduction Program is a Medicare pay-for-performance program that supports the CMS effort to link Medicare payments to health care quality in the inpatient hospital setting to encourage eligible hospitals to reduce HACs (Johnson & Magnan, 2019). Section 1886(p) of the Social Security Act established the statutory requirements for the HAC Reduction Program. The HAC Reduction Program is a Medicare value-based purchasing program that reduces payments to hospital based on how they perform on measures of hospital-acquired conditions (CMS, 2021). The program supports CMS’s long-standing effort to link Medicare payments to health care quality in the inpatient hospital setting (CMS, 2021). This measure affects my organization from the falls with hip fracture issue and also readmissions for fall related injury readmissions (Singh et al., 2019).
How Do These Standards and Regulations Influence or Support Ethical Principles, and Influence Patient Care and Nursing Practice?
CMS’s new comprehensive initiative “Meaningful Measures” was launched in 2017 and identifies high priority areas for quality measurement and improvement. Its purpose is to improve outcomes for patients, their families and providers while also reducing burden on clinicians and providers (Hickey, & Giardino, 2019). Although the original Meaningful Measures initiative accomplished its initial goals, its scope and purpose have evolved to keep pace with a rapidly changing health care environment (Hickey, & Giardino, 2019). With Meaningful Measures 2.0, CMS will not only continue to reduce the number of measures in its programs but will further shape the entire ecosystem of quality measures that drive value-based care (Hickey, & Giardino, 2019). Meaningful Measures 2.0 will promote innovation and modernization of all aspects of quality, addressing a wide variety of settings, stakeholders, and measurement requirements (Hickey, & Giardino, 2019). Commit to a patient centered approach in quality measure and value-based incentives programs to ensure that quality and safety measures address health care equity is an objective of Meaningful Measures 2.0 (Hickey, & Giardino, 2019).
Centers for Medicare & Medicaid Services. (2021). National impact assessment of the Centers for Medicare & Medicaid Services (CMS) quality measures reports.
Hickey, J. V., & Giardino, E. R. (2019). The Role of the Nurse in Quality Improvement and Patient Safety. Journal of Neurological & Neurosurgical Nursing, 8(1), 30–
Johnston, M., & Magnan, M. A. (2019). Using a Fall Prevention Checklist to Reduce Hospital Falls: Results of a Quality Improvement Project. AJN American Journal
of Nursing, 119(3), 43–49. https://doi-org.ezp.waldenulibrary.org/10.1097/01.NAJ.0000554037.76120.6a
Singh, I., Edwards, C., Duric, D., Rasuly, A., Musa, S. O., & Anwar, A. (2019). Dementia in an Acute Hospital Setting: Health Service Research to Profile Patient
Characteristics and Predictors of Adverse Clinical Outcomes. Geriatrics (Basel, Switzerland), 4(1). https://doi-org.ezp.waldenulibrary.org/10.3390/geriatrics4010007
Observational Study Designs
Observational studies measure the patterns of disease exposure in a population to draw inferences about the etiology. Observational studies can be descriptive or analytic, descriptive studies include case reports and cross-sectional surveys that are used to depict an individual’s health characteristics. Analytic studies include case-control studies and cohort studies, they are used to test etiologic hypotheses (Friis, and Sellers, 2021).
Association Between the Risk Factor and Health Outcome
High blood Cholesterol and Coronary Artery Disease (CAD)
CAD is a major cause of disability and premature death throughout the world. The underlying pathology of atherosclerosis develops over many years and is usually advanced by the time symptoms occur, generally in middle age. The risk of developing CAD increases with age and includes age >45 years in men and >55 years in women (Hajar,2017). The prevalence of heart failure is increasing in the aging population, and heart failure is a disease with large morbidity and mortality. When there is too much cholesterol in the blood, it builds up in the walls of the arteries, causing a process called atherosclerosis, a form of heart disease. The arteries become narrowed and blood flow to the heart muscle is slowed down or blocked. The build-up of cholesterol causes lumps of hard fat called plaque to form on the artery walls. These can break off, block the artery, and cause heart attacks and strokes (Gidding, and Allen,2019).
According to (Varbo, and Nordestgaard, 2018), The association between the risk factor and the disease outcome is that high concentrations of nonfasting triglycerides and low-density lipoprotein cholesterol are associated with higher risk of heart failure in the general population.
A cohort study looks at 2 or more groups of people that have a different attribute (for example, some with high cholesterol and some do not) to try to understand how the specific attribute affects an outcome (CAD). The goal is to understand the relationship between one group’s shared attribute (in this case, high cholesterol) and its eventual outcome. Cohort studies helps to advance medical knowledge and practice by enabling researchers to get a better understanding of the risk factors that increase a person’s chances of getting a particular disease.
Strengths of Cohort Study for Addressing CAD
Using a cohort study for this health issue will help to identify the overall cumulative incidence of CAD in the population as disease outcomes of CAD like stroke, and peripheral arterial disease can be traced from the baseline of the study to a stipulated follow-up period (Al Rawahi et al., 2017).
cohort studies help researchers calculate the incidence rate, cumulative incidence, relative risk, and hazard ratio of health conditions.
cohort studies allow researchers to observe and track multiple outcomes from the same exposure.
Cohort studies allows researchers to measure the variables and the participants’ health outcomes with relative accuracy.
Cohort studies can last for years and the cost of running the study can add up.
There is potential for biases.
Participants may drop out due to lengthy time commitments which increases the risk for bias.
Lessons from Selected Study Design That Might Lead to Improvements in Population Health with Evidence from the Literature.
Evidence from Editors, (2020) suggests that cohort study design might lead to improvements in population health because through a cohort study, a researcher might identify subjects at a point in time when they do not have the outcome of interest which later, can be compared to the incidence of the outcome as cohort studies can collect data on people that covers a long period of time which can be used to investigate the causes of disease and to establish links between risk factors and health outcomes.
Al Rawahi, A. H., Lee, P., Al Anqoudi, Z., Al Busaidi, A., Al Rabaani, M., Al Mahrouqi, F., & Al Busaidi, A. M. (2017). Cardiovascular Disease Incidence and Risk Factor Patterns among Omanis with Type 2 Diabetes: A Retrospective Cohort Study. Oman medical journal, 32(2), 106–114. https://doi.org/10.5001/omj.2017.20
Editors, M. (2020). Correction to: General concepts in biostatistics and clinical epidemiology: observational studies with cohort design. Medwave, 20(01), e7776. https://doi-org.ezp.waldenulibrary.org/10.5867/medwave.2020.01.7776
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.
Gidding, S. S., & Allen, N. B. (2019). Cholesterol and Atherosclerotic Cardiovascular Disease: A Lifelong Problem. Journal of the American Heart Association, 8(11), e012924. https://doi.org/10.1161/JAHA.119.012924
Hajar, R. (2017). Risk factors for coronary artery disease: Historical perspectives. Heart Views, 18(3), 109–114. https://doi-org.ezp.waldenulibrary.org/10.4103/Heartviews.Heartviews_106_17
Varbo, A., & Nordestgaard, B. G. (2018). Nonfasting Triglycerides, Low-Density Lipoprotein Cholesterol, and Heart Failure Risk: Two Cohort Studies of 113 554 Individuals. Arteriosclerosis, Thrombosis, and Vascular Biology, 38(2), 464–472. https://doi-org.ezp.waldenulibrary.org/10.1161/ATVBAHA.117.310269
Identify the association between the risk factor and health outcome you selected, and suggest which observational study design you feel is most appropriate for examining that association.
Risk factors are identified as that which raises a person’s risk of contracting diseases. On the other hand, health outcomes are improvements in the wellbeing of certain healthcare investments or interventions. Some of the risk factors associated with depression and anxiety include; trauma, use of drugs and alcohol, stress buildup, other mental health disorders, inheritance from relatives, and stress due to an illness. Health outcomes that may result from this include; one suffers the risk of heart attacks, insomnia, feeling of sadness, and memory issues. The association between the two is that the risk factors lead to health outcomes. One develops depression and anxiety only after the risk factors are experienced in their body (Mayo Clinic, 2021). The observational study design that I feel is most appropriate for association examination is retrospective design.
Support your selection of the observational design, noting its strengths and limitations for addressing the health problem.
The researcher can establish hypotheses regarding possible links between a result and exposures using a retrospective research design and test those hypotheses deeper in the retrospective study design. Some several strengths and weaknesses result from using the study design; these studies are less expensive to perform. All that one requires is to identify the risk factors associated with depression, and later one can identify the possible depression health outcomes. A limitation is that the exposure factor is difficult to regulate (Talari & Goyal, 2020).
What might you be able to learn by using your selected study design that might lead to improvements in population health? Support your response with evidence from the literature.
Using retrospective study, I might learn that I ought to use patients’ medical records, interview the patients if possible, and use administrative databases. This will enable me to cause depression and anxiety among patients and get clear information of the health outcomes evident in a patient and the appropriate medication required (Ranganathan & Aggarwal, 2018). I also learned that retrospective study is a study of choice and is mainly used to examine a rare outcome among depression and anxiety patients.
Mayo Clinic. (2021). Anxiety disorders – Symptoms and causes. https://www.mayoclinic.org/diseases-conditions/anxiety/symptoms-causes/syc-20350961.
Ranganathan, P., & Aggarwal, R. (2018). Study designs: Part 1–An overview and classification. Perspectives in clinical research, 9(4), 184. https://pubmed.ncbi.nlm.nih.gov/30319950/.
Talari, K., & Goyal, M. (2020). Retrospective studies – utility and caveats. Journal Of The Royal College Of Physicians Of Edinburgh, 50(4), 398-402. https://doi.org/10.4997/jrcpe.2020.409