Agency for Healthcare Research and Quality [AHRQ] (2018) defines quality indicators [QI] as measures of healthcare quality that generate data to be utilized by healthcare leaders and administrators. The QI data is used for decisions on quality improvement, staffing, operational budget, and innovations. AHRQ develops QI to provide healthcare leaders along with evaluation tools (AHRQ, 2018). Nash et al. (2019) believe that a variety of QI is needed to achieve six areas for improvement identified by the Institute of Medicine [IOM] committee on safety. The six areas of improvement are; effectiveness, patient-centeredness, timeliness, efficiency, and equity (Institute of Medicine, Committee on Quality of Health Care in America [IOM], 2001). Quality improvement is categorized into three areas of quality of care: structural, process, and outcome (Institute for Healthcare Improvement [IHI], 2020).
Two Nurse-Sensitive Indicators of Quality in Ambulatory Care
I currently work in the ambulatory care setting in an integrated academic institution as the Senior Manager for Clinical Services and Operations for Neurosurgery, Stroke Neurology, and Neurology & Sleep. The two indicators chosen for this discussion are; pain assessment and follow-up and unplanned transfers to hospital (Start et al., 2018). These two QI are nursing-sensitive QI that relates to ambulatory care for my current practice. The work setting is the Physician Group Practice [PGP] for each of the three departments, with four neurosurgery clinic locations, one stroke clinic, and two sleep centers. The patient population is approximately 60% neurosurgery, 30% stroke, and 10% sleep therapy.
Early Quality Improvement Theories and Philosophies on the Development of the Two QI
The emphasis on QIs for ambulatory care became actualized in late 1997, with a committee appointed by the American Nurses Association [ANA] to expand nursing-sensitive quality indicators [NSQI] to ambulatory care (Martinez et al., 2015). The ANA committee aimed to show nursing contributions in the ambulatory practice in improving health outcomes and healthcare delivery cost efficiencies. Despite this push by ANA to expand NSQI to ambulatory care, the process was slowed until 2008 with Swan article that called for nurses to act on NSQI in ambulatory care. The American Academy of Ambulatory Care Nursing [AAACN] in 2013 committee started on the challenge for NSQI in ambulatory care, with the first set of NSQI being published in 2014 (Martinez et al., 2015).
The concept of pain assessment and follow-up is embedded in every aspect of patient care. Petiprin (2020) sees the nurse playing the most critical role in assessing and managing the patient’s pain. The evolution of patient pain assessment and management has always had nurses at the forefront. Nursing theories and quality tools have been used over the years by nurses to study and implement pain management (Petiprin, 2020b). Mid-ranged psychological theories such as Kolcaba’s Comfort Theory are used by nursing research in the evolution of pain management in nursing care (Petiprin, 2020a).
Marquet et al. (2015) see unplanned admission transfers to hospital, as the marker for patients’ adverse events [AES]. AEs concerns are international health issues for healthcare leaders, professionals, administrators, patients, and their families. Annually AEs lead to unintended injuries or complications, disabilities, deaths, prolongation of hospital stay, and higher healthcare costs rather than the patient’s disease (Marquet et al., 2015).
Two Nursing Research Articles that Relate to Two QI Influence on Practice
Article one: Pain Assessment and Follow-up
Meissner et al. (2017) examine the use of QIs in acute postoperative pain management [POPM]. The goal was to use QIs to facilitate caregivers to differentiate between good and poor quality of pain management. The researchers seek to explore the evidence gathered from pain specialists’ experiences in managing patients’ acute pain postoperatively and literature review using QI for acute POPM. The specialist for this study was chosen from Europe and the United States of America [USA]. The inclusion criteria for the participants were a member of a Pain Advisory Board (Meissner et al., 2017). The QIs assessed the healthcare providers’ services and how efficient the interventions were in relieving acute postop pain.
The QI measures used were documentation, timeliness of pain assessment, pain reassessment, and timeliness to giving analgesic medication. Pain assessment was done on day one postop using the numerical rating scale of 0 – 10 was used for pain assessment, with 0 being no pain and 10 being the maximum pain experienced. The review found that patients had poor pain management, with pain levels being moderate to severe, 4 – 10 on the numerical scale (Meissner et al., 2017). The data review found that the barriers to acute POPM were; cost to treat acute pain, lack of knowledge on pain management among staff, lack and unclear instructions, inadequate pain assessments, and sub-optimal care (Meissner et al., 2017).
Article Two: Unplanned Transfers to Hospital
Marquet et al. (2015) view unplanned transfers to the hospital due to preventable adverse patient reactions. The researchers’ research was a three-stage retrospective review on screening, records review, and consensus judgment over six months in Belgium. The study aimed to examine the frequency of preventable adverse reactions and lead to unplanned admission or higher levels of care. A total of 830 medical records were reviewed, 456 of the medical record revealed patient adverse reactions. The review found that 56% of the adverse events were preventable that lead to unplanned hospital admission. This review also revealed that 25% of these adverse reactions required a higher level of care in the intensive care unit. Unplanned transfer to hospital is also a quality standard for The Joint Commission [TJC]. The AHRQ provides a toolkit for healthcare organizations to report and analyze the prevalence and rate of unplanned transfers to hospital (Rahn, 2016).
Agency for Healthcare Research and Quality. (2018). AHRQ quality indicators. https://www.ahrq.gov/cpi/about/otherwebsites/qualityindicators.ahrq.gov/qualityindicators.html
Institute for Healthcare Improvement. (2020). How to improve with the model for improvement. chi.org. https://education.ihi.org/topclass/topclass.do?CnTxT-144791570-contentSetup-tc_student_id=144791570-item=967-view=1
Institute of Medicine, Committee on Quality of Health Care in America. (2001). Crossing the quality chasm: A new health system for the 21st century. ProQuest Ebook Central. https://ebookcentral.proquest.com
Marquet, K., Claes, N., De Troy, E., Kox, G., Droogmans, M., Schrooten, W., Weekers, F., Vlayen, A., Vandersteen, M., & Vleugels, A. (2015). One fourth of unplanned transfers to a higher level of care are associated with a highly preventable adverse event. Critical Care Medicine, 43(5), 1053–1061. https://doi.org/10.1097/ccm.0000000000000932
Martinez, K., Battaglia, R., Start, R., Mastal, M. F., & Matlock, A. M. (2015). Nursing-sensitive indicators in ambulatory care. Nursing Economic$, 33(1). https://doi.org/https://www.aaacn.org/sites/default/files/documents/news-items/NursingEcARTICLE_NursingSensitiveIndicatorsinAmbulatoryCare.pdf
Meissner, W., Huygen, F., Neugebauer, E. A., Osterbrink, J., Benhamou, D., Betteridge, N., Coluzzi, F., De Andres, J., Fawcett, W., Fletcher, D., Kalso, E., Kehlet, H., Morlion, B., Montes Pérez, A., Pergolizzi, J., & Schäfer, M. (2017). Management of acute pain in the postoperative setting: The importance of quality indicators. Current Medical Research and Opinion, 34(1), 187–196. https://doi.org/10.1080/03007995.2017.1391081
Nash, D. B., Joshi, M. S., Ransom, E. R., & Ransom, S. B. (Eds.). (2019). The healthcare quality book: Vision, strategy, and tools, fourth edition (4th ed.). Health Administration Press.
Petiprin, A. (2020a). Kolcaba’s theory of comfort. Nursing Theory. https://nursing-theory.org/
Petiprin, A. (2020b). Pain scale 1-10. Nursing Theory. https://nursing-theory.org/articles/pain-scale.php
Rahn, D. J. (2016). Transformational teamwork. Journal of Nursing Care Quality, 31(3), 262–268. https://doi.org/10.1097/ncq.0000000000000173
Start, R., Matlock, A. M., Brown, D., Aronow, H., & Soban, L. (2018). Realizing momentum and synergy: Benchmarking meaning ambulatory care nurse-sensitive quality indicators. Nursing Economic$, 36(5), 246–251. https://doi.org/https://www.aaacn.org/sites/default/files/documents/NSI-Measure-Table.pdf
Nursing-sensitive indicators (NSIs) can be an essential tool in identifying patient care issues that could arise in the healthcare setting. By analyzing the data on specific NSI, the quality of patient care can be optimized, and patient satisfaction can be improved. As a result, NSIs have become a progressively effective and dependable method to support nursing care quality and performance measurement in the healthcare establishment, including evaluating clinical nursing practice (Heslop et al., 2014). The American Nurses Association (ANA) and the National Database of Nursing Quality Indicators (NDNQI) are two sources of information and guidelines for nurses and nurse managers to use in planning patient care and workloads for each nursing unit. Quality indicators refer to clear, measurable items to outcomes and demonstrate the effect on health and population (Rahn, 2016). The different frameworks and theories appeal to care that concentrates on the patients’ individual needs, wishes, and cultural practices.
Two Nurse-Sensitive Indicators of Quality
Patients with Chronic Kidney Disease (CKD) are at risk for End-Stage Kidney Disease (ESKD), leading to dialysis or transplantation (Manns et al., 2017). To optimize kidney health, health systems should monitor the quality of care provided to patients suffering from CKD (Manns et al., 2017) with specifics quality indicators (QI) such as nosocomial infections and falls. These NSIs affect other aspects of nursing other than renal care, and as a concept, it is effective in developing nursing care implementation (Heslop et al., 2014). However, the conceptual basis, theoretical role, meaning, use, and interpretation of the concept of NSIs tend to differ. Generally, the studies of indicators of nosocomial infections and falls derive from the NDNQI point to facilitate the ability of health organizations to act in response to patient and staff needs (Montalvo, 2007).
Abbasi et al. (2020) described the quality indicator of nosocomial infection in CKD patients. This is a quality assessment for improving patient quality outcomes. Nosocomial infections are classified as NSI because the best practices add to nursing’s knowledge base and help nurses nationwide to advance nursing practice and patient outcomes (Montalvo, 2007). ESRD is one of the leading causes of morbidity and mortality, and more extended hospital stay, prolong catheterization, decrease white blood cell count. Multiple comorbidities are risk factors that reduce quality outcomes and increase dialysis patients for nosocomial infections. Nursing interventions include ways to decrease nosocomial infections, such as pneumonia, urinary tract infections (UTIs), bloodstream infections, and diarrhea (Abbasi et al., 2020, p. 6). Reducing risk factors will enhance the patient’s QoL, a conceptual framework developed by Walker and Avant (Boudreau & Dubé, 2014). Increasing the life expectancy of this patient population is a significant health target, and health care professionals can achieve this by minimizing infection complications (Abbasi et al., 2020). The relationship between ESRD and NSI nosocomial infections is very intriguing.
Gao et al. (2018) identified a group of NSQIs to evaluate the quality of clinical nursing of ESRD patients in the dialysis unit. They utilized the Delphi surveys to collect opinions from independent experts, where 11 NSQIs were identified. One of the NSQIs determined was the incidence of falls among hemodialysis patients. The rate of falls in ESRD is higher compared to the general population. Risk factors identified for falls are diabetic visual impairment, fragile bones, dizziness, fatigue, and cardiopulmonary dysfunctions; thus, making falls an essential NSQI. Therefore, nursing interventions comprise of efforts to prevent falls and associated injuries by intensive monitoring and education.
QIs are established and supported by the NDNQI to provide for the need for multifunctional and comprehensible quality measures used to measure healthcare performance. The usage of QI is evidence-based and can be used to differentiate inconsistencies in the eminence of care provided to people at outpatient and inpatient facilities.
Abbasi, S. H., Aftab, R. A., & Chua, S. S. (2020). Risk factors associated with nosocomial infections among end stage renal disease patients undergoing
hemodialysis: A systematic review. PloS One, 15(6), e0234376. https://doi-
Boudreau, J. É., & Dubé, A. (2014). Quality of life in end stage renal disease: A concept
analysis. CANNT Journal, 24(1), 12–20.
Gao, J. L., Liu, X. M., Che, W. F., & Xin, X. (2018). Construction of nursing-sensitive
quality indicators for haemodialysis using Delphi method. Journal of clinical
nursing, 27(21-22), 3920–3930. https://doi.org/10.1111/jocn.14607
Heslop, L., Lu, S., & Xu, X. (2014). Nursing-sensitive indicators: a concept
analysis. Journal of advanced nursing, 70(11), 2469–2482.
Manns, L., Scott-Douglas, N., Tonelli, M., Weaver, R., Tam-Tham, H., Chong, C., &
Hemmelgarn, B. (2017). A population-based analysis of quality indicators in
CKD. Clinical Journal of the American Society of Nephrology, 12(5), 727-733.
Montalvo, I., (2007). The National Database of Nursing Quality Indicators. OJIN: The
Online Journal of Issues in Nursing, 12(3).
Rahn, D. J. (2016). Transformational Teamwork. Journal of Nursing Care
Quality, 31 (3), 262-268. doi: 10.1097/NCQ.0000000000000173.
Descriptive epidemiology is used to characterize the distribution of disease within a population. It describes the person, place, and time characteristics of disease occurrence. Analytical epidemiology is used to test hypotheses to determine whether statistical associations exist between suspected causal factors and disease occurrence. Since disease does not occur randomly but in patterns that reflect the underlying factors, descriptive epidemiology portrays the occurrence of disease with respect to the characteristics of person, place, and time. The person encompasses who is being affected, like males versus females? Rich versus poor? and other factors. Place relates to the geographical location of the problem like in cities or rural areas, in some states more than others, or in the United States versus other countries and time refers to when the problem is occurring like during summer or winter. Descriptive epidemiology is used to discover clues to the causes of health and illness as it helps to recognize patterns of disease and generate hypothesis regarding their underlying causes (Friis, and Sellers, 2021).
Prescription Drug Overdose
The selected health problem for this discussion is prescription drug overdose.
Prescription drug abuse and overdoses are a major public health concern. In 2019, nearly 50,000 people in the United States died from opioid-involved overdoses. The misuse of and addiction to opioids including prescription pain relievers, heroin, and synthetic opioids such as fentanyl is a serious national crisis that affects public health as well as social and economic welfare (CDC,2017). Men are more likely than women to engage in illicit drug use but women may be more susceptible to craving and relapse, which are key phases of the addiction cycle. Findings from a recent literature suggests that women 40-64 years are more likely to use prescription opioids compared to men. Seventy-nine percent of individuals who overdose on opioids are non-Hispanic White, 10% are Black and non-Hispanic, and 8% are Hispanic (Silver and Hur, 2020).
Opioid addiction, such as heroin and prescription pain medication, is a growing problem in the United States and internationally. Knowledge and respect for the epidemiology of opioid abuse and addiction, its consequences, and the role of the prescriber and nurse in reducing the risk of opioid abuse and addiction is critical to reduce the incidence of adverse outcomes and deaths (Green, 2017). Drug poisoning deaths involving opioid analgesics have more than tripled since 1999, with more than 16,000 deaths in 2013 alone. The CDC has been focused on boosting resources for State prevention efforts in conjunction with other Federal efforts to help States expand and intensify their work to address this growing problem (CDC,2017).
The United States have seen an epidemic of opioid misuse and abuse that has been called the deadliest drug crisis in American history. Opioid addiction includes the abuse of prescription, nonprescription, and illegal pain relievers The misuse of and addiction to opioids, including prescription opioids, heroin, and synthetic opioids such as fentanyl, is a serious problem that affects not only the health of many Americans but also the social and economic welfare of the country (Mattson et al., 2021).
Data Sources with Strengths and Limitations
Data sources utilized for this discussion are secondary or use of existing data from hospital outpatient statistics. The strength of this data source is that clinics and outpatient departments provide a large volume of care for this population. The limitations are that hospital records are not well developed sometimes, and diagnostic data may be incomplete. Another data source is data from public health clinics. A strength of this data source is that the data can be used for possible identification of cases for disease study and the limitation is that the population denominator is unknown (Friis, and Sellers, 2021).
Two Methods to Use in Collecting Raw Data For descriptive epidemiology of Prescription Drug Overdose
To determine the descriptive epidemiology of prescription drug overdose, it is important to identify and classify which types of drugs are involved in an overdose, how often they are involved, and how that involvement changes over time. Data can be collected from two methods, primary and secondary data sources. Primary data is original data collected by interviewing people while secondary data is data collected by other individuals or organization.
How Methods Would Influence the Completeness of Case Identification
Identifying the type of drug and the drug involvement will help to determine appropriate prevention and response activities.
Centers for Disease Control and Prevention. (2017). Opioid overdose. https://www.cdc.gov/drugoverdose/states/state_prevention.html
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.
Green, J. (2017). Epidemiology of Opioid Abuse and Addiction. Journal of Emergency Nursing, 43(2), 106–113. https://doi-org.ezp.waldenulibrary.org/10.1016/j.jen.2016.09.004
Mattson, C. L., Tanz, L. J., Quinn, K., Kariisa, M., Patel, P., & Davis, N. L. (2021). Trends and Geographic Patterns in Drug and Synthetic Opioid Overdose Deaths – United States, 2013-2019. MMWR. Morbidity and Mortality Weekly Report, 70(6), 202–207. https://doi-org.ezp.waldenulibrary.org/10.15585/mmwr.mm7006a4
Silver, E. R., & Hur, C. (2020). Gender differences in prescription opioid use and misuse: Implications for men’s health and the opioid epidemic. Preventive Medicine: An International Journal Devoted to Practice and Theory, 131. https://doi-org.ezp.waldenulibrary.org/10.1016/j.ypmed.2019.105946
Obesity is a global challenge. Many adverse events are related to obesity. The purpose of this discussion is to explore obesity and its relation to descriptive epidemiology.
The field of descriptive epidemiology depicts the occurrence of a health concern considering characteristics of person, place, and time (Friis & Sellers, 2021). Furthermore, defining features within these characteristics can help outline patterns and possible underlying causes (Friis & Sellers, 2021). By doing this, descriptive epidemiology is a valuable tool to prevent disease and plan for a satisfactory response to a health concern.
Obesity is a population health concern as it can have serious health, economic, and social consequences (Centers for Disease Control and Prevention (CDC), 2021a). Health complications include hypertension, dyslipidemia, diabetes, coronary heart disease, stroke, sleep disturbances, mental illness, and body pain (CDC, 2021). Due to obesity, health care costs increase. For obesity-related reasons, affected individuals may not be able to participate in certain activities or work.
Person, Place, and Time
Obesity can result from various factors, including genetics, environmental influences, and behaviors (CDC, 2021). By utilizing descriptive epidemiology methods, preventative measures and pertinent interventions can be applied to susceptible populations. Person, place, and time are determining characteristics.
Determinants of place include international, geographic variations, urban or rural differences, and localized occurrences (Friis & Sellers, 2021). In a nationally representative cross-sectional sample study, Wen et al. (2017) found that the odds of obesity were greater in rural areas compared to urban areas. Educational and environmental factors are relevant to the disparity (Wen et al., 2017). Globally, 61% of the population of Nauru is obese (Procon.org, 2020).
Aspects of time include cyclic fluctuations, point epidemics, secular time trends, and clustering (Friis & Sellers, 2021). Robinson et al. (2021) suggest that the Covid-19 pandemic may have had a disproportionately sizeable and negative influence on weight-related behaviors among adults with a higher BMI.
Characteristics defining person include race, sex, and age. In 2017, 42.5% of adults over 20 years of age had obesity (CDC, 2021b). According to the CDC (2021b), 34 states and the District of Columbia had an obesity prevalence of 35% among non-Hispanic Black adults. In most countries, females are more obese than males (Ameye & Swinnen, 2019). In a Brazilian study, Araujo et al. (2018) conclude that racial disparities in obesity are socioeconomic status level and sex dependent.
Data Sources and Associated Strengths and Limitations
Most of the data sources from this discussion utilized the PubMed database. Strengths from this database include a wide variety of baseline data and information. A limitation may be that the representative sample in an article may not accurately portray the whole population. Most of the articles utilized secondary data as it was derived from existing surveys. One of the articles utilized primary data as participants completed a questionnaire. A strength is that the data is from the original source, but a limitation may be that the participant may falsely answer. Another data source utilized was the National Center for Health Statistics by the CDC. This is a federal government website that provides various health statistics. This source is valuable as there is a great deal of health statistics that are easily accessible.
Raw Data Collection
Two methods to collect raw data are wearable technologies and hospital data. Public health professionals can utilize hospital data on to determine if obesity is present. However, hospital statistics lack the representation of a specific population (Friis & Sellers, 2021). Wearable technologies can provide data on vital signs and physical activity. However, this can present challenges as devices may not be consistently utilized to provide accurate data for the completeness of a study. Furthermore, public health professionals must respect privacy and data sharing principles.
Ameye, H., & Swinnen, J. (2019). Obesity, income and gender: the changing global relationship. Global Food Security, 23, 267-281.
Araujo, M. C., Baltar, V. T., Yokoo, E. M., & Sichieri, R. (2018). The association between obesity and race among Brazilian adults is dependent on sex and socio-economic status. Public Health Nutrition, 21(11), 2096–2102. https://doi-org.ezp.waldenulibrary. https://www.cdc.gov/obesity/index.htmlorg/10.1017/S1368980018000307
Centers for Disease Control and Prevention. (2021a). Overweight and obesity. Overweight and obesity. https://www.cdc.gov/nchs/fastats/obesity-overweight.htm
Centers for Disease Control and Prevention. (2021b). Obesity and overweight. National center for health statistics. https://www.cdc.gov/nchs/fastats/obesity-overweight.htm
Friis, R. H., & Sellers, T.A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.
Procon.org. (2020). Global obesity levels. https://obesity.procon.org/global-obesity-levels/
Robinson, E., Boyland, E., Chisholm, A., Harrold, J., Maloney, N. G., Marty, L., Mead, B. R., Noonan, R., & Hardman, C. A. (2021). Obesity, eating behavior and physical activity during COVID-19 lockdown: A study of UK adults. Appetite, 156. https://doi-org.ezp.waldenulibrary.org/10.1016/j.appet.2020.104853
Wen, M., Fan, J. X., Kowaleski-Jones, L., & Wan, N. (2018). Rural-urban disparities in obesity prevalence among working age adults in the United States: Exploring the mechanisms. American Journal of Health Promotion, 32(2), 400–408. https://doi-org.ezp.waldenulibrary.org/10.1177/0890117116689488
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