After completing this chapter, students will be able to:
• Defi ne and explain quality , equity , and effi ciency in terms of health care. • Identify and describe key population health outcomes. • Discuss how the United States has fared in measures of health outcomes in
recent years. • Identify common clinical outcomes used to evaluate quality of health care. • Defi ne and explain clinical effectiveness and patient safety . • Defi ne healthcare-associated infections and describe their prevalence and
methods to prevent them. • Identify organizations that have a major infl uence on health care quality and
explain their functions. • Describe the role of data and information technology in assessing the
performance of health care systems.
Health Care System Performance
© Artizans Entertainment Inc. By Chris Wildt.
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Copyright Springer Publishing Company. All Rights Reserved. From: Jonas' Introduction to the U.S. Health Care System, Ninth Edition DOI: 10.1891/9780826174048.0007
2 16 I ■ U. S. Hea l t h Ca re S y s t em : P re sen t S t a t e
INTRODUCTION Satisfactory performance is critical to health care organizations and to the health care system as a whole. It is the basis of the confi dence and support they need from individuals and insti- tutions to survive and succeed. Thus, assessing health system performance is a vital function. And although health care performance has always been informally evaluated, the Flexner Report of 1910 ( Duffy, 2011 ) started medicine and the health care fi eld on the path toward rigorous scientifi c assessments. However, it was the widespread availability of electronic computing capacity in the 1970s that allowed health systems assessments to move from small, time-consuming studies with limited fi ndings to current evaluations based on “big health data.” Today’s performance assessments, supported by electronic data capture and powerful computing capabilities, are comprehensive and timely compared with the past.
As a result of the importance of health systems performance, it is essential for health care professionals to understand the assessment process. First, they increasingly contribute—in big or small ways—to the process of assessing and improving system performance. Second, they are greatly affected by the results of assessing health system performance. Whether they provide direct care to patients as a nurse, therapist, physician, or other clinician, or contribute to management of a health care organization—be it a hospital, nursing home, clinic, or other—their careers will be infl uenced by the assessment process. Over time, the insights gained from evaluating health systems performance drive change in professional roles, practice, and training.
There are numerous frameworks for evaluating health care system performance, and, as discussed in Chapter 1, Introduction, this book uses the work of Aday et al., which identifi es three evaluation criteria for health care systems: (a) quality, (b) equity, and (c) effi ciency ( Aday, Begley, Lairson, & Balkrishnan, 2004 ; Aday, Begley, Lairson, & Slater, 1993 ).
Health care system performance may be assessed at the microlevel—for organizations including hospitals, physician practices, long-term care facilities, and other health care settings—or the macrolevel—for regions, states, nations, and other large population groups. At the microlevel, performance assessment is conducted with identifi able health care pro- viders, organizations, and treatments. For example, we may be interested in the quality of care received by cataract patients in an ambulatory surgery center as indicated by the rate of postsurgical complications. Or if we are focused on health care effi ciency , we might examine medical care costs for cataract surgery in that ambulatory center. If our interest is equity , we could ask if cataract patients with private health insurance have fewer postsurgical compli- cations than those with Medicaid, in that ambulatory center.
At the macro level of regions, states, and nations, the picture is more complex. Many health care providers and organizations are assessed simultaneously using global measures of health outcomes such as life expectancy and mortality rates. For example, we might be interested in how the health care system, as a whole, performs for seniors in a particular region. To evaluate the quality of care delivered by the health system in this region, we might use chronic disease mortality rates for people over 65. Or if our interest is equity , we might compare the chronic disease mortality rates of seniors with supplemental health insurance to those with Medicare only to determine whether mortality is higher in the Medicare-only group. In the case of effi ciency , we could examine global costs for hospitalization among seniors in the region. In all cases, the quality , equity , and effi ciency of the health care system for seniors is inferred from global measures that aggregate many health care providers and organizations. We actually do not know how many seniors received health care and, if they did, what treatments were received, how often, and by what health care providers and organizations. These details underlie the results but are obscured because the data are not available.
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However, it should be noted that the capacity to collect and process “big health data” continues to increase and, as a result, macrolevel information is becoming better and better. This topic is discussed in the last section of the chapter.
Regardless of whether evaluation occurs at the microlevel or macrolevel, the process re- quires a comparison to be useful. Referents are either a:
• “Gold standard”—a recognized “best” performance, or a • “Benchmark”—a starting point from which to begin the comparison
An entity’s own performance may be the “benchmark.” Here, the question asked is whether the entity—ambulatory practice, hospital, region, nation, or other—has improved over its own past performance. Or the “benchmark” may be the performance of another entity— ambulatory practice, region, nation, or other. The question, then, is whether the entity is per- forming as well as or better than a peer, perhaps one that is recognized for high performance.
In this chapter, we discuss each criterion for assessing health systems performance — quality, equity, and effi ciency —including common indicators and measures for each. We emphasize the quality criterion, describing the process for assessing quality in health care systems and providing an example of a quality assessment and improvement initiative. The major organi- zations that infl uence health systems’ performance are identifi ed. The chapter concludes with the role of data and information systems in assessment of health systems performance.
HEALTH CARE QUALITY Using the model originally developed by Donabedian, health care quality is assessed in terms of structure, process, and outcomes ( Donabedian, 1980–1985 ). “Structure ... is meant to desig- nate the conditions under which care is provided” ( Donabedian, 2003 , p. 46). It includes mate- rial resources, such as facilities and equipment; human resources, such as number and qualities of professional and support personnel providing health care; and organizational characteris- tics, such as (for individual facilities such as hospitals) nonprofi t status, academic affi liation, and governing structure. Examples of structure-oriented questions are as follows: What is the nurse-to-patient ratio on a hospital fl oor? What is the age of the facility? What proportion of a hospital’s patients do not have insurance, are receiving Medicaid, or are covered by Medicare? Are the physicians in a practice salaried employees or paid on a fee-for-service basis?
Process “is taken to mean the activities that constitute health care—including diagnosis, treatment, rehabilitation, prevention, and patient education—usually carried out by pro- fessional personnel, but also including other contributions to care, particularly by patients and their families” ( Donabedian, 2003 , p. 46). For example, a study of health care process might ask the following questions: Is infection control policy followed by the hospital staff? How long does it take for the primary care physician to receive the test results needed for diagnosis? How does the treating physician transmit information about a drug’s side effects to the patient? What is the waiting time in the emergency room? How much time does a physician spend with a patient, on average, for an annual physical? What is the standard practice among the physician staff for treating a particular health condition, such as acute myocardial infarction or stroke?
Structure and process infl uence the outcomes of health care. For example, each of the structure- and process-oriented issues just mentioned may lead to poor health care out- comes, but they are not outcomes in themselves. Outcomes “are taken to mean changes (desirable or undesirable) in the health of individuals and populations that can be attributed to health care” ( Donabedian, 2003 , p. 46).
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Ultimately, our interest is in outcomes, and we ask: Have the structures and processes of the health care system resulted in the desired outcome: quality , equitable , and effi cient health care ?
Generally speaking, there are two types of outcomes used to assess the quality of health care systems:
• Population health outcomes • Clinical (or healthcare-associated) outcomes
Population health outcomes are generalized outcomes that are not attributed to a specifi c medical intervention. They include, for example, life expectancy and mortality rates for pop- ulations with a varied history of health problems and medical care. The age-adjusted mor- tality rate for all people living in a geographic region is an example. Clinical outcomes result from medical interventions for particular health conditions. They are directly attributable to health care processes. The age-adjusted mortality rate for patients who have undergone open-heart surgery is an example.
We begin with population health outcomes and then consider clinical (healthcare-associated) outcomes .
Population Health Outcomes Health outcomes can be measured at the population level and used to evaluate the qual- ity of care provided by a health care system ( Kindig, 1997 ). Population health outcomes include population mortality and morbidity rates, and they are used in macrolevel perfor- mance evaluations of regions, states, and nations. The impact of health care on these rates is presumed even though there is no explicit measure of health care interventions among the population considered. If, for example, a disease-specifi c mortality rate is higher in one region than another, we assume that the health care system is not optimal for that disease in the region with the higher mortality rate. There is an inferred, not explicit, connection between the health care system and the population health outcome.
Historically, population health outcomes that have been used to assess the quality of health systems are life expectancy, premature death rate, time lost to premature death, age-adjusted death rates, disease-specifi c death rates, and infant mortality rate. The World Health Organization (WHO) also emphasizes maternal mortality and the under-5 mortality rate. The most common are defi ned as follows:
• Life expectancy is defi ned as the number of years of life that individuals in a pop- ulation can be expected to live, on average. Life expectancy at birth is commonly reported. It is defi ned as the average number of years that a newborn can expect to live, if he or she were to be exposed throughout life to the conditions prevailing at the time of his or her birth.
• Using life expectancy, premature death can be calculated. Premature death is de- fi ned as death before the expected age of death for an individual’s population. The time lost to premature death, also called years of potential life lost (YPLL), is based on the difference between the actual age at death and the expected age at death. Deaths at a younger age are weighted more heavily in the YPLL, providing an indi- cator of the severity of premature death’s impact on the population. It is expressed as years lost per 100,000 population.
• Mortality (or death) rates are measures of the frequency of death in a defi ned popu- lation during a given time period, often a year. Mortality rates are usually expressed
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as the number of deaths per 100,000 population. There are a number of commonly used death rates.
• The crude death rate is the number of deaths from all causes for a population during a defi ned time period, often a year, and usually expressed as 100,000 per population.
• The cause-specifi c death rate is the number of deaths from a particular health con- dition (e.g., stroke) for a given population during a defi ned time period, often a year, and usually expressed as 100,000 per population.
• The age-specifi c death rate is the number of deaths among individuals in a specifi c age group (e.g., adults ages 39–64) in the population of that age during a defi ned time period, often a year, and usually expressed as 100,000 per population.
• The age-adjusted death rate takes into account the population’s age distribution when calculating mortality rate. Using a statistical method that “standardizes” the target population to a reference population, this measure is commonly used when comparing mortality rates across different populations.
• The infant mortality rate is the number of deaths among infants between birth and exactly 1 year of age per all births in the population during a defi ned time period, often a year. This rate is expressed per 1,000 live births.
A more recent population health concept takes into account quality of life. Healthy life expectancy (HALE) at birth is defi ned by WHO as the “average number of years that a person can expect to live in ‘full health’ by taking into account years lived in less than full health due to disease and/or injury” ( WHO, 2012 , p. 1). HALE is a measure that combines length and qual- ity of life into a single estimate that indicates years of life that can be expected in a specifi ed state of health ( Kindig, 1997 , p. 45). Other health-adjusted life expectancy measures are quali- ty-adjusted life years (QALYs), which emphasize the individual’s perceived health status as the indicator of quality of life; disability-adjusted life years (DALYs), which combine mortality and disability measures; and years of healthy life (YHL), which combine perceived health and dis- ability activity limitation measures from the National Health Interview Survey ( Kindig, 1997 ).
Next, we briefl y consider the performance of the U.S. health care system, based on sev- eral population health indicators. This section relies on data from WHO, the Organisation for Economic Co-operation and Development (OECD), and the National Center for Health Statistics (NCHS), a component of the Centers for Disease Control and Prevention (CDC).
United States Compared With Other Countries
In this discussion, we examine life expectancy , infant mortality , and years of potential life lost — comparing the United States with all OECD member countries (see Table 7.1). As all OECD members are democratic countries with market economies, this is an interesting comparison. The OECD member countries include wealthy, developed nations such as the United States, Germany, Japan, and Australia, as well as those less developed but with similar democratic values and economic goals, such as Poland, Mexico, Turkey, and Estonia. Unfortunately, the United States’ performance on these indicators is near the bottom, along with countries that are far less developed and have far fewer resources.
First, we consider life expectancy . In 2016, the life expectancy of women in the 35 OECD member countries varied widely—from 77.8 years in Mexico to 87.1 years in Japan, with an average of 83.4 years. The United States was 30th at 81.1 years. Only the Slovak Republic (80.7), Turkey (80.7), Hungary (79.7), Latvia (79.6), and Mexico (77.8) had a lower life expec- tancy for women than the United States.
For men, there are similar results. In 2016, life expectancy for men in the 35 OECD member countries ranged from 69.8 years in Latvia to 81.7 years in Switzerland, with an average life expectancy of 78.1 years. The life expectancy for men in the United States was 76.1 years, as in the
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Czech Republic. Only seven of the 35 countries had a lower male life expectancy than did the United States—Latvia, Mexico, Estonia, Poland, Slovakia, Hungary, and Turkey (see Table 7.1).
Comparison of infant mortality rates in the United States with the other OECD countries also indicates a problem in the United States. In 2016, the U.S. IMR was 5.9 per 1,000 live births. Although this rate is low, there are only three countries among the OECD member states with a higher IMR—Chile (6.9), Turkey (10.0), and Mexico (12.1). The lowest IMR was 0.7/1,000 live births in Finland. The OECD average IMR was 3.9 (see Table 7.1).
Finally, a review of years of potential life lost among the OECD member countries confi rms a problem in the United States. The following fi gures are per 100,000 men or women, 0-69 years old. In 2016, the YPLL among men in OECD member states ranged from 2,782 years in Norway to 9,570 years in Latvia. The average for all OECD member countries was 4,296 years. The U.S. YPLL was 5,909 years, signifi cantly higher than the OECD average. In only six OECD mem- ber countries was the YPLL higher than in the United States: Latvia, Mexico, Estonia, Poland, Hungary, and the Slovak Republic. The YPLL for women in the United States was 3,523 years, which was higher than that in all other countries except Mexico, where it was 4,604 years. The average for all OECD member countries for women was 2,247 years (see Table 7.1 ).
TABLE 7.1 Population Health Outcomes in OECD Member Countries, 2016
OECD Member Country
Infant Mortality a
Life Expectancy at Birth (2016 or Nearest Year)
Potential Years of Life Lost, All Causes b
Life Expectancy at Birth (2016 or Nearest Year)
Potential Years of Life Lost, All Causes c
Australia 3.1 84.6 2,013.3 80.4 3,420.9
Austria 3.1 84.1 1,913.8 79.3 3,401.7
Belgium 3.2 84.0 2,266.8 79.0 3,732.1
Canada 4.7 83.9 2,368.7 79.8 3,669.8
Chile 6.9 82.7 2,815.4 77.1 5,098.5
Czech Republic 2.8 82.1 2,235.7 76.1 4,469.8
Denmark 3.1 82.8 2,140.8 79.0 3,319.0
Estonia 2.3 82.2 2,686.9 73.3 6,932.5
Finland 1.9 84.4 1,785.8 78.6 3,788.6
France 3.7 85.5 2,039.4 79.2 4,019.2
Germany 3.4 83.5 2,129.7 78.6 3,758.3
Greece 4.2 84.0 2,061.1 78.9 4,257.9
Hungary 3.9 79.7 3,135.5 72.6 6,595.0
Iceland 0.7 84.1 1,486.6 80.4 2,876.0
Ireland 3.0 83.6 1,975.8 79.9 3,404.2
Israel 3.1 84.2 1,744.0 80.7 3,072.0
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Quality-of-Life Adjusted Measures The WHO (2015) comparisons of the United States with a subset of the most developed OECD member countries indicate, once again, that the U.S. population is not as healthy as one would expect given its wealth, as well as the enormous amount spent on health care. In 2013, HALE at birth for males was 68 years in the United States, the lowest ranked country of the 13 (Finland and United States were at the bottom, with 68 years). Japan was ranked fi rst (72 years). For HALE at birth for females, the United States and Denmark were ranked at the bottom in 2013, with 71 years. In 2012, the age-standardized DALY per 100,000 popu- lation for all causes of death was higher in the United States than in any of its 12 peer coun- tries (22,775/100,000 population). The next highest DALY was 20,376/100,000 population in the United Kingdom.
TABLE 7.1 Population Health Outcomes in OECD Member Countries, 2016 (continued)
Italy 2.8 85.6 1,689.5 81.0 2,965.3
Japan 2.0 87.1 1,600.9 81.0 2,923.2
Korea 2.8 85.4 1,681.8 79.3 3,488.4
Latvia 3.7 79.6 3,471.3 69.8 9,570.8
Luxembourg 3.8 85.4 1,255.4 80.1 2,880.8
Mexico 12.1 77.8 4,604.3 72.6 8,297.2
Netherlands 3.5 83.2 2,140.0 80.0 2,846.4
New Zealand 5.7 83.4 2,429.4 80.0 3,756.2
Norway 2.2 84.2 1,711.0 80.7 2,782.3
Poland 4.0 82.0 2,685.0 73.9 6,749.1
Portugal 3.2 84.3 1,889.7 78.1 4,295.9
Slovak Republic 5.4 80.7 2,855.4 73.8 6,397.4
Slovenia 2.0 84.3 1,826.8 78.2 3,993.7
Spain 2.7 86.3 1,619.8 80.5 3,111.9
Sweden 2.5 84.1 1,775.3 80.6 2,856.4
Switzerland 3.6 85.6 1,777.1 81.7 3,047.3
Turkey 10.0 80.7 2,985.0 75.3 5,012.8
United Kingdom 3.8 83.0 2,323.6 79.4 3,676.7
United States 5.9 81.1 3,523.7 76.1 5,909.1
OECD Average 3.9 83.4 2,247.0 78.1 4,296.5
a Deaths per 1,000 live births, no minimum threshold of gestation period or birth weight. b Years lost/100,000 women, aged 0 to 69 years. c Years lost/100,000 men, aged 0 to 69 years. SOURCE: Data from Organisation for Economic Co-operation and Development. (2019). OECD health statistics 2018—Frequently requested data . Retrieved from http://www.oecd.org/health/health-statistics.htm
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Limitations of Population Health Outcomes for Assessing Health Care Systems
Although population health outcome measures such as life expectancy , mortality rates , and others refl ect on a region’s health care system, it is also recognized that many factors outside the health care system affect these measures. For example, the difference in life expectancy and mortality rates between Whites and African Americans is thought to refl ect in part dif- ferences in standard of living as well as access to health services ( Geiger, 1996 ; Institute of Medicine [ IOM], 2003 ; Schwartz, Kofi e, Rivo, & Tuckson, 1990 ). As Levitt, Claxton, Cox, Gonzales, and Kamal (2014) summarize:
Complicating any assessment of health system performance is the problem of dis- tinguishing health system impacts from those that stem from other societal drivers. Many factors outside of the health system—such as poverty, diet, exercise, sub- stance use, environmental factors, and social policies—not only contribute directly to health status, but also affect access to medical care. Even when potentially infor- mative measures can be identifi ed, consistent trend data may not always be avail- able at the health system level for the U.S. and other countries.
Historically, more emphasis has been placed on cost than on health system out- comes. However, industry leaders and policymakers cannot make good judgments about spending without understanding how well the system is working for pa- tients. To put it simply, we need to better understand how much we are spending, what is driving growth, and what we are getting for what we spend. This means tracking how well the health system is performing at keeping people healthy and treating them when they get sick. Are we getting better health outcomes for our increased spending? If we are successful at reducing growth in spending, is there a sacrifi ce in terms of health? At the broadest level, we can think of a dual focus on health system spending and outcomes as assessing the effi ciency of the health system. ( Levitt et al., 2014, paras 4 and 5 )
Now we turn from population health outcomes to a discussion of clinical outcomes.
Clinical (or Healthcare-Associated) Outcomes Clinical outcomes (which will be used synonymously with healthcare-associated outcomes ) are the consequences of health care interventions that are specifi c to persons who have received care. Unlike the quality assessment based on population health outcomes , the clinical outcomes assessment begins with the provider–patient interaction and its subsequent clinical pro- cesses and outcomes. For example, clinical outcomes for pancreatic cancer treatment may be 5-year survival and 30-day hospital readmission. For hip replacement surgery, clinical outcomes may be the absence of long-term pain and discomfort and patient satisfaction with changes in mobility after the surgery.
Studies of clinical interventions and their outcomes are essential for evaluating and maintaining the quality of the health care system. Conducted with scientifi cally valid meth- ods, clinical outcomes studies indicate the quality of the health care received by patients— answering these questions:
• Was the health care intervention implemented correctly? (the process) • Was the health care intervention effective? That is, did it produce the desired or
intended results? (an outcome) • Did the health care intervention have adverse consequences for patients? (an outcome)
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Increasingly, the patient’s point of view about treatment outcomes contributes to the de- termination of success or failure—whether the intervention was effective and whether it had adverse consequences for patients. Health care providers and patients have been found to differ in their assessment. For example, a 2010 study of adverse events in hospitals found that “The patient’s concept of adverse events was different from that of the physician. ... Patients emphasized emotional consequences of the adverse events” ( Masso Guijarro, Aranaz, Mira, Perdiguero, & Aibar, 2010 , p. 144).
Clinical outcomes studies are the basis of evidence-based medicine , which has famously been defi ned in the British Medical Journal as:
Evidence-based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence-based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research. ( Sackett, Rosen- berg, Gray, Haynes, & Richardson, 1996 , p. 71)
Clinical outcomes studies ensure that scientifi cally valid, empirical research provides the basis for assessing the effectiveness, or quality, of health care at the microlevel of physician practices, hospitals, and other health care settings.
An example of a clinical outcomes study is Association Between Adverse Clinical Outcomes After Coronary Artery Bypass Grafting and Perioperative Blood Transfusions. The abstract is reprinted in Exhibit 7.1.
EXHIBIT 7.1 Association Between Adverse Clinical Outcomes After Coronary Artery Bypass Grafting and Perioperative Blood
Background: Bleeding is a serious complication of coronary artery bypass grafting that of- ten leads to blood transfusion. Approximately 50% of patients who have the surgery receive blood products, and blood transfusions play a role in adverse outcomes after the surgery.
Objective: To examine the association between perioperative blood transfusion and postoperative adverse outcomes in patients undergoing coronary artery bypass grafting.
Methods: A systematic review of the literature, via the matrix method of quality evalu- ation, was conducted. PubMed, CINAHL, and Science Direct databases for 2000 through 2016 were searched. Inclusion criteria were articles published in English and original research related to clinical outcomes of blood transfusion after coronary artery bypass …
HCM 340 Milestone One Guidelines and Rubric Overview: The focus of this module was on quality care. For your final project, you will research a gap in access to quality, equity, or efficiency of healthcare, (including existing initiatives in place to address the gap and economic and regulatory factors that are currently in place to address the gap). To begin this project, you must first conduct some background research into the gap and the impacted population. Prompt: After reviewing the Final Project Guidelines and Rubric document, select a healthcare delivery process gap and population affected from the list below. This will be the base of your final project, the healthcare delivery systems research paper.
• Option One: A gap in quality related to care coordination for individuals with chronic illnesses • Option Two: A gap in equity related to mental health access for veterans • Option Three: A gap in efficiency related to rising cost of pharmaceuticals for the aging population
In your paper, you will explain why the chosen topic is a gap in healthcare and analyze the specific population impacted by the issue. Specifically, you should address the following:
• Describe a specific gap in the delivery of healthcare. Include the specific population affected by the gap. • Briefly describe the history of this gap in access to healthcare. Has this been an issue historically, or is it a modern issue? • Explain the impact that the socioeconomic background of the population has on their access to healthcare. • Describe how the healthcare delivered to the population is affected by the gap in access. • Predict any potential implications if this gap in access is not addressed.
Guidelines for Submission: Your paper must be submitted as a 2- to 3-page Microsoft Word document with double spacing, 12-point Times New Roman font, one-inch margins, and at least three sources cited in APA format.
Critical Elements Proficient (100%) Needs Improvement (75%) Not Evident (0%) Value Introduction: Gap Describes a specific gap in the delivery
of healthcare, including the specific population affected by the gap
Describes a specific gap in the delivery of healthcare, including the specific population affected by the gap, but the description is cursory or contains inaccuracies
Does not describe a specific gap in the delivery of healthcare to a specific population
Introduction: History Briefly describes the history of this gap in access to healthcare
Describes the history of this gap in access to healthcare, but description is verbose or contains inaccuracies
Does not describe the history of this gap in access to healthcare
Introduction: Socioeconomic Background
Explains the impact socioeconomic background has on a population’s access to healthcare
Explains the impact socioeconomic background has on a population’s access to healthcare, but explanation is cursory or illogical
Does not explain the socioeconomic background has on a population’s access to healthcare
Introduction: Affect Describes how the healthcare delivered to the population is affected by the gap in access
Describes how the healthcare delivered to the population is affected by the gap in access, but description cursory or contains inaccuracies
Does not describe how the healthcare delivered to the population is affected by the gap in access
Introduction: Implications Predicts any potential implications if the gap in access is not addressed
Predicts any potential implications if the gap in access is not addressed, but predictions are unrealistic or unrelated
Does not predict any potential implications if the gap in access is not addressed
Articulation of Response Submission has no major errors related to citations, grammar, spelling, syntax, or organization
Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas
Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas
- HCM 340 Milestone One Guidelines and Rubric