Introduction
The American health system is complex, involving multiple players and policies to be followed in decision-making. Although the structures and mechanisms that make the system complex aim to promote quality, safe, affordable, and accessible care for all, it affects decision-making in individual health care facilities. The local, state, and even national facilities feel the impact. However, to ease decision-making and make impactful prioritization, these facilities can use evidence to understand some trends in U.S. healthcare. That will ensure that facilities have enough resources and are well prepared to address the current and future healthcare demands and challenges. In this report, the primary focus is to analyze data from three states to understand the current trends in care demand and areas that healthcare facilities need to prioritize. The analysis of the states’ data was acquired from the Health Cost and Utilization Project (HCUP) website. The State Inpatient Database’ (SID) database on this website documents discharges records from all community hospitals and health care facilities in multiple states in the U.S. The SID files comprise data of all patients and provide a unique view of the hospital inpatient care in states over time. The database is developed through the industry-state-federal partnership and supported by the Agency for Healthcare Research and Quality (AHRQ). A proper analysis of this database informs hospital decision-making at the community, state, and national levels.
The HCUP’s SID data for Alaska, Kansas, and Iowa were analyzed to give an insight into the current and possible future healthcare demands in the three states. The results from the analysis can be used beyond the three states because they significantly represent the demands nationally. The analysis will major on aspects such as age, sex, common disease and condition (DRG_NoPOA), months, DISPUNIFORM (transfers and discharges), mortality, hours of the day, and the trends over the years. Although the researchers do not represent all the factors that should be considered by hospitals when making decisions and priorities, the covered factors are central influencers of decision-making in all clinical settings, and thus, the findings are insightful. However, further research is warranted.
Background and Significance
Before modernization and civilization, the U.S. at large depended solely on the traditional healthcare system, which applies beliefs, knowledge, and practices incorporating spiritual therapies, exercises, manual techniques, and animal, mineral and plant-based medicines (Umar et al., 2021). The healthcare system was deemed ineffective in sustaining human well-being, leading to the evolution of the modern healthcare system, which uses modern medicine, technology, and research. Therefore, modern healthcare facilities are central pillars for the health of over 320 million U.S. population (United States Census Bureau, n.d.). In reference to the America Hospital Association, the U.S has 6,083 hospitals. Among these, 1,796 are classified as rural community hospitals, and 3,343 are urban community hospitals. The hospitals are classified as community hospitals, federal hospitals, non-federal psychiatric care, and non-federal long-term care. The community hospitals and federal government hospitals greatly rely on the federal government for resources to learn its operations. The two types of non-federal hospitals do not get funding from the federal government but can be funded by local governments or private entities. Despite the mode of funding, all these hospitals share common roles, saving lives and improving the well-being of Americans. Such roles could not be achieved without proper decision-making, planning, and prioritizing.
Prioritizing is critical in the healthcare system and individual hospitals because it helps clinicians to take care of patients in the most beneficial way (Swarnakar et al., 2022). Besides, through prioritizing, clinicians are able to keep all clients safe, healthy, and alive. In a hospital with an effective prioritization framework, all available time is well planned for, and the staff understands what and who requires the most time and resources, as well as what must be done first, depending on the needs (Swarnakar et al., 2022). Through prioritizing in healthcare, the U.S. moves close to achieving major elements of the American dream, such as equality in healthcare, liberty, and access to quality and safe healthcare, because prioritizing is the foundation for equity and fairness in care delivery. Lastly, through prioritizing, hospitals will understand where to focus their resources, such as time, technology, and funding, depending on the current and projected future demands. With such a plan, there will be minimal wastage, and the U.S. health care system will be less expensive and hence affordable. A 2021 study showed that approximately 25% or between $760 billion and $935 of the total 3.6 trillion allocated for health care in that year went to waste (Fairley, 2021). Research shows that the waste mostly arises from unnecessary services (approximately $200 billion), excessive administrative costs (approximately $190 billion), inefficient care delivery ($130 billion), and missed prevention opportunities ($55 billion) (Fairley, 2021). Therefore, with a working plan that focuses on both the current and the future, the country will avoid unnecessary services, cut the administrative cost, offer efficient care led by current evidence and effectively use all prevention opportunities to avoid recurrent costs.
Methods
The healthcare Cost and Utilization Project (HCUP) is made up of software, reports, databases, and several other tools healthcare professionals, researchers, and administrators can use to learn patterns in healthcare. The State Inpatient Database (SID), which will be of the greatest importance for this analysis, is sponsored by the Agency of Healthcare Research and Quality (AHRQ) and collects together data from states and hospitals. The database encompasses “more than 95 percent of all U.S. hospital discharges” (HCUP, 2022). SID offers documentation for restrictions, resources, and specifications critical for professionals and researchers in healthcare. Therefore, considering the scope of this analysis, the SID database will be used to locate multiple sets of data for analysis. Other HSUP databases that will be referenced include the Nationwide Readmission Database (NRD) and the National Inpatient Samples (NIS). However, SID will be the main source of data.
The data from the SID database and largely the entire HCUP can be depended on as safe and authentic. HCUP users are offered a fifteen-minute training on data use agreement (DUA), which covers the benefits of data protection, possible violations on the website, and individual responsibilities on the website and using the HCUP data. Besides, the databases are “read-only,” and thus, only the HCUP can add, change or modify data in their databases. All files available on the HCUP website are original files submitted to the HCUP by its partner organizations. The information included in the files includes the source, records excluded, type of hospitals entailed in the files, and all other information that may help to understand the files’ composition. Therefore, all data on the website is a representation of partner organizations’ submissions.
In the development of this analysis, the states of Alaska, Kansas, and Iowa are important. The analysis uses data from these three states to learn patterns and trends in the U.S. healthcare system across different groups and over the years. The analysis emphasizes datasets such as age, sex, common disease and condition (DRG_NoPOA), days with highest admissions, DISPUNIFORM (transfers and discharges), mortality, hours of the day, and the trends over the years. These datasets are used in the framing of the research question, which seeks to identify the different areas where the hospitals in the U.S. should focus on today and in the near future. The analysis aims at exploring areas of priority in the U.S. health care system to avoid resource wastage, the severity of chronic diseases, and the high mortality rate. The research question is viewed as important considering the shock the country experienced during the coronavirus pandemic. The U.S. registered the most cases of mortality and severe morbidity from the pandemic. Largely, the health care system was less prepared for a pandemic, and the hospitals were less focused on the most vulnerable groups and the health conditions that are the greatest threat to health in the country. Despite the large budget allocated for health in the U.S., due to the lack of an effective prioritization plan, many hospitals do not allocate enough resources for services that are integral to modern and future healthcare demands.
Therefore, to address the question, this research will examine data from the various variable of interest. The analysis uses the SID files downloaded from the Summary statistics for All Sates Across All years data elements tab. The tab allows the researcher to select a state, database, and data year of interest. The Core summary statistics file displayed after searching gives an inpatient summary of the searched state. Therefore, these files are downloaded and examined for data to use in this analysis.For example, the researcher will examine the ages most represented in the hospital inpatient records and the mortality rate across the age groups in the three states.
Results
To start the analysis, the research obtained the age distribution frequency of the patients admitted to inpatient hospitals in the three states. The data helps to understand the ages that represent the greatest population of the patients admitted to hospitals in the three states. The data represented in the tables in this section represent the 2020 records, except in the table showing distribution for years.
Frequency Distribution for Age (top 4 ages with high admission rates)
| Age Bracket | Alaska | Kansas | Iowa |
| Less than 1 | 8412 | 34996 | 37917 |
| 60 – 70 | 831 | 5095 | 4712 |
| 23 -33 | 802 | 3456 | 3643 |
| 14 -24 | 534 | 1895 | 1857 |
Table 1.0: Age brackets with high demand for inpatient care
Based on this frequency distribution table, four things are clear. First, children who are below one year are the greatest consumer of inpatient services in the three states. In Alaska, Kansas, and Iowa, this population composed 15.02%, 11.65%, and 12.57%, respectively, of the total inpatient population in 2020. The second evident point from this table is that persons between the age of 60 and 70 are the second greatest consumer of inpatient services in the three states. At this age, individuals start experiencing the health challenges associated with elderliness, such as decreasing immunity, body imbalance, and weakening body muscles. By age 70, most persons in this age bracket understand their health status and thus seek outpatient care more than in-patient care. The third and fourth point is that persons between the age of 23 and 33 and between 14 and 24 are the third and fourth greatest consumers of inpatient services in the three states, respectively.
Frequency Distribution by AHOUR.
The data in this distribution shows the hours when the demand for inpatient care is highest in inpatient hospitals. The tables below show the hours of the day when more patients are admitted to the hospitals and the number of patients admitted in the three states.
Table 2.1: AHOUR frequency distribution in Alaska
| Hour | Frequency |
| 1200 | 3065 |
| 1400 | 320 |
| 1500 | 3385 |
| 1600 | 3292 |
Table 2.2; AHOUR frequency distribution in Kansas
| Hour | Frequency |
| 1300 | 16970 |
| 1400 | 18553 |
| 1500 | 18841 |
| 1600 | 17849 |
AHOUR table for Iowa is not available in the 2020 report file. Based on the two tables, it is clear that the demand for inpatient care is increasing from 1300 hr to 1500hr. After the 1500 hr, the demand decreases progressively but is still high in the 1600 hr.
Frequency Distribution for AMONTH
The data in these tables shows the four months when the demand for inpatient services is highest in the four states.
| Month | Frequency |
| January | 5208 |
| October | 5044 |
| December | 4853 |
| August | 4727 |
Table 3.1: Months with the highest frequency distribution of inpatient admission in Alaska
| Month | Frequency |
| January | 28039 |
| February | 26326 |
| July | 26126 |
| August | 25934 |
Table 3.2: Months with the highest frequency distribution of inpatient admission in Kansas
| Month | Frequency |
| January | 29005 |
| October | 26551 |
| February | 25986 |
| November | 25495 |
Table 3.3: Months with the highest frequency distribution of inpatient admission in Iowa
Based on the three tables, the demand for inpatient services in 2020 was high in January in all three states, August in Alaska and Kansas, February in Kansas and Iowa, and October in Alaska and Iowa. Other months when the demand is high in individual states are December, July, and November.
Frequency Distribution for AWEEKEND
The data in this section compared the demand for inpatient services on weekdays and weekends in the three states. Weekdays are from Monday to Friday, and the weekend is from Saturday to Sunday.
| State | Weekday frequency | Weekend Frequency |
| Alaska | 44100 | 11921 |
| Kansas | 240614 | 56528 |
| Iowa | 241407 | 60174 |
Table 4.0: Demand for inpatient services on weekdays and weekends
Based on this table, the demand is high over the weekday and low over the weekend in all three states.
Frequency Distribution for Years
The data in this table shows the demand for inpatient services trends across the three states from 1950 to today. The table records the data of the demand in years set apart by ten years, which is one decade.
| Year | Alaska | Kansas | Iowa |
| 1950 | 776 | 4933 | 4761 |
| 1960 | 767 | 4317 | 4127 |
| 1970 | 461 | 2626 | 2467 |
| 1980 | 573 | 2333 | 2210 |
| 1990 | 885 | 3740 | 4056 |
| 2000 | 452 | 1961 | 1988 |
| 2010 | 110 | 307 | 298 |
| 2020 | 7936 | 33783 | 36397 |
Table 5.0: demand for inpatient services since 1950
Generally, in the three states, the demand for inpatient care decreased progressively from 1950 to 1980 and rose in 1990 before decreasing again until 2010. The year 2020 registered the highest number of inpatient admissions since 1950 due to the COVID-19 pandemic.
In terms of sex, in 2020, Alaska inpatient hospitals admitted more females (31003) than men (25009), similar to Kansas (169954 females and 130318) males, and Iowa (167369 females and 13189 males). That means more females in all three states are admitted to inpatient hospitals in the three states, than males. The DISPUNIFORM (transfers and discharges) data from the three states also shows that most patients in inpatient care are discharged to home or self-care, while others are transferred to short-term hospitals.
Strengths and Limitations
One of the strengths of the HCUP dataset lies in its inclusivity and size. The dataset has the database for individual countries documented in an individual file and thus making it easy to use. For each state, data is gathered from hospitals within the state and thus represents the entire population within the state. Overall, HCUP has data files for almost 48 states which include outpatient and inpatient data. Another key strength of the dataset is uniformity. For all the NIS files used SID files used for this analysis, the files for the three states, data is uniformly organized, making it easy to locate and compare data. Also, the data is current and is the original submission from organizations that partner with HCUP.
Nevertheless, some details are unavailable, making the dataset slightly unreliable. For example, there is no data on the demand for inpatient services per hour in a day for Iowa State. Also, not all the states documented in the dataset report their data using details such as counties, patient zip code, illnesses, and ethnicity. That made it impossible to use data from some states.
Discussion
Using the findings made in this analysis, inpatient hospitals and the larger U.S. healthcare system can make a prioritization plan to ensure that it directs resources where they are needed most, and services are available to those who need them most. For example, the age distribution table indicates a high demand for care by infants below one year and individuals between ages 60 and 70. Therefore, more resources must be dedicated to intentions and research targeting these two populations. The data on demand for inpatient services based on hours and days shows a high demand from 1300hr and 1600 hr, and on weekdays. Therefore, more staff, technology, and funds should be availed for use during this time, especially in the months of January, February, July, August, September, October, November, and December, when the demand for inpatient care is very high.
Using the AWEEKEND table, the data shows a high demand for inpatient care over the weekdays than weekends in the three states. There are five weekdays in a week and just two days on the weekend. The high frequency over the weekday covers the entire five days, and the frequency for the weekend covers the two days. The mean for the weekday and weekends show a high demand for the inpatient services over the weekdays than on weekends. For example, in Kansas, which registers the highest demand over the weekdays (240614), the mean or average demand for care on a single day of the five weekdays is 44122, which is significantly higher than a single average admission rate in a single day over the weekend which is 30087. Based on the demand for inpatient services over the years, the demand has been in a decreasing mode since 2000 but sharply increased to the highest records ever registered in the three states due to the COVID-19 pandemic 2020. As a result of the increase in 2020, many inpatient hospitals were overwhelmed and could not support the high demand because they were less prepared for such high demand. It resulted in massive loss of lives and high morbidity. Therefore, the U.S. healthcare system, as well as individual hospitals, should always consider the likelihood of an unprecedented pandemic or epidemic emerging in the future and plan for it.
The data on admission in terms of sex reveals a high admission rate for females compared to their counterparts, males. Evidence shows that females are more likely to seek healthcare services than males (Sharma et al., 2020), and this could be the cause of the high female admission than men. Therefore, although the margins are slightly insignificant, inpatient hospitals should focus slightly more resources on female patients’ admission. Allocating equal resources for the two sexes could result in wastage (from the males’ side) and resource-shortage (from the females’ side. However, they should be keen to ensure that their prioritization plan does not result in gender inequality in the delivery of care. Lastly, the data from the DIS database emphasize the need for cooperation between inpatient hospitals and home or self-care and short-term hospitals. Such cooperation will streamline the discharge and transfer operations because most patients from inpatient care are either discharged to home or self-care or transferred to short-term hospitals.
Conclusion
Proper planning and prioritizing are central to maximum use of the available resources for optimal benefits. Health care services for an integral part of the U.S. as a society, economy, and jurisdiction. A healthy nation is able to work towards its economic and political goals with greater chances of success. However, such a nation may not be achieved when the country and its individual healthcare facilities do not understand where to focus most attention. Therefore, based on this research, the U.S. health system can avoid resource wastage and artificial shortage by focusing more on resources, where and to whom to allocate, and when it’s needed most. The research also reveals potential trends in demand for inpatient services in the future, and thus inpatient hospitals can use the findings to prepare for the projected and unprecedented future.
References
Fairley, M. C. Z. (2021). Data-Driven Analytics for Clinical Decision Making, Healthcare Operations Management and Public Health Policy. Stanford University.
Healthcare Cost and Utilization Project. (n.d.) Hcup overview course—Accessible version. Retrieved July 23, 2022, from https://www.hcupus.ahrq.gov/HCUP_Overview/HCUP_Overview/index508_2019.jsp#NIS1
Healthcare Cost and Utilization Project. (2022). Summary statistics for all states all years. Retrieved July 23, 2022, from https://www.hcup-us.ahrq.gov/db/state/siddbdocumentation.jsp
Sharma, G., Volgman, A. S., & Michos, E. D. (2020). Sex differences in mortality from COVID-19 pandemic: Are men vulnerable and women protected? Case Reports, 2(9), 1407-1410. https://www.jacc.org/doi/full/10.1016/j.jaccas.2020.04.027
Swarnakar, V., Bagherian, A., & Singh, A. R. (2022). Prioritization of critical success factors for sustainable lean six sigma implementation in Indian healthcare organizations using best-worst-method. The TQM Journal. https://doi.org/10.1108/TQM-07-2021-0199
Umar, U., Khan, M. A., Irfan, R., & Ahmad, J. (2021, July). IoT-based cardiac healthcare system for ubiquitous healthcare service. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/abstract/document/9493478
