Despite the constant development of the field of medical sciences for the sake of common good, hospital readmissions are a persistent issue in contemporary medical care. Of course, the overwhelming majority of the patients do not want to return to the hospitals, and, to boost the medical institutions’ measure-taking activities, Medicare practices the system of penalties for the unplanned readmissions. But even though medical institutions are struggling to prevent their patients from unplanned readmissions, the statistical data for patients experiencing readmissions is rather humbling. The problem of our current interest, that is, the prevention of readmission in geriatric patients is not any less pressing than other readmissions. In 2015, of the total 25,593 geriatric beneficiaries using the services of FFS Medicare, a mean 4.1% experienced at least one readmission within 30 days after the discharge (Gorina et al., 2015).
The problem and purpose of the study
The statistical data gathered from the geriatric patients of Fee-for-service Medicare organizations concerns readmissions and death rates among female and male patients aged 65 to 85 and over. However, it does not account for the presence or absence of post-discharge medical services in relation to the cases of readmission. Therefore, the issue we would like to explore is related to the subject of post-discharge medical care among geriatric patients who have experienced discharge and readmission. Thus, the purpose of this study is to estimate whether there is a relationship between the discharged and re-hospitalized patients’ receiving post-discharge medical care and the period between their discharge and readmission.
Research questions and hypothesis
The main question of our research is closely related to the problem we would like to study. To formulate the question, we have used the PICOT format where P stands for Population we are going to study, I for the Intervention, that is, the treatment received by the Population, C for Comparison of the group that does not get the Intervention, O for Outcome or the result of the Intervention, and T for Time period (Riva et al., 2012). Thus, the question of our research can be put as follows: in elderly patients who have experienced hospital readmission, how does rendering post-discharge medical services, compared to non-rendering the said services, influence the range of readmission dates within the period between their hospitalization and re-hospitalization? Is the period between the two longer in patients that have received post-discharge medical services? What services were included, if any, in the geriatric patients’ post-discharge plan (e.g., they were or were not provided with some information and medications upon the discharge, they were or were not given a follow-up treatment plan, they had or did not have an opportunity to be engaged into a Telehealth program, etc.)? Hypothetically, the correlation between the services and whether the patient is re-hospitalized can exist. Thus, we suppose that rendering the post-discharge services we have mentioned increases the period between the discharge and the readmission or does not result into a readmission at all. As a null hypothesis we can admit that the presence or absence of post-discharge services may not influence the date range between the initial hospitalization and readmission.
We have used the data on the elderly patients being re-hospitalized into FSS facilities presented by the copy of National Health Statistics Reports of September 2015 which was the most up-to-date data on the chosen subject at the moment. The data concerned whether the patients’ readmission was planned or unplanned by the time of discharge, the post-discharge services the patients have or have not received during their period of recovery, and the period of time between the patients’ first and second hospitalization. The exactitude of these variables can be checked for using the charts available in the NHSR copy (Gorina et al., 2015). We define a readmission as an unplanned one if was is not included in the plan, as identified in The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) that has collected information regarding re-hospitalization after surgical procedures (Sellers et al., 2013). We focus our study on the unplanned ones only. A readmission is considered short-period if a patient was re-hospitalized within 30 days after the discharge and long-period if the re-hospitalization occurred after more than one month and within a year after the discharge. As for the post-discharge services, of course, every hospital has its own policy in these cases, however, some basic rules and procedures are present everywhere. We define post-discharge medical services, or care, as an assessment plan that a patient is given when discharged. It can depend on the patient’s needs, e.g., the patient is provided with an individual plan considering what kind of services the patient will receive, with what frequency and by whom, what equipment and medications will be needed, etc. The patient can be transported home and it can be arranged that a professional nurse or the patient’s friend or relative will help him/her. Besides, the patient can use a Telehealth system to be assisted via Skype and suchlike.
The amount of literature attempting to make descriptions and evaluations of hospital readmission is on the increase. A significant part of the said body of literature tries to make prognoses and predict the possible risk factors. For that purpose, they overview existing discharge-to-readmission models and develop diverse solutions; we are dubbing these solutions “diverse” since the researchers whose works we have overviewed offer different opinions that sometimes contradict each other. At any rate, there are two possible causes of interest in such models. Firstly, there are researchers who stick to the opinion that interventions and changes in the course of treatment can substantially scale back the number of readmissions. Thus, they consider it useful to study potential risk factors in cohorts of adult and advanced-year patients with morbidities who have experienced re-hospitalization. The studies can be used forward to inform the patients of all possible outcomes in due time and decrease the very percentage of risk. From our point of view, such interventions should occur not post-discharge but considerably before it. Secondly, among other researchers, it is somewhat popular to use the readmission statistical data to measure the quality of services rendered in medical institutions. For this purpose, a calculation of standard risk and readmission rate is needed. For instance, The Centers for Medicare & Medicaid Services (CMS) are already deploying the rates publicly (Kansagara et al., 2011). Their models are able to derive exact predictions and utilize relevant data that is, at the same time, easy to access. They can be applied to large-scale samples and use field-related population-valid variables. Nevertheless, the usage of readmission rates to measure the service quality means that re-hospitalizations indicate the unsoundness and potential flaws in rendering the services. There are also researches that declare readmissions – or at least some of the readmissions – as being preventable, although this area is understudied. Considering that our very goal is to study whether by caring for the patients after their discharge can prevent readmissions of at least prolong the period between the hospitalization and re-hospitalization, we shall have to rely on the material that exists.
In their systematic review, Naylor et al. state the importance of lower cost of medical services (2011). In their work, they mention the Affordable Care Act of 2010, according to which various programs concerning discharged and transitioned patients were bound to enhance their standards and cut expenses. The programs also presupposed several types of post-discharge care for patients. The authors did a review of the existing literature on the subject and ended up with an extended summary of clinical cases for patients suffering chronical illnesses. The measure-taking activities deployed by medical institutions to reduce the readmission rates were demonstrated by several cases. Many common steps and measures, i.e., clinical management led by a professional nurse or home health care, were featured in the cases (Naylor et al., 2011).
Indeed, predictions and prognoses made in relation to readmissions are a key factor in identification of the types of patients for whom adjustments to treatment during transitions would prove beneficent. Another systematic review conducted by Kansagara et al. is aimed at deriving a summary of possible models that could predict the potential risk of readmissions (2011). The review can be of service in clinical and managerial conditions. The researchers summarize some models and discover the flaws and drawbacks in the majority of them; thus, they mark their performance as poor (Kansagara et al., 2011).
Among the works that are closer to our current subject, there is an article by García-Pérez et al., where the aging of the population is related to a growing number of hospitalizations (2011). It is yet another study that tries to single out the risk factors influencing re-hospitalizations but the population group is of geriatric persuasion, that is, persons aged 75 and over. In the study, the authors concede that a minor part of the readmission models is affected by the low level of income in the said persons. However, the patients experiencing acute illnesses, comorbidities, and disabilities were at the highest risk of readmission. Thus, it is concluded that there is a pressing necessity to focus on geriatric patients taking into account their previous hospitalizations, the length of stay in a medical institution, morbidities, and disabilities (García-Pérez et al., 2011).
Our study is focused on the preventive measures that can be taken in relation to readmissions. Thus, it appears justified to put a question whether they are at all preventable. The article by Joynt and Ashish is devoted to this problem only (2012). It was reported that re-hospitalizations were possible to prevent in less than a third of all cases, although, on the country scale, these figures vary. On the other hand, it was stated that despite the different numbers of all readmissions in different institutions, the variability of the percentage of preventable re-hospitalizations was nil. The authors assume this result can mark the low quality of medical care in hospitals. One might have an impression that the readmission rates can indicate whether this or that hospital renders good-quality service, but the authors state it is, surprisingly, a poor indicator. The reason to it can be that there are a plethora of other factors triggering readmission, e.g., mental disorders, lack of support from the society, family, and friends, and low level of income. These factors are often underestimates, which is yet another reason they can badly affect the accuracy of readmission rates as a quality indicator (Joynt & Ashish, 2012).
While the majority of the studies identifies “readmission” as “re-hospitalization within 30 days after discharge,” the study “Risk factors for hospital readmission of elderly patients” by Franchi et al. is aimed at identification of the factors that correlate with geriatrics’ re-hospitalization within 3 months’ time (2013). The overall percentage of those who have experienced readmission was 19. The results of the research suggest that there is a number of factors solidly associated with the risk of an unplanned re-hospitalization, such as previous hospitalizations, particular drugs taken in the course of treatment, comorbidities and acuteness, etc. It was estimated that the elderly patients suffering liver and circulation diseases were also part of the risk group (Franchi et al., 2013).
The study by DePalma et al. concerned elderly patients who returned home for recovery without their ADL (activities of daily living) needs having been met (2013). The authors made a hypothesis that the non-met needs in the geriatric patients could be directly linked to unplanned re-hospitalization. The authors defined an ADL disability as not being able to care for oneself without other people’s assistance. Thus, the ADL needs that have been met can be explained as the hospitals providing professional assistance (nurses, etc.) to the patients at home or informing their friends and relatives to arrange for daily in-house assistance. It was concluded that the unmet ADL needs could positively be related to unplanned re-hospitalization risk and that the geriatric patients’ needs ought to be assisted post-discharge (DePalma et al., 2013). The importance of adjusting to the patients’ needs was emphasized in another work that stressed the necessity to plan the discharge and post-discharge ahead (Shepperd et al., 2013).
In their work, Field et al. supposed that, if a discharged geriatric patients visited a physician within a week of his/her discharge, that would decrease the risk of readmission. However, despite all probability, the visit did not prove effective. The authors suggest that the visits should be adjusted to the patients’ individual needs and estimate that in-house visits would be more efficient (Field et al., 2015).
Another possible means of post-discharge care was studied by Smith et al.: the authors hypothesized that the automated medical help would help reduce errors when discharging patients. They studied a sample of patients before using the primary care physician (PCP) tools and after using it, respectively. They discovered that medical errors were on the decrease after the mechanized intervention was deployed. However, the errors were not clinically important. The more so, the application of PCP did not influence the readmission rates (Smith et al., 2015).
The most up-to-date study we have managed to find concerned the period of the patients’ hospitalization and the probability of readmission and the date range between the hospitalization and re-hospitalization. It was estimated that the shorter the period of stay, the higher the risk of readmission (Moran et al., 2016).
Having reviewed the modern literature on the subject of our interest, we can see that readmission rates do not measure the quality of medical care (Joynt & Ashish, 2012). We can also see that most of the models of prevention work poorly and it is doubted whether the readmissions can be at all predicted and prevented (Kansagara et al., 2011; Naylor et al., 2011). These theories support the idea that post-discharge services should be adjusted to the patients’ – especially geriatric patients’ – needs and, for that purpose, various activities can be deployed (Franchi et al., 2013; DePalma et al., 2013). The further theories are extensions of the concept of post-discharge activities that can reduce the readmission rates. One such activity could be an in-house visit by the health care team or nurse (Field et al., 2015). Another possible solution could be the application of mechanized health care, e.g., PCP and suchlike (Smith et al., 2015). The latter does not seem to work when discharging patients, but the impact of various computer-based treatment programs is not stated. Moreover, if the patients have the possibility to prolong the time of their stay, the presence and the period of readmission could be influenced (Moran et al., 2016). Thus, using the theoretical knowledge on post-discharge services and their potential implications on geriatric patients, we can put forward an idea that post-discharge medical services (such as follow-up plan, in-house visits by carers, Telehealth, etc.) can make a difference in geriatric patients’ readmission rates. We have hypothesized that the correlation between the services and whether the patient is re-hospitalized or not can exist. Thus, we propose that, by rendering such services, the readmissions in geriatric patients can be reduced, if not completely prevented.
To conduct a high-quality research on the subject, it is needed to operate with the basic ideas of sampling since inappropriate techniques used in research are likely to lead to inconsistent and one-sided samples. The most optimal choice of the strategy here is the systematic random sample since it ensures that every member of the population is chosen by an inclusion that provides a non-biased choice. Relying on the figures provided by the National Health Statistics Reports copy, we are going to account for geriatric patients of FSS Medicare organizations aged 65 and over. The total amount of the re-hospitalized patients is 1491 (Gorina et al., 2015). We will use the Researcher’s Toolkit to count the sample size. With the SD of 1, we set the α-error confidence at 5% and β-error at 20%. After the calculation, the desired sample size amounts to 471 (Researcher’s Toolkit, 2015). (Researcher’s Toolkit, 2015). Using a simple formula, it is possible to define a sample interval of systematic random sampling that does not require as significant resources as the initial number.
Where K is the sample interval. Thus, it is best to choose every third patient in the whole population.
We understand that we will not possibly be allowed to control the intervention in the treatment of the group that we have chosen as our sample, especially since the group is so vulnerable. That said, we choose to conduct a quasi-experimental research that will allow us to empirically estimate the possible effects of rendering post-discharge services to geriatric patients on the period (and actual presence) of their readmission and make a small yet significant contribution to science and the good of humanity.
DePalma, G., Xu, H., Covinsky, K. E., Craig, B. A., Stallard, E., Thomas J., & Sands, L. P. (2013). Hospital Readmission Among Older Adults Who Return Home with Unmet Need for ADL Disability. The Gerontologist, 53(3), 454-461.
Field, T. S., Ogarek, J., Garber, L., Reed, G., & Gurwitz J. H. (2015). Association of Early Post-Discharge Follow-Up by a Primary Care Physician and 30-Day Rehospitalization Among Older Adults. Journal of General Internal Medicine, 30(5), 565-571.
Franchi, C., Nobili, A., Mari, D., Tettamanti, M., Djade, C. D., Pasina, L.,… Mannucci, P. M. (2013). Risk factors for hospital readmission of elderly patients. European Journal of Internal Medicine, 24(1), 45-51.
García-Pérez, L., Linertová, R., Lorenzo-Riera, A., Vázquez-Díaz, J. R., Duque-González, B., Sarría-Santamera, A. (2011). Risk factors for hospital readmissions in elderly patients: a systematic review. An International Journal of Medicine, 104(8), 639-651.
Gorina, Y., Pratt, L. A., Kramarow, E. A., & Elgaddal, N. (2015). Hospitalization, Readmission, and Death Experience of Noninstitutionalized Medicare Fee-for-service Beneficiaries Aged 65 and Over. National Health Statistics Reports, 84, 1-24. Web.
Joynt, K. E., & Ashish, K. J. (2012). Thirty-Day Readmissions – Truth and Consequences. The New England Journal of Medicine, 366, 1366-1369.
Kansagara, D., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., & Kripalani, S. (2011). Risk Prediction Models for Hospital Readmission: A Systematic Review. The Journal of the American Medical Association, 306(15), 1688-1698.
Moran, V., Jacobs, R., & Mason, A. (2016). Variations in Performance of Mental Health Providers in the English NHS: An Analysis of the Relationship Between Readmission Rates and Length-of-Stay. Administration and Policy in Mental Health and Mental Health Services Research, 43(1), 1-13.
Naylor, M. D., Aiken, L. H., Kurtzman, E. T., Olds, D. M., & Hirschman, K. B. (2011). The Importance of Transitional Care in Achieving Health Reform. Health Affairs, 30(4), 746-754.
Researcher’s Toolkit. (2015). Web.
Riva, J. J., Malik, K. M.P., Burnie, S. J., Endicott, A. R., & Busse, J. W. (2012). What is your research question? An introduction to the PICOT format for clinicians. The Journal of the Canadian Chiropractic Association, 56(3), 167–171.
Sellers, M. M., Merkow, R. P., Halverson, A., Hinami, K., Kelz, R. R., Bentrem, D. J., & Bilimoria, K. Y. (2013). Validation of new readmission data in the American College of Surgeons National Surgical Quality Improvement Program. Journal of the American College of Surgeons, 216(3), 420-427.
Shepperd, S., Lannin, N. A., Clemson, L. M., McCluskey, A., Cameron, I. D., & Barras, S. L. (2013). Discharge planning from hospital to home. Web.
Smith, K. J., Handler, S. M., Kapoor, W. N., Martich, G. D., Reddy, V. K., & Clark, S. Automated Communication Tools and Computer-Based Medication Reconciliation to Decrease Hospital Discharge Medication Errors. Web.