Can a Patient Be Discharged and Then Admitted Again Same Day

Soc Work Health Care. Writer manuscript; available in PMC 2016 Jan 29.

Published in final edited form every bit:

PMCID: PMC4731880

NIHMSID: NIHMS688641

Why practise patients keep coming back? Results of a Readmitted Patient Survey

Holly C. Felix, PhD, MPA, Associate Professor of Health Policy, Beverly Seaberg, RN, BSN, Quality Assurance Coordinator, Zoran Bursac, PhD, MPH, Acquaintance Professor of Biostatistics, Jeff Thostenson, MS, Biostatistician, and M. Kathryn Stewart, MD, MPH, Professor of Health Policy

Holly C. Felix

Fay W. Boozman Higher of Public Wellness, Academy of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 820-12, Petty Rock, AR 72205, ude.smau@yllohxilef / 501-526-6626 / 501-526-6620 fax

Beverly Seaberg

Academy Hospital, University of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 572, Piffling Stone, AR 72205, ude.smau@grebaesab / 501-686-6703

Zoran Bursac

Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 820, Picayune Stone, AR 72205, ude.smau@narozcasrub 501-526-6723

Jeff Thostenson

Fay West. Boozman Higher of Public Health, Academy of Arkansas for Medical Sciences, 4301 Westward Markham Street, Slot 820, Trivial Rock, AR 72205, ude.smau@nosnetsohtdj / 501-526-6727

G. Kathryn Stewart

Fay Westward. Boozman College of Public Wellness, University of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 820-12, Little Rock, AR 72205, ude.smau@kyramtrawets / 501-526-6625

Abstract

Hospital readmissions can negatively touch on toll and patient outcomes. Predictors of 30-24-hour interval readmissions have been primarily identified using medical claims data. Reported here are results of a patient survey developed as part of regular hospital quality assurance activities. Two-thirds of patients reported good discharge experiences but were still readmitted. One-third of patients discharged had a post-discharge medico appointment scheduled; half were readmitted before that scheduled date. Results propose post-discharge experiences could be improved, specially the timing of follow upward doctor appointments. Identified weaknesses in the survey process highlight need for date of survey methodologists in efforts to sympathise patient experiences.

Keywords: Hospital readmissions, Patients discharge, Survey research

Introduction

In the mid-1980s, infirmary 30-twenty-four hour period readmission rates were >20% (Anderson & Steinberg, 1984), and similar rates keep today (Jencks, Williams, & Coleman, 2009; (MEDPAC, 2007). These readmissions are of concern because of their impact on cost and patient outcomes. The circumstances surrounding hospital readmissions are not fully known; poor intendance coordination after discharge (MEDPAC, 2007) and poor follow-up care (Hernandez et al., 2010; Jencks, Williams, & Coleman, 2009)(Jencks, Williams, & Coleman, 2009) are considered 2 primary factors.

Other patient and clinical factors that predict readmission have been identified by research (Bohannon & Maljanian, 2003; Jasti, Mortensen, Obrosky, Kapoor, & Fine, 2008; Krumholz et al., 2000). For example, older adults compared to younger adults (Robinson, Howie-Esquivel, & Vlahov, 2012; Wier, Barrett, Steiner, & Jiang, 2011); females compared to males (Robinson et al., 2012), patients of lower socio-economic status (SES) compared to those with college SES (Jasti et al., 2008; Wier et al., 2011), and those who have shorter hospital stays compared to longer stays (Carey & Lin, 2014) have all been shown through research to elevate gamble for a readmission shortly later on a hospital discharge.

Although a large body of inquiry has developed and tested a diverseness of interventions targeting "root causes" of xxx-day infirmary readmissions (Boutwell & Hwu, 2009; Coleman, Parry, Chalmers, & Min, 2006; Jack et al., 2009; Naylor et al., 1999), national rates averaged xviii.4% (Gerhardt et al., 2013) in 2012, with Arkansas hospitals experiencing higher 30-day readmission rates than the nation as a whole (Rau, 2012). These readmissions result in an excess healthcare expenditure of $12 billion to $17 billion annually (MEDPAC, 2007), and put patients at risk for infirmary-acquired infections, medical errors, and overall deconditioning (Jahnigen, Hannon, Laxson, & LaForce, 1982). The federal authorities has implemented several programs (public reporting of thirty-twenty-four hour period hospital readmission rates and penalties for higher than average 30-mean solar day readmission rates for certain conditions) to help incentivize hospitals to reduce their readmissions rates. Additionally, federal resources have been allocated to support Community-based Care Transitions Programs, through which community-based organizations and hospitals interact to smooth transitions of patients back to their habitation (or other community settings) to minimize the risk of readmission (CMS, no date). However, in 2012, more than 1400 hospitals failed to reduce their readmission rates to prescribed levels, resulting in more than $280 million in penalties (Rau, 2013).

The persistently high 30-24-hour interval readmission rates amid many US hospitals, and the societal and patient impacts, highlight the demand for continued report to better empathise factors which affect rapid patient readmissions. Much of the previous research in this area has relied on analysis of medical claims data. In contrast, this study obtained and analyzed existing readmitted patient survey data collected for administrative purposes, with survey responses reported herein.

Methods

This observational study made use of existing authoritative data [hereafter referred to equally the survey dataset] to describe the experience of readmitted patients betwixt the initial hospitalization and the re-hospitalization within 30 days and to explore the associations between patient demographic and clinical characteristics and patient post-belch experience.

Data Source

After authors obtained study approval from the [Institution Proper noun] Institutional Review Board in 2013 (described below), they obtained the survey dataset from the [Institution Name] Academy Medical Centre Quality Assurance (QA) staff. The express dataset excluded directly patient identifiers only contained patient characteristics [eastward.g., age, gender] and some protected wellness data [e.thou., admission dates] on completers and non-completers, and survey responses of completers) in guild to complete the assay described herein.

Setting and Routine Quality Assurance Activities

The [Institution Name] University Medical Heart is a teaching infirmary and a Level one trauma center based in [Name of Urban center, State], a moderately-sized capital city of a rural Southern state. The Academy Medical Middle has 437 patient beds, and, according to federal Hospital Compare website (www.medicare.hospitalcompare.gov) from 2009 to 2012, had a 30-day hospital readmission rate of 18.3% for patients with acute myocardial infractions, 23.0% for patients with congestive heart failure, and 17.six% for patients with pneumonia.

As office of its on-going QA activities and internal efforts to address high readmission rate rates, University Medical Eye QA staff developed a survey to obtain information from patients who had returned to the infirmary within 30 days of a discharge. The survey instrument they developed consisted of three sections. Sections 1 and 2 sought detail on the patient, the index access and the readmission extracted from the existing medical record by QA or discharge planning staff. Section 3 independent questions which sought item on patients' belch and post-discharge feel, including questions request almost specific factors that have been shown to be associated with readmissions (east.thou., poor intendance coordination later discharge (MEDPAC, 2007) and poor follow upwardly care (Hernandez et al., 2010; Jencks et al., 2009)). Questions were asked by and patient responses were recorded by the staff administering the surveys.

From July to Dec 2012, QA staff identified 1061 patients who had been readmitted to the University Medical Center inside 30 days of a previous hospitalization using daily automated searches of electronic medical records. Staff extracted existing data from the medical records on the identified patients, and then attempted, in-person, to invite all identified patients to consummate the survey. If patients were reached, invited, and agreed to participate, staff asked patients the questions and recorded the answers on the survey course. Staff made several attempts to reach patients who on initial tries were non in their rooms or available to discuss the survey.

Written report Sample

The analysis of the survey dataset was restricted to adult (historic period ≥ 18 years) patients readmitted between July and December 2012 to the Academy Medical Center within thirty days of a discharge. Non-adults (age < 18 years) were excluded as factors associated with their readmission are likely to be different than those of adults. Those whose ages were missing eliminated ability to verify developed status were and also excluded from the analysis. Readmission was defined as readmissions inside 30 days of a hospital discharge as federal initiatives are defining readmissions in this mode (CMS, no date). Patients with more than 30 days between the two admissions and those patients for whom data were not available to calculate the days between their admissions were excluded from the assay.

Measures

The study used many patient and clinical characteristics from Sections 1 and 2 of the survey (data obtained by staff from the medical record) and patient responses to survey questions from Section iii on discharge and post-discharge feel. These are listed in Tables two.

Table ii

Patient Discharge and Post Discharge Experience

	n	%
Before last discharge, patient …
Agreed on clear discharge goals	401	75.vii
Got readable belch care plans	412	77.seven
Later final belch, patient…
Understood cocky-care	418	78.9
Understood Rx	422	79.six
Able to become Rx filled	393	74.2
Knew when to contact DR	423	79.8
Able to discuss concerns with DR/Hospital	352	66.4
Had transport for follow up appointment	425	80.2
Saw DR betwixt index and readmit	167	31.3
Had change in Rx	41	vii.7

Several variables (length of stay, days betwixt admissions, high/depression length of stay, loftier/low SES, and elderly/non-elderly) were calculated from data available in the survey dataset. Length of stay of the initial hospitalization was calculated by counting the days between the engagement of the initial access and the appointment of the initial discharge. Those with lengths of stay greater than the average length of stay (vi.4 days) were considered to accept a high length of stay. Days betwixt admissions was calculated by counting the days between the date of initial discharge and date of the readmission. The SES variable was constructed using insurance status, with low SES defined every bit having Medicaid just, being dually eligible for Medicaid and Medicare, or having no insurance (self-pay) (Wier et al., 2011). Elderly was defined every bit age ≥ 65 years old. This age was used as a cut point as federal initiatives targeting infirmary readmissions are focusing on Medicare beneficiaries, who become eligible for Medicare primarily at age 65 (CMS, no date).

Analysis

Descriptive statistics (due east.g., frequency, mean, and standard difference) were used to characterize the readmitted patients and summarize their experience at discharge and between the initial hospitalization and their readmission.

Bivariate assay using logistic regression was used to compare each patient demographic and clinical feature with each patient discharge and post-discharge experience (note: all belch / mail-belch experience survey questions were dichotomous – presence or absence of the feel). Multivariate logistic regression was used to explore the association between each patient demographic and clinical characteristics and patient discharge and post-discharge experience, later controlling for the other characteristics (east.g. all four variables were entered into a single model for each discharge and postal service-belch feel).

Patient characteristics considered in the bivariate and multivariate models included elderly, male, and low SES. The clinical characteristic included in the models was long-stay during the alphabetize hospitalization. These were selected for inclusion as previous research (discussed in the introduction section) has shown that these characteristics are associated with hospital readmissions.

The alpha level for significance was prepare at 0.05. All statistical analysis was conducted using Stata v12 (StataCorp LP, College Station, TX).

Narrative responses fabricated to the i open-ended question were coded and analyzed with content assay. Common themes were then identified.

Study Review

The original readmitted patient survey information collection try was part of regular hospital QA activities, and, as such, did not crave review as research involving man subjects. Infirmary staff were not required to obtain consent from patients invited to consummate the readmitted patient survey. However, the study reported on in this paper aimed to generate and share new cognition about patients' discharge and post-discharge experiences which could touch on 30-twenty-four hour period hospital readmissions. Every bit such, the study was considered research, and a protocol for the assay of that existing survey dataset was submitted to the Institutional Review Lath of the [Proper name OF INSTITUTE], which approved information technology nether expedited procedures. A waiver of HIPAA authorization was too granted.

Results

From July to December 2012, infirmary staff attempted to invite all identified readmitted patients (north=1061) to complete the survey. At that place were 587 patients who completed the survey, for an overall response rate of 55.3%. Of the remaining 474 patients who did not complete the survey, 468 (44.1% of 1061) patients were unavailable at the time staff attempted to invite their participation in the survey (e.grand. out of room for a medical process) and half-dozen (0.1% of 1061) refused to complete the survey. Therefore, the cooperation rate – the proportion of those invited who completed the survey – was 98.ix%.

50-7 participants were excluded from the analysis equally they did non meet the study eligibility criteria or eligibility could not be determined (east.k., due to missing information). The concluding study cohort included 530 patients. Figure 1 shows the period of patients from identification through inclusion in the assay reported herein.

Written report Participant Selection Flow

Significant differences in demographic characteristics of survey completers and survey non-completers were observed. Completers were significantly younger and of lower SES status. There were no differences observed between the length of stay during their index admission, the number of days betwixt the admissions, or gender (data not shown). Although differences were observed on some characteristics, the survey sample analyzed and reported on herein is considered a convenient one, and the respondents are non representative of all readmitted patients.

Respondents Characteristics

Table i provides descriptive information regarding survey respondents. Respondents were on average 52.9±17.0 years of historic period, with 25.three% of respondents beingness 65 years or older. Gender and depression SES were nearly evenly distributed among respondents (48.5% male person, 49.half-dozen% low SES).

Table 1

Readmitted Patient Survey Respondent Characteristics

	Mean	SD
Historic period, in years	52.9	17.0
Alphabetize length of stay, in days	6.iv	vii.3
Days betwixt index admission and readmission	12.2	8.0
	Number	%
Male	257	48.five
65 years or older	134	25.iii
Alphabetize admission length of stay > 6.iv days	173	32.vi
Depression SES	193	36.4
Patient discharged from Index access to…
Dwelling house without HHS	341	64.three
Home with HHS	154	29.1
Facility	25	iv.seven
Street / Unknown Place	ten	1.9
Patient readmitted from…
Abode	457	86.2
Facility	36	6.8
Street / unknown Identify	23	4.iii
Clinic	fourteen	2.6

The average length of stay during the index admission among survey respondents was vi.four ±7.3 days, with 32.six% of respondents having a long-stay (stay longer than average length of stay of 6.4 days). Nearly all of the responding patients were discharged from their alphabetize admission to home, with 64.3% of all discharges being home without home health services and 29.2% of all discharges being dwelling house with home health services. Most respondents (86.2%) were besides readmitted from dwelling.

Patients' discharge and post-discharge experiences

Overall, most readmitted patients surveyed reported a relatively positive discharge procedure, with approximately three-quarters indicating they had agreed on belch goals (75.7%) and received a readable intendance programme at discharge (77.7%). Nearly four out of every v responding patients indicated they understood how to care for themselves once discharged, understood how to take prescribed medications, were able to get their prescriptions filled, knew when to contact a health care provider if their status declined, and had transportation to get to a follow-up doctor's appointment. Two-thirds of patients reported that they had been able to discuss concerns with their md's role or the hospital. Few (7.7%) reported they had whatsoever changes in their prescriptions afterward the offset hospitalization. See Table 2.

The survey database included data extracted from the medical tape, which indicated that 38.0% of patients had had a follow up doc'south appointment scheduled prior to their discharge from the alphabetize hospitalization. When directly asked on the survey if they had seen a physician between the two hospitalizations, most one-third (31.1%) reported that they had. Survey data extracted from the medical record also provided the actual date of the follow up physician date for 188 of the 530 responding patients. For those patients with recorded follow-up appointment dates, there was on average 14.5 days between the date of belch and the follow-up physician's engagement date. The average number of days betwixt the belch from the index admission and the readmission was 12.2 ±7.9 days. The readmission engagement came earlier the follow-upward dr.'s appointment date in well-nigh half of the cases (48.4%).

Associations with discharge and post-discharge experiences

A series of bivariate and multivariate logistics regression models were run to explore associations between selected patient (age grouping, gender, SES) and clinical (length of stay during index access) characteristics, individual and so equally a group, and each of the seven discharge and post-discharge experiences. Run across Table 3. The bivariate models revealed that 3 characteristics were associated with simply two of the discharge and mail-discharge experiences. Males were significantly less likely to empathize their self-intendance compared to females (p=0.04). Both those with low SES compared to those with high SES and elderly patients compared to non-elderly patients were significantly less likely to be able to get their prescriptions filled (p=0.002 and p=0.05, respectively).

Table 3

Associations between Discharge and Mail-Discharge Feel and Select Patient and Clinical Characteristics

	Individual Models			Full Models				Individual Models			Full Models
	OR	SE	P	OR	SE	P		OR	SE	P	OR	SE	P
	Understood self-care							Was able to discuss concerns with doctors
Depression SES	0.74	0.16	0.17	0.68	0.17	0.12	Low SES	0.83	0.xvi	0.32	0.78	0.16	0.24
Male	0.64	0.14	0.04	0.63	0.14	0.03	Male	0.77	0.14	0.16	0.76	0.fourteen	0.14
Elderberry	1.02	0.25	0.94	0.82	0.23	0.47	Elder	1.00	0.21	one.00	0.87	0.twenty	0.56
Long LOS	one.03	0.23	0.xc	1.04	0.24	0.85	Long LOS	i.thirteen	0.22	0.54	1.14	0.23	0.51
	Understood medication requirements							Had transportation
Low SES	0.76	0.17	0.20	0.67	0.17	0.ten	Low SES	0.75	0.17	0.xix	0.71	0.18	0.16
Male	0.84	0.18	0.43	0.83	0.18	0.38	Male	0.68	0.15	0.08	0.67	0.fifteen	0.07
Elderberry	0.90	0.22	0.67	0.73	0.20	0.26	Elder	one.04	0.26	0.89	0.85	0.24	0.56
Long LOS	1.07	0.25	0.77	0.09	0.25	0.72	Long LOS	0.86	0.20	0.53	0.88	0.twenty	0.56
	Was able to fill up prescriptions							Saw physician between hospitalizations
Low SES	0.54	0.eleven	0.002	0.57	0.13	0.01	Low SES	0.97	0.xix	0.87	1.09	0.24	0.68
Male	0.71	0.fourteen	0.09	0.71	0.14	0.10	Male	0.76	0.14	0.fourteen	0.77	0.15	0.17
Elder	1.62	0.40	0.05	1.21	0.33	0.49	Elderberry	1.36	0.29	0.xv	one.38	0.32	0.16
Long LOS	0.86	0.18	0.49	0.89	0.19	0.57	Long LOS	1.06	0.21	0.77	ane.06	0.21	0.78
	Knew when to contact doctor about condition							Had a change in medications
Low SES	0.86	0.19	0.50	0.78	0.19	0.33	Depression SES	0.fourscore	0.28	0.52	0.89	0.34	0.77
Male	0.86	0.xix	0.50	0.85	0.18	0.45	Male	0.59	0.20	0.12	0.60	0.20	0.13
Elder	0.89	0.22	0.63	0.78	0.21	0.37	Elder	i.41	0.fifty	0.33	1.28	0.l	0.52
Long LOS	0.94	0.22	0.fourscore	0.95	0.22	0.84	Long LOS	0.74	0.27	0.41	0.74	0.27	0.42

Two of the iii characteristics (male and low SES) each remained significant in 1 multivariate model. Specifically, males remained significantly less likely to not empathize their self-care, even after controlling for SES status, age group, and length of stay grouping (p=0.03). Those with low SES remained significantly less likely to exist able to get their prescriptions filled, compared to those with high SES, after control for gender, historic period grouping, and length of stay grouping (p=0.01). After controlling for other characteristics, males were significantly less probable to understand how to care for themselves after discharge and were significantly more than probable to be readmitted before their scheduled follow upwards md appointment. Respondents with lower SES status were significantly less likely to report power to get their prescriptions filled or discuss concerns with their md or with the infirmary and significantly less likely to take a follow upwards doctor appointment scheduled earlier they discharged from the index hospitalization, after controlling for historic period group, gender and length of stay.

Patients' perceptions of why they were readmitted

Patients were asked how they thought they became sick enough to be readmitted to the hospital. Ninety-ane percent of the survey participants (northward=482) responded to this question, providing 617 perceived reasons for their readmission. Those perceived reasons were categorized into 55 groups, indicating the diversity of reasons. The vast majority of the perceived reasons (76.5% of all comments) were reports of symptoms, such as shortness of jiff (9.4% of comments), nausea/airsickness (x.5% of comments), and abdominal pain (5.05% of comments). However some patients did report non-symptom related reasons for their readmissions: 1.3% reported they felt they had been discharged too early from their previous hospitalization, 3.2% reported having complications from a previous handling, and one.viii% reported missing outpatient or dwelling treatment. Just nether 2% (northward=10) reported non-medical reasons. For example, one patient with colon cancer said he was unable to speak to anyone at the clinic in a timely mode to become questions answered.

Conclusion/Discussion

Almost studies on hospital readmissions actualization in the literature relied on medical chart review or analysis of large medical claims datasets, such as the seminal piece of work in this area past Jencks et al that used Medicare claims data (Jencks et al., 2009). Few studies describing patient perceptions of their discharge and post-discharge experience appear in the literature (Kangovi et al., 2012). For example, less than a quarter of the 35 studies included in a systematic review of research identifying patient predictors of acute myocardial infarction relied on patient interviews (along with existing patient data) (Desai, Stauffer, Feringa, & Schreiner, 2009). This study fills this gap by presenting findings of a survey of patients readmitted to a university hospital located in the Southern US.

In general, readmitted patients reported reasonably skilful post-discharge experiences, as indicated past the high percentage (≥74%) of patients who reported understanding self-care, their medications, when to contact the doctor, being able to get prescriptions filled, and having transportation. More 3-fourths of patients reported that they believed they were prepared for their initial belch. In a similar survey study of patients readmitted to ii large Pennsylvania hospitals, conducted in 2012 past Kangovi et al, responses were also loftier and similar to these – in that survey, 86.4% of patients reported feeling they were prepared for self-care (Kangovi et al., 2012).

2 possibilities can help explain the high marks on discharge noesis and experience constitute amid readmitted patients in this report. Commencement, it is possible that these survey responses suffer from courtesy or acquiescence response bias, where surveyed patients gave positive or pleasant responses they idea the surveyors desire to hear (Felix, White, McCullough, Morgan, & Stewart, 2004; Hall, 1995). At to the lowest degree theoretically, had these patients (and/or their caregivers) been as prepared at discharge for domicile care as indicated by the survey responses, they may not have needed to exist readmitted. However, this hypothesis could not be tested given the availability of data and the lack of a matched group of patients who were not readmitted inside thirty days. Time to come research to tease this out is warranted.

Second, it is possible that weaknesses in question construction affected the responses of patients. Patients were just given the opportunity to select "yes" or "no" in response to the questions. Notwithstanding, several of the readmitted patient survey questions addressed concepts that go beyond an "all or zip" response. For example, one question asked patients, "Do yous understand what y'all were supposed to do to care for yourself at home?" Patients may have some, but not comprehensive, knowledge and understanding of self-intendance; yet the survey allowed them to select just "yes" or "no" in response. The utilise of a Likert Calibration for this question would have immune patients to bespeak the degree of understanding, thus allowing for the identification of patients with some limited cognition but not lack of complete cognition (Bethlehem & Biffignandim, 2007; Streiner & Norman, 2008).

Another key finding of this study was low rates of reported post-discharge doctor visits. In other research into hospital readmissions, about half of patients saw their medico betwixt hospitalizations (Jencks et al., 2009; Kangovi et al., 2012). In the present study, only about one-3rd (31.iii%) of readmitted patients reported seeing doctors betwixt hospitalizations. Theoretically having hospital staff arrange follow-upwardly medico appointments for patients prior to their discharge may ameliorate this rate. However, every bit shown in the results, of the 201 patients discharged with a post-hospitalization doctor appointment in hand, only 73 (36.3%) actually showed upwards for the date. Greater attention may need to exist given to understanding why patients do not prove upward for follow up doctor appointments in club to develop strategies to improvement the show upward charge per unit.

For 188 of the patients who completed the survey, the bodily date of the follow-up appointment was included in the survey information. For these patients, the average number of days from discharge to the actual appointment date was 14.five days (SD 12.9 days). Nevertheless, for these same patients, the average number of days betwixt their discharge from the initial hospitalization and their readmission was 12.2 days (SD 7.9 days). In other words, they were readmitted to the hospital before their follow up md date could take even occurred. This suggests that follow up doc appointments scheduled fifty-fifty relatively close to discharge (inside two weeks of belch) may be as well far out. Discharge staff may desire to strive for follow up doctor appointments closer to the date of discharge.

Only two patient characteristics – male gender and lower SES -- were associated with a poorer post-belch experience. Both males and those with lower SES were significantly less probable to report that they had a follow upwards physician appointment scheduled before they discharged from the hospital. If post discharge medico appointments are important for reducing the odds of readmission, discharge staff may demand to specifically target these ii patient subgroups for follow up doc appointment scheduling.

Males were less likely to sympathize self-care, and those with lower SES were less probable to discuss concerns with their doctor'due south part or the infirmary. These patients may need to be targeted for increased pre-belch patient educational activity to improve self-care and self-advocacy. Interestingly, a high percentage of all responses to the open-ended question about what led to the readmission were symptoms (e.g. came back to the infirmary because of airsickness, fever or hurting). This may indicate that patients understood their condition and alert signs of decline in their condition, and sought treatment. However, the relationship of reported symptoms to actual diagnoses and the relationship of reported symptoms to patient characteristics diagnoses were not tested given the available data.

Finally, patients with lower SES were less likely to be able to get their prescriptions filled afterwards belch. To help reduce the risk of hospital readmissions, patients with limited resources may demand help from hospital discharge staff with getting their prescriptions filled prior to belch. However, it is possible that in the future express prescription drug access volition be less of an issue as insurance plans in the Health Insurance Marketplace established under the Affordable Care Deed are required to include prescription drug coverage.

The study results take several implications for health care delivery and quality improvement exercise. However, limitations to these findings must be best-selling. This study was limited by its reliance on administrative data. The data were collected as part of regular infirmary QA activities and not through a controlled study with a rigorous survey research design method or use of a comparing grouping of patients who were not readmitted. As such, bias may have been introduced through questionnaire blueprint, patient selection, the data drove process, and data entry errors. For case, we recognize that the study may suffer from courtesy bias or acquiescence response bias, which may misconstrue the motion-picture show of bodily post-discharge experience of patients. In the time to come, academics and/or hospital administrators conducting such surveys could employ strategies to minimize courtesy bias, such every bit ensuring a comfortable interview setting, establishing skillful rapport between the patient and surveyor, and having non-clinical staff administrate the survey.

Despite the limitations, this written report does aggrandize the hospital readmission literature by providing direct patient responses to questions related to their post-discharge experience rather than additional predictors of 30-day hospital readmission derived for medical claims data. Although patients reported a relatively positive post-discharge experience, results propose that there is room for improving the discharge and post-belch process. Of item note, follow up doctor appointments likely need to be schedule closer to the discharge date to help reduce infirmary readmissions (although this suggestion should exist tested in an intervention trial). In addition, this report highlights bug around the use of survey research in administrative operations. Survey enquiry methods are complex and involve, at a minimum, sample pick, question/musical instrument design, standardized data collection processes, and specialized analysis methods (Fowler, 2013). Although many may believe they tin administrate a survey, poorly designed surveys do occur (Babbie, 1973; Fowler, 2013; Morrel-Samuels, 2002). A number of survey design and data collection issues were identified as the data from the readmitted patient survey were analyzed and reported on herein. Engagement of researchers or consultants with expertise in survey research with routine QA and administrative activities could aid accost those issues and improve reliability and validity of results.

Acknowledgments

The study was supported by a pilot enquiry award from the UAMS Translational Research Plant (UL1TR000039).

Footnotes

The authors take no conflict of interest in the execution of this study or publication of its results.

Contributor Data

Holly C. Felix, Fay West. Boozman College of Public Health, Academy of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 820-12, Little Rock, AR 72205, ude.smau@yllohxilef / 501-526-6626 / 501-526-6620 fax.

Beverly Seaberg, University Hospital, University of Arkansas for Medical Sciences, 4301 Due west Markham Street, Slot 572, Little Rock, AR 72205, ude.smau@grebaesab / 501-686-6703.

Zoran Bursac, Fay W. Boozman College of Public Wellness, Academy of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 820, Little Rock, AR 72205, ude.smau@narozcasrub 501-526-6723.

Jeff Thostenson, Fay West. Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 West Markham Street, Slot 820, Little Rock, AR 72205, ude.smau@nosnetsohtdj / 501-526-6727.

Thousand. Kathryn Stewart, Fay W. Boozman Higher of Public Health, University of Arkansas for Medical Sciences, 4301 Westward Markham Street, Slot 820-12, Little Rock, AR 72205, ude.smau@kyramtrawets / 501-526-6625.

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Can a Patient Be Discharged and Then Admitted Again Same Day

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731880/

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