Investigating the characteristics and needs of frequently admitting hospital patients: a cross-sectional study in the UK

Objectives This study forms the user requirements phase of the OPTIMAL project, which, through a predictive model and supportive intervention, aims to decrease early hospital readmissions. This phase aims to investigate the needs and characteristics of patients who had been admitted to hospital ≥2 times in the past 12 months. Setting This was a cross-sectional study involving patients from Croydon University Hospital (CUH), London, UK. Participants A total of 347 patients responded to a postal questionnaire, a response rate of 12.7%. To meet the inclusion criteria, participants needed to be aged ≥18 and have been admitted ≥2 times in the previous 12 months (August 2014–July 2015) to CUH. Primary and secondary outcomes To profile patients identified as frequent admitters to assess gaps in care at discharge or post-discharge. Additionally, to understand the patients’ experience of admission, discharge and post-discharge care. Results The range of admissions in the past 12 months was 2–30, with a mean of 2.8. At discharge 72.4% (n=231/347) were not given a contact for out-of-hours help. Regression analysis identified patient factors that were significantly associated with frequent admissions (>2 in 12 months), which included age (p=0.008), being in receipt of care (p=0.005) and admission due to a fall (p=0.01), but not receiving polypharmacy. Post-discharge, 41.8% (n=145/347) were concerned about being readmitted to the hospital. In the first 30 days after discharge, over half of patients (54.5% n=189/347) had no contact from a healthcare professional. Conclusion Considering that social care needs were more of a determinant of admission risk than medical needs, rectifying the lack of integration, communication and the under-utilisation of existing patient services could prevent avoidable problems during the transition of care and help decrease the likelihood of hospital readmission.

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[3]However, not all readmissions are due to sub-optimal patient care and many readmissions may be unavoidable and appropriate, for example where patients are chronically or terminally ill. [4] [5] Two UK studies found around 60% of early readmissions were due to the same reason as the primary admission, suggesting that these could have been reduced by medication reviews, better discharge communication and a rapid response to preventable issues. [6] [7] Both polypharmacy and chronic conditions such as chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD) and diabetes have been found to be associated with high readmissions rates and increased needs following discharge. [8] [9] Accurately identifying patients as high risk enables resources to be channelled specifically to these patients through supportive interventions, rather than providing for all patients, many of whom may not be at risk of readmission. Several predictive models have been developed in the UK such as PARR-30 [10] and in Canada the LACE [11] with relatively good predictive accuracy.
Evaluating the effectiveness of interventions designed to prevent early readmissions is problematic due to the lack of robust studies with good methodologies. [9] Intervention types which have been studied, often in combination include: Extensive discharge planning, telephone calls, home visits, a 24-hour hot line and patient education. [9] The provision of follow up telephone calls is a common intervention, with variation in the number and length of calls and profession of caller. The most successful results included both pre and post-discharge interventions. [12] Schemes for supporting patients with their medications in the community were introduced into  The need for successful management of the pre and post-discharge period is highlighted in the National Institute for health and Care Excellence (NICE) Guidelines [14], developed in 2015 to help with the transition of adult patients with social care needs from hospital to the community. These guidelines emphasise the importance of the transition of care being co-ordinated using good communication. All healthcare professionals (HCPs) involved with the care of the patient in hospital and the community, should be included in the communication loop, with all patients/carers being provided with a medication list and a care plan with a single HCP responsible for co-ordinating the discharge for both social and medical needs. This paper reports on the first stage of the OPTIMAL project, funded by Innovate UK. The OPTIMAL project encompasses the development of a predictive risk model, together with a supportive postdischarge patient intervention with the aim of reducing early hospital readmission. Although the success of both predictive risk models and interventions to prevent hospital readmission have been developed and studied separately before, this is the first time, to our knowledge, that a predictive model and a preventative intervention have been integrated to support patients.
The aim of this study was to undertake a needs assessment to investigate any common characteristics of patients identified as frequent admitters (≥2 in the past 12 months) and understand their experiences of both the discharge process and the immediate post-discharge period, with any difficulties which could contribute to readmission. This will assist in the development of an appropriate post-discharge intervention for patients identified at high risk of readmission. A cross-sectional study was carried out at Croydon University Hospital (CUH). Patients were considered for inclusion in the study if they met the following criteria: ≥18 years, a home address on the CUH database, experienced ≥ 2 admissions to CUH in the past 12 months (August 2014-July 2015) and were discharged between > 30 days and < 12 months ago. Paediatric, oncology and maternity patients were excluded from the study. CUH research and development (R&D) department using patient records identified a total of 2732 patients who met these inclusion criteria.

METHODS
To provide a confidence level of 95% and a confidence interval of 5%, the sample size was calculated as 337 patients. As a low response rate may be expected from postal survey, all 2732 patients were invited to complete the postal questionnaire. An explanatory letter was sent with the questionnaire together with a pre-paid return envelope. The questionnaire was only made available in English and no reminders were sent.
Ethical approval was obtained from Kingston University Delegated Research Ethics Committee (Ref: 1415/035) and approved by the R&D department by CUH as a service evaluation.
A quantitative cross-sectional questionnaire survey was designed using a mixture of open and closed questions. The validated tools AUDIT-C (a brief alcohol screening tool used to identify alcohol dependency) [15] and a medical health literacy score [16] were incorporated together with other questions which investigated patient experience and knowledge of medication and discharge counselling. The questionnaire was in four sections: Firstly, demographic information, collecting personal information such as age, as well as medication list and current medical conditions. Secondly, understanding the patient's admission experience, the reason for the patient's attendance at A&E and satisfaction with the admission process. Thirdly, the patient's discharge experience, investigating patients' involvement in their discharge planning and the provision of medication counselling. Finally, understanding the patients' post-discharge experience, the discharge support received by patients, as well as patients' confidence in managing their health and coping at home post-discharge. After receiving ethics approval, a pilot study was conducted which involved asking 10 patients from the discharge lounge at CUH to complete the survey for validation. Minor changes were made to the questionnaire. To prevent any bias, the findings from the pilot were not included in the final results.

Patient and Public Involvement
The study was a follow up study from 50 patients at the Trust who indicated mixed experience in counselling and shared decision making during admission. As part of the funding, the researchers the researchers agreed to inform patients/public of the outcome of the study which was done in the public engagement forums within the Trust.

Data Analysis
The responses from the returned questionnaires were analysed using IBM SPSS ver. 23 ® through descriptive statistics and the Chi-squared test for independence, with a level of significance set at 5% (p<0.05). A comorbidity polypharmacy score (CPS) was calculated (defined as the total of the number of pre-trauma comorbidities and the number of pre-admission medications in trauma patients ≥45 years). Our modified calculation was performed for all patients ≥45 years, using the number of medications specified in the questionnaire, together with the number of existing complaints recorded. A three question Audit-C score [15]was calculated, with each question having a possible score of 0-4 and giving a total score in the range between 0-12. A score of ≥ 5 is considered positive, indicating a higher risk of alcohol consumption. A single question health literacy tool was utilised giving scores of 1-5, with scores >2 indicating some difficulty reading printed health material.
The number of medications most associated with adverse drug reactions (ADR) resulting in hospital admission was also recorded for each patient [17].
A linear regression analysis was carried out on the data to help identify significant patient characteristics which may have contributed to a greater number of admissions in the previous 12 months. This was carried out by adding a dependent variable column "frequent_admitter" to the data which was then assigned 1 if a patient's admissions in the previous year were >2 or 0 if ≤2. The independent variables included in the regression analysis were: admission reason, (ethnicity, condition complexity indicator (which was set if a patient described their existing situation as complex/complicated or reported ≥ 2 conditions), a care indicator (identified by patients who were in receipt of some home care), CPS, patient age, number of medications. Any rows where any of these variables was missing was dropped from the regression analysis, thus leaving 169 patients to be included in the analysis.

RESULTS
The questionnaires were sent to 2722 patients, 347 were completed and returned giving a response rate of 12.7%. Valid percentages are reported due to respondents not always fully completing the questionnaire.
The most common reasons given for the last admission were respiratory problems such as asthma and COPD (15.0%, n=52). Nearly 10% (n=33) of patients were admitted due to a fall. Nearly a third (n=107) of patients reported more than one condition or described their condition as complex (Table   1).  Over a quarter (28.8%, n=99/344) of patients lived alone and less than 5% (4.4% n=15/344) lived in a care home. Not all patients had someone to care for them; 26.7% (n=88/330) reported that they had no available care. Only 13.1% (n=43/328) of patients currently smoked, which is less than the UK average of 19% [18]. However, 39.3 % (n=129/328) described themselves as ex-smokers. Nearly a third of patients had a limited health literacy score (29.8%, n=101/339) and over 15% (16.6%, 30/180) had a positive AUDIT-C score associated with a higher alcohol consumption risk.

Admission
Over half of patients were referred to A&E by an HCP (59.3%, n=204/344), with just over a third (34.9%, n=120/344) of patients reporting that a family member or they themselves made the decision. Although, two-thirds of patients (69%, n=234/339) were consulted regarding admission and care decisions, nearly all patients (93.1%, n=311/334) wanted to be more involved with these

Regression Analysis
Four variables were found to be significant predictors of >2 admissions in the previous 12 months.
These were admission for a fall (p=0.008), not identifying as having a complex condition or reporting <2 conditions (p=0.002), age (p=0.009) and being in receipt of care at home (p=0.006). Additionally, the overall regression is significant according to the F test (F=0.03).

Discharge
Nearly half of patients, (44.1%, n=146/331) were not informed of the discharge decision 24 hours in advance, including 43.4% (n=43/99) of those who lived alone.
Nearly three-quarters (70.1% n=234/334) of patients agreed that the decisions regarding the discharge procedure were clearly explained (Table 2). However, only a third of patients (37.9%, n=119 /314) were provided with information to enable them to detect signs of deteriorating health.
Furthermore, nearly three quarters of patients, (72.4% n=231) were not provided with contacts for out-of-hours support. Less than a third of patients were referred to a post-discharge service and less than half of respondents reported joining this service (Table 2). Over half of patients (55.9%, n=132/236) were prescribed two or more 2 medications that could put them at high risk of admission due to an ADR (Table 4). sharing supports the transition from hospital and helps prevent readmission [14][34]. Contact information should be provided in case of a short-term crisis, which should be proactive rather than waiting for a more serious problem to arise. However, it was found that nearly 40% of patients were not provided with the signs of deterioration of their condition and nearly three quarters of patients were not provided with details of who to contact if this situation arose. This lack of information could result in patients returning to hospital. Additionally, patients' carers and families were not always informed of the discharge, making it hard for them to adequately support the patient at home.

19
Although the success of both predictive risk models and interventions to prevent hospital 20 readmission have been developed and studied separately before, this is the first time, to our 21 knowledge, that a predictive model and a preventative intervention have been integrated to support 22 patients.

23
The aim of this study was to undertake a needs assessment to investigate any common 24 characteristics of patients admitted more than one time to CUH in a period of 12 months and 25 understand their experiences of both the discharge process and the immediate post-discharge

14
The questionnaires were sent to 2722 patients, 347 were completed and returned giving a response 15 rate of 12.7%. 16 The most common reasons given for the last admission were respiratory problems such as asthma 17 and COPD (15.0%, n=52). Nearly 10% (n=33) of patients were admitted due to a fall. Nearly a third 18 (n=101) of patients reported more than one condition or described their condition as complex (Table   19 1).
20 Two thirds (67.4% n=234/347) of patients agreed that the decisions regarding the discharge 5 procedure were clearly explained (Table 2). However, only a third of patients (34.3%, n=119/347) 6 were provided with information to enable them to detect signs of deteriorating health. Furthermore, 7 only a third of patients (33.4% n=116/347) were provided with contacts for out-of-hours support. 8 Less than a third of patients were referred to a post-discharge service and less than half of 9 respondents reported joining this service (Table 2).
10 Steroidal Anti-Inflammatory Drug (NSAID) and a diuretic. 8 Over half of patients (56.4%, n=132/234) were prescribed two or more 2 medications that could put 9 them at high risk of admission due to an ADR (Table 4).
10 There is additional evidence to suggest that co-morbidities are a significant factor when predicting 14 early readmission. The Charlson Index, which predicts 10-year mortality based on patients' 15 comorbidities, was found to be significantly associated with readmission within 28 days for patients 16 scoring ≥3 in a retrospective observational study by Li et al. [29] Interestingly, Considine et al [30] 17 found that comorbidities were not significant predictors of readmission ≤1 day post-discharge for 18 patients from acute-care, however health service use was notable in the 6-months preceding the 19 index admission with ≥1 ED attendance or ≥1 hospital admission in 42.6% (n=579) and 40.7% (n=553) 20 respectively. Although our study focused primarily on frequent admission as opposed to 21 readmission, the latter study could provide an explanation of why co-morbidities were only a 22 predictor of high admission rate (>3 in 12 months). [30] 23 It must be noted that in this study, medications and conditions were self-reported. However, these 24 were not found to be significantly associated with frequent admission (>2 in 12 months), thus This part is about your experience while you were being admitted and treated at Croydon University Hospital.

1) What was the reason for your last admission at Croydon University Hospital?
2) Who decided that you need to go to A&E?
3) Did you try to seek help from any of the following before attending A&E? (

8) How often do you have a drink containing alcohol?
▪ Never (please skip to 11) ▪ Monthly or less ▪ 2 to 4 times a MONTH ▪ 2 to 3 times a WEEK ▪ 4 or more times a week 9) How many drinks of alcohol do you drink on a typical day when you are drinking? ▪ 1 or 2 drinks ▪ 3 or 4 drinks ▪ 5 or 6 drinks ▪ 7 or 8 or 9 drinks ▪ 10 or more drinks iii Are you currently using e-cigarettes?  Yes  No 12) How many regular medicines are you currently taking?

21
Evaluating the effectiveness of interventions designed to prevent early readmissions is problematic 22 due to the lack of robust studies with good methodologies. [9] Intervention types which have been 23 studied, often in combination include: extensive discharge planning, telephone calls, home visits, a 24 24-hour hot line and patient education. [9] The provision of follow up telephone calls is a common

19
Although the success of both predictive risk models and interventions to prevent hospital 20 readmission have been developed and studied separately before, this is the first time, to our 21 knowledge, that a predictive model and a preventative intervention have been integrated to support 22 patients.

23
The aim of this study was to undertake a needs assessment to investigate any common 24 characteristics of patients admitted more than one time to CUH in a period of 12 months and 25 understand their experiences of both the discharge process and the immediate post-discharge After receiving ethics approval, a pilot study was conducted which involved asking 10 patients from 7 the discharge lounge at CUH to complete the survey for validation. Minor changes were made to the 8 questionnaire. To prevent any bias, the findings from the pilot were not included in the final results.

9
Patient and Public Involvement

10
The study was a follow up study from 50 patients at the Trust who indicated mixed experience in 11 counselling and shared decision making during admission. As part of the funding, the researchers 12 agreed to inform patients/public of the outcome of the study. This was completed via the public 13 engagement forums within the Trust.
14 Data Analysis

15
The responses from the returned questionnaires were analysed using IBM SPSS ver. 23 ® through 16 descriptive statistics and the Chi-squared test for independence, with a level of significance set at 5% 17 (p<0.05). A comorbidity polypharmacy score (CPS) was calculated (defined as the total of the 18 number of pre-trauma comorbidities and the number of pre-admission medications in trauma 19 patients ≥45 years). Our modified calculation was performed for all patients ≥45 years, using the 20 number of medications specified in the questionnaire, together with the number of existing 21 comorbidities recorded. A three question Audit-C score [16] was calculated, with each question 22 having a possible score of 0-4 and giving a total score in the range between 0-12. A score of ≥ 5 is 23 considered positive, indicating a higher risk of alcohol consumption. A single question health literacy 24 tool was utilised giving scores of 1-5, with scores >2 indicating some difficulty reading printed health  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  16 RESULTS

17
The questionnaires were sent to 2722 patients, 347 were completed and returned giving a response 18 rate of 12.7%.

19
The most common reasons given for the last admission were respiratory problems such as asthma 20 and COPD (15.0%, n=52). Nearly 10% (n=33) of patients were admitted due to a fall. Nearly a third 21 (n=101) of patients reported more than one condition or described their condition as complex (Table   22 1).
23 only a third of patients (33.4% n=116/347) were provided with contacts for out-of-hours support.

10
Less than a third of patients were referred to a post-discharge service and less than half of 11 respondents reported joining this service (Table 2).

1) What was the reason for your last admission at Croydon University Hospital?
2) Who decided that you need to go to A&E?
▪ Yes ▪ No (please go to 12) i Yes, and I am currently a smoker o How frequently do you smoke cigarettes? ▪ Regularly ▪ Occasionally (b) How many cigarettes do you smoke per day?
▪ 10 or Less ▪ 11-20 ▪ 21-30 ▪ 31 or More ii Yes, but I am an ex-smoker o How frequently did you smoke cigarettes? ▪ Regularly ▪ Occasionally (b) About how many cigarettes did you smoke in a day?  10 or Less  11-20  21-30  31 or More (c) For approximately how many years did you smoke cigarettes regularly?
(d) How long ago did you stop smoking cigarettes?
iii Are you currently using e-cigarettes?  Yes  No 12) How many regular medicines are you currently taking?

Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 3-5 Objectives 3 State specific objectives, including any prespecified hypotheses 5

Study design 4
Present key elements of study design early in the paper 6  Discuss the generalisability (external validity) of the study results 16-17

Other information
Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based 18 *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46