Factors Affecting the Effectiveness of Discharge Planning Implementation: A Case-Control Cohort Study : Journal of Nursing Research

Secondary Logo

Journal Logo


Factors Affecting the Effectiveness of Discharge Planning Implementation: A Case-Control Cohort Study

PO, Hui-Wen1; LIN, Fang-Ju2; CHENG, Hsing-Jung3; HUANG, Mei-Ling4; CHEN, Chung-Yu5; HWANG, Juey-Jen6; CHIU, Yi-Wen7∗

Author Information
Journal of Nursing Research 31(3):p e274, June 2023. | DOI: 10.1097/jnr.0000000000000555
  • Open



Improvements in medical care quality, nutrition, and environmental hygiene have led to continuing growth in the older adult population worldwide. The share of the global population aged 65 years or above is projected to raise from 10% in 2022 to 16% in 2050. The number of persons aged 65 years or over worldwide is projected to be more than twice the number of children (United Nations, 2022). Population aging has been the fastest in Asia. In Taiwan, the older adult population exceeded 14% in 2018 and is projected to exceed 20% in 2025 (Ministry of the Interior, Taiwan, ROC, 2021).

Population aging reflects a change in the demographic structure that typically increases the population living with chronic diseases and disabilities. The total number of disabled people in Taiwan exceeded 800,000 in 2018 and is estimated to increase to 1.3 million in 2031 (Ministry of the Interior, Taiwan, ROC, 2021). Because the rate of disability related to acute conditions after hospital discharge is as high as 50%, corresponding medical care and public health policies have become more important (World Health Organization, Organisation for Economic Co-operation and Development, & The World Bank, 2018). In addition, because medical resources are limited, rising numbers of people with long-term care needs occupying acute beds in hospitals will necessarily increase the average length of hospital stays and rates of readmission. Therefore, promoting effective discharge planning is essential in clinical practice.

Discharge marks the beginning of the emergence of care problems. Therefore, discharge planning must assess the specific types of care and resources patients may need after discharge and then provide the necessary health knowledge, care guidance, home environment improvement advice, and social resource introductions during hospitalization (Pellett, 2016). Discharge planning is intended to solve the problems of patients and their families in a planned way, with the aim of helping make the discharge process smooth, comfortable, and comprehensive for patients and their families. Hospital-based case management models should provide time-effective and integrated medical professional care to ensure patients receive good-quality, cost-effective care (Case Management Society of America, 2016).

A systematic review of 30 controlled trials conducted by Gonçalves-Bradley et al. in 2016 found that personalized discharge planning shortened the length of hospital stays, reduced the 3-month readmission rate by 13%, and increased patient and medical staff satisfaction (Gonçalves-Bradley et al., 2016). Although analysis in previous studies on the effectiveness of hospital discharge planning focused on hospitalization days, readmission rates, and mortality, most of these studies focused on a single disease/procedure such as heart failure, stroke, diabetes, orthopedic surgery, and mental illness (Henke et al., 2017; Nunes & Queirós, 2017; Xiao et al., 2019). Large-scale database analyses on the effectiveness of overall discharge planning are lacking in the literature.

The purpose of discharge planning is to help ensure patients obtain guidance for follow-up care and other arrangements as soon as possible with the goals of reducing discharge anxiety and care load and facilitating a smooth discharge experience for patients and their families. The benefits of discharge planning realized by the hospital and health insurance providers include reduced hospitalization stays and lower readmission rates. Furthermore, discharge planning increases hospital bed utilization and turnover rates and reduces overall medical expenses. Therefore, the purpose of this study was to explore the effectiveness of discharge planning and investigate the clinical factors affecting length of hospitalization, readmission rates, and follow-up status after discharge.


Study Design and Sample

This retrospective study was conducted at a 1,000-bed medical center in Yunlin County, Taiwan. Patients' medical records were collected from the medical information system database and the care service management information system of the Ministry of Health and Welfare. Data were collected retrospectively from patients who had received medical treatment and follow-up care at the center between January 2017 and December 2018. Factors affecting the effectiveness of discharge planning implementation were compared between patients who were hospitalized for more than 30 days, were readmitted within 14–30 days after discharge, or died within 30 days after discharge and patients in the control group (hospitalization < 30 days, no readmission between 14 and 30 days after discharge, and survival through 30 days after discharge).

Ethical Considerations

The study protocol was reviewed and approved by the institutional review board of National Taiwan University Hospital (IRB number: 201810056RIND). Signed informed consent from the patients was waived because of the retrospective nature of the study.

Data Collection and Variables

After being exported from the databases, data were first sorted, and patients with incomplete data, predischarge mortality, a critical illness, or an advisory against discharge were excluded. The relevant data for this study were extracted from two databases: (a) the Hospital Medical Information System database, which provided basic demographic and clinical characteristics, including gender, age, medical diagnosis, main caregivers, Activities of Daily Living Scale scores, indwelling catheter and tube status (including nasogastric tube or feeding tube, tracheostomy tube, surgical drainage tube, and Foley/urinary catheter), unclean wound status, dates of readmission, and financial concerns, and (b) the Care Service Management Information System of the Ministry of Health and Welfare, which contains data on patient medical and follow-up care situations after hospital discharge.

Activities of Daily Living Scale

The daily activity function was evaluated based on Activities of Daily Living Scale scores. The scale assesses the respondent's performance on 10 self-care activities, including seven self-care abilities (eating, grooming/personal hygiene, toileting, bathing, dressing and undressing, stool control, urinary control functions) and mobility (shifting/turning between wheelchair and bed, walking/walking on flat ground, up and down stairs). Each item is scored as “complete independence,” “partial assistance,” or “complete dependence,” and the total possible score is 100, with higher scores associated with a higher degree of independence in life activities. After the score was imported automatically, it was divided into five grades: totally dependent (0–20), severely dependent (21–60), moderately dependent (61–90), mildly dependent (91–99), and completely independent (100).

Hospitalization was defined as total length of hospital stay. Readmission refers to the patient being readmitted to the hospital after discharge. Survival status refers to the survival or death of the patient between discharge and the end of the follow-up period (September 30, 2020).

Statistical Analysis

Continuous variables included age and total hospitalization days and were expressed as mean ± standard deviation. Categorical variables, including gender, activities of daily living (ADLs), diagnostic department, discharge status, main caregivers after discharge, and long-term care service utilization, were expressed in numbers and percentages (%). Correlations between the above variables were calculated using the Pearson's chi-square test and expressed as correlation coefficients. Odds ratios (ORs) were tested using Bonferroni post hoc tests of the respective variables. Multivariate analysis was performed using binary logistic regression when p < .05.

For correlations between diagnostic department, discharge status, ADL values, and total length of stay, the differences were analyzed using Student's t tests or one-way analyses of variance, after which binary variable regression was used for multivariate regression analysis. The impact of the implementation of discharge planning on length of hospitalization was assessed using the Kaplan–Meier curve for single-variable analysis and the log-rank test. The Cox regression model was used for multivariate analysis. IBM SPSS Statistics Version 22.0 (IBM Inc., Armonk, NY, USA) was used for various analyses. A p value of less than .05 was used to determine statistical significance.


Data from 7,796 patients hospitalized between 2017 and 2018 and accepted for discharge planning were used as the initial sample in this study. Over half (4,365, 56.0%) of the participants were male, the median age was 77 years (range: 2–105 years; mean ± standard deviation: 73.5 ± 14.4 years), and 4,401 cases (56.5%) were older than 75 years. In terms of reason for admission, 4,832 patients were admitted because of an internal-medicine-related disease; 1,141, because of surgery; 329, because of a neurologic disease; 133, because of an orthopedic disease; and 1,361, because of other diseases.

Hospital Length of Stay

Of the 953 patients who were hospitalized for more than 30 days, 553 (58.0%) were male. Most of them were below the age of 75 years (n = 517, 53.2%), whereas 19.9% (n = 190) had ADL scores of 60 or lower, and 17.9% (n = 171) had indwelling catheters or tubes (as shown in Table 1).

Table 1. - Clinical Characteristics Associated With Hospital Length
Clinical Factor n Hospital Stay ≥ 30 Days Hospital Stay ≥ 60 Days
(n = 953)
(n = 6,843)
χ2 Yes
(n = 207)
(n = 7,589)
n % n % n % n %
Male 1.828 5.221**
 Yes 4,365 553 12.7 3,812 87.3 132 3.0 4,233 97.0
 No 3,431 400 11.7 3,031 88.3 75 2.2 3,356 97.8
≥ 75 years old 50.580*** 14.559***
 Yes 4,401 436 9.9 3,965 90.1 90 2.0 4,311 98.0
 No 3,395 517 15.2 2,878 84.8 117 3.4 3,278 96.6
Enrollment criteria for discharge planning 787.709***
 ADL score ≤ 60 82.834***
  Yes 4,787 190 4.0 4,597 96.0 190 4.0 4,597 96.0
  No 3,009 763 25.4 2,246 74.6 17 0.6 2,992 99.4
 With tubes 534.072*** 75.081***
  Yes 4,128 171 4.1 3,957 95.9 171 4.1 3,957 95.9
  No 3,668 782 21.3 2,886 78.7 36 1.0 3,632 99.0
 With pressure wound or unclear wound 39.339*** 13.125***
  Yes 692 33 4.8 659 95.2 33 4.8 659 95.2
  No 7,104 920 13.0 6,184 87.0 174 2.4 6,930 97.6
 Poorly controlled chronic diseases 9.385** 1.142
  Yes 148 6 4.1 142 95.9 6 4.1 142 95.9
  No 7,648 947 12.4 6,701 87.6 201 2.6 7,447 97.4
Readmission in 14 days with the same disease 3.141 0.001
  Yes 37 1 2.7 36 97.3 1 2.7 36 97.3
  No 7,759 952 12.3 6,807 87.7 206 2.7 7,553 97.3
 Financial problems 0.639 0.686
  Yes 17 1 5.9 16 94.1 1 5.9 16 94.1
  No 7,779 952 12.2 6,827 87.8 206 2.6 7,573 97.4
 Insufficient ability of main caregiver 3.113 3.572
  Yes 84 5 6.0 79 94.0 5 6.0 79 94.0
  No 7,712 948 12.3 6,764 87.7 202 2.6 7,510 97.4
 Older adult live alone 3.409 0.001
  Yes 39 1 2.6 38 97.4 1 2.6 38 97.4
  No 7,757 952 12.3 6,805 87.7 206 2.7 7,551 97.3
 Inclusion criteria ≥ 2 248.260*** 123.733***
  Yes 3,146 161 5.1 2,985 94.9 161 5.1 2,985 94.9
  No 4,650 792 17.0 3,858 83.0 46 1.0 4,604 99.0
Note. ADL = activity of daily living.
*p < .05. **p < .01. ***p < .001.

Among those patients who stayed in the hospital for over 30 days and were below 75 years old, several had lower ADL scores (60 or lower), indwelling catheters or tubes, pressure ulcers or unclean wounds, and poorly controlled chronic diseases. Nonetheless, most of the patients (n = 932, 97.8%) who stayed for over 30 days had other medical conditions, which included 675 (70.8%) patients who required prolonged treatment or rehabilitation, 154 (16.2%) who had comorbidities or complications, and 103 (10.8%) who had sustained disease progression.

Two hundred seven patients were hospitalized for more than 60 days, of whom 132 (63.8%) were male. Most were younger than 75 years (n = 117, 56.5%), 91.8% had lower ADL scores ≤ 60 (n = 190), and 82.6% had indwelling catheters or tubes (n = 171).

Those patients with hospital stays over 60 days included a significantly higher proportion of male patients (p = .022), those aged < 75 years (p < .001), those with ADL scores ≤ 60 (p < .001), those with indwelling catheters or tubes (p < .001), and those with pressure ulcers or unclean wounds (p < .001). Furthermore, the requirement for inclusion in discharge planning was having two or more items (p < .001), which was also responsible for the markedly higher percentage of patients who stayed in the hospital for more than 60 days (as shown in Table 1). Of all the patients reviewed, only two (1.0%) had medical issues that demanded prolonged treatment or rehabilitation.

The multivariate analyses revealed that being male (OR = 1.37, 95% CI [1.02, 1.84]), being < 75 years old (OR = 2.50, 95% CI [1.87, 3.34]), having an ADL score ≤ 60 (OR = 9.84, 95% CI [4.16, 23.27]), and having indwelling catheters or tubes (OR = 4.17, 95% CI [1.84, 9.47]) were associated with a significantly increased risk of hospitalization for more than 60 days (Table 2).

Table 2. - Multivariate Analysis of Clinical Factors Affecting Hospital Length
Clinical Factor Wald OR 95% CI p
Male 4.42 1.37 [1.02, 1.84] .035
< 75 years old 38.49 2.50 [1.87, 3.34] < .001
ADL score ≤ 60 27.10 9.84 [4.16, 23.27] < .001
With tubes 11.67 4.17 [1.84, 9.47] .001
With wounds 1.82 1.32 [0.88, 1.97] .177
Inclusion criteria ≥ 2 0.40 0.75 [0.30, 1.85] .529
Note. ADL = activity of daily living; OR = odds ratio.

Readmission After Discharge

Nine hundred fifty-four patients exhibited factors affecting readmission within 14 days of discharge, including 576 (60.4%) men, who were mostly ≥ 75 years old (490, 51.4%). Patients with lower ADL scores (ADL score ≤ 60, n = 654, 68.6%), with indwelling catheters and tubes (n = 698, 73.2%), and with pressure ulcers or unclean wounds were found to be at a higher risk for readmission within 14 days after discharge (Table 3). Six hundred eighty-six patients were readmitted within 30 days after discharge. Analysis of the factors affecting readmission within this period showed risk factors included being male, being < 75 years, having an ADL score ≤ 60, and having indwelling catheters or tubes (Table 3).

Table 3. - Clinical Characteristics Associated With Readmission
Clinical Factor n Readmission ≤ 14 Days Readmission ≤ 30 Days
(n = 954)
(n = 6,842)
χ2 Yes
(n = 686)
(n = 7,110)
n % n % n % n %
Male 8.490** 6.196*
 Yes 4,365 576 13.2 3,789 86.8 415 9.5 3,950 90.5
 No 3,431 378 11.0 3,053 89.0 271 7.9 3,160 92.1
≥75 years old 11.453** 24.400***
 Yes 4,401 490 11.1 3,911 88.9 326 7.4 4,075 92.6
 No 3,395 464 13.7 2,931 86.3 360 10.6 3,035 89.4
Enrollment criteria for discharge planning
 ADL score ≤ 60 23.449*** 8.155**
  Yes 4,787 654 13.7 4,133 86.3 456 9.5 4,331 90.5
  No 3,009 300 10.0 2,709 90.0 230 7.6 2,779 92.4
 With tubes 178.310*** 140.080***
  Yes 4,128 698 16.9 3,430 83.1 511 12.4 3,617 87.6
  No 3,668 256 7.0 3,412 93.0 175 4.8 3,493 95.2
 With pressure wound or unclear wound 15.424*** 2.897
  Yes 692 117 16.9 575 83.1 73 10.5 619 89.5
  No 7,104 837 11.8 6,267 88.2 613 8.6 6,491 91.4
 Poorly controlled chronic diseases 0.051 0.001
  Yes 148 19 12.8 129 87.2 13 8.8 135 91.2
  No 7,648 935 12.2 6,713 87.8 673 8.8 6,975 91.2
 Financial problems 0.641 0.181
  Yes 17 1 5.9 16 94.1 1 5.9 16 94.1
  No 7,779 953 12.3 6,826 87.7 685 8.8 7,094 91.2
Major caregiver after discharge
 Living alone 15.661*** 92.156***
  Yes 845 139 16.4 706 83.6 149 17.6 696 82.4
  No 6,951 815 11.7 6,136 88.3 537 7.7 6,414 92.3
 Spouse 36.413*** 3.043
  Yes 575 116 20.2 459 79.8 62 10.8 513 89.2
  No 7,221 838 11.6 6,383 88.4 624 8.6 6,597 91.4
 Family members 9.053** 0.097
  Yes 1,168 174 14.9 994 85.1 100 8.6 1,068 91.4
  No 6,628 780 11.8 5,848 88.2 586 8.8 6,042 91.2
 Friends 20.706*** 3.712
  Yes 1,147 187 16.3 960 83.7 118 10.3 1,029 89.7
  No 6,649 767 11.5 5,882 88.5 568 8.5 6,081 91.5
 Hired caretaker 16.045*** 9.968**
  Yes 2,578 370 14.4 2,208 85.6 264 10.2 2,314 89.8
  No 5,218 584 11.2 4,634 88.8 422 8.1 4,796 91.9
Note. ADL = activity of daily living.
*p < .05. **p < .01. ***p < .001.

Analysis of the association between caregiver and readmission within 14 days and 30 days after discharge found that patients who lived alone or who were cared for by a spouse, family member, friend, or hired caretaker had a higher risk of readmission within 14 days and that those who lived alone or were cared for by a hired caretaker had a higher risk of readmission within 30 days after discharge.

Multivariate analysis revealed that being male, being ≥ 75 years old, having an ADL score ≤ 60, having indwelling catheters or tubes, living alone, and being cared for by a caregiver independently increased the risk of readmission because of clinical factors within 14 days after discharge (Table 4).

Table 4. - Multivariate Analysis of Clinical Factors Affecting Readmission Within 14 Days
Variant Wald OR 95% CI p
Clinical factor
 Male 4.02 1.16 [1.00, 1.34] .045
 Age ≥ 75 years 4.87 0.84 [0.72, 0.98] .027
 ADL score ≤ 60 7.00 0.77 [0.63, 0.93] .008
 With tubes 33.74 1.62 [1.37, 1.90] < .001
 With wounds 3.32 1.23 [0.99, 1.52] .068
Major caregiver
 Living alone 115.14 6.66 [4.71, 9.42] < .001
 Spouse 118.35 5.36 [3.96, 7.25] < .001
 Family members 115.22 5.53 [4.05, 7.56] < .001
 Friends 150.60 8.46 [6.01, 11.89] < .001
 Hired caretaker 129.04 7.54 [5.32, 10.68] < .001
Note. ADL = activity of daily living; OR = odds ratio.

Postdischarge 30-Day Mortality and Survival

The results of univariate analysis revealed that factors including gender, age, criteria of discharge planning, and main caregivers after discharge increased mortality risk within 30 days after discharge for patients with one or more of the following characteristics: ≥ 75 years old, with prolonged hospital stays, with readmission within 14 days, with an ADL score ≤ 60, with indwelling catheters or tubes, and with pressure ulcers or unclean wounds. In contrast, being cared for by a caregiver was found to have no effect on postdischarge mortality risk (Table 5).

Table 5. - Clinical Factors Associated With 30-Day Postdischarge Mortality
Clinical Factor n 30-Day Postdischarge Mortality
Yes (n = 274) No (n = 7,522)
n % n % χ2
Male 0.197
 Yes 4,365 157 3.6 4,208 96.4
 No 3,431 117 3.4 3,314 96.6
≥75 years old 9.102**
 Yes 4,401 179 4.1 4,222 95.9
 No 3,395 95 2.8 3,300 97.2
Hospital stay ≥ 30 days 4.658*
 Yes 953 22 2.3 931 97.7
 No 6,843 252 3.7 6,591 96.3
Hospital stay ≥ 60 days 31.730***
 Yes 207 22 10.6 185 89.4
 No 7,589 252 3.3 7,337 96.7
Readmission within 14 days 21.090***
 Yes 954 58 6.1 896 93.9
 No 6,842 216 3.2 6,626 96.8
Readmission within 30 days 3.249
 Yes 686 21 3.1 665 96.9
 No 5,553 253 4.6 5,300 95.4
Enrollment criteria for discharge planning
 ADL score ≤ 60 120.125***
  Yes 4,787 255 5.3 4,532 94.7
  No 3,009 19 0.6 2,990 99.4
 With tubes 4.874*
  Yes 4,128 163 3.9 3,965 96.1
  No 3,668 111 3.0 3,557 97.0
 With pressure wound or unclear wound 19.997***
  Yes 692 45 6.5 647 93.5
  No 7,104 229 3.2 6,875 96.8
 Poorly controlled chronic diseases 0.657
  Yes 148 7 4.7 141 95.3
  No 7,648 267 3.5 7,381 96.5
 Financial problems 0.282
  Yes 17 1 5.9 16 94.1
  No 7,779 273 3.5 7,506 96.5
Major caregiver after discharge
 Living alone 2.962
  Yes 845 21 2.5 824 97.5
  No 6,951 253 3.6 6,698 96.4
 Spouse 0.570
  Yes 575 17 3.0 558 97.0
  No 7,221 257 3.6 6,964 96.4
 Family members 0.001
  Yes 1,168 41 3.5 1,127 96.5
  No 6,628 233 3.5 6,395 96.5
 Friends 2.275
  Yes 1,147 49 4.3 1,098 95.7
  No 6,649 225 3.4 6,424 96.6
 Hired caretaker 0.746
  Yes 2,578 84 3.3 2,494 96.7
  No 5,218 190 3.6 5,028 96.4
Note. ADL = activity of daily living.
*p < .05. **p < .01. ***p < .001.

Cox regression multivariate analysis showed that those with hospital stays over 30 days (hazard ratio = 2.145, 95% CI [1.37, 3.36]) and ADL scores ≤ 60 (hazard ratio = 3.101, 95% CI [1.91, 5.05]) had significantly fewer days between discharge and death within 30 days (Table 6).

Table 6. - Cox Regression Multivariate Analysis of Clinical Factors Affecting 30-Day Postdischarge Survival
Clinical Factor Wald HR 95% CI p
≥ 75 years old 0.085 1.040 [0.80, 1.35] .770
Hospital stay ≥ 30 days 11.166 2.145 [1.37, 3.36] .001
Readmission within 14 days 0.057 0.965 [0.72, 1.29] .811
ADL score ≤ 60 20.748 3.101 [1.91, 5.05] < .001
With tubes 16.333 0.603 [0.47, 0.77] < .001
With wounds 1.788 1.245 [0.90, 1.72] .181
Note. ADL = activity of daily living; HR = hazard ratio.


Discharge planning is an integral but ill-defined process in most acute care settings. Length of hospitalization, readmission rate, and discharge outcome are widely used indicators of hospital care quality. The results show that poor ADL scores (≤ 60), having indwelling catheters or tubes, and having unclean wounds had the greatest negative impacts on discharge planning efficacy. As the results of this study were mixed and complicated, further exploration of the ideal protocol and method for implementing the discharge planning process is still warranted.

The proportion of men in this study was relatively high (56.0%), which is consistent with that of overall hospitalization in Taiwan. According to the statistics of the National Health Insurance Administration, Ministry of Health and Welfare (2021), about 2 million people are hospitalized in Taiwan annually. The crude hospitalization rate for men over 45 years old is higher than that for women, and the average age of hospitalization has been trending consistently downward in recent years. Men more than women tend to self-perceive their health as good, which may contribute to their relatively lower use of medical resources, as determined by number of outpatient visits and the utilization rate of adult health examinations (Vlassoff, 2007). However, the hospitalization rate, hospitalization costs, and average cost per outpatient visit are higher in men than women (Damery & Combes, 2017), indicating that men seek medical assistance only after their illness is more serious, which may result in more follow-up care issues requiring attention (Hoffmann & Allers, 2016).

Most of the sample used in this study were aged 75 years or older. In general, older adults represent a large proportion of hospitalized patients and tend to have more comorbid chronic illnesses and disabilities and to require age-appropriate management to lessen the risk of adverse events during hospitalization. Older adults account for 18.52% of the population in Yunlin County, making it the most aged county in Taiwan (Ministry of the Interior, Taiwan, ROC, 2019). Most of the older adult patients in this study had functional dependencies, which required the help of another person to carry out daily household duties and provide assistance with basic needs. These characteristics of older adult homecare after hospitalization support the need for comprehensive functional assessment as part of discharge planning (Mabire et al., 2018). The LACE index has been shown to predict hospital readmissions and death with variable accuracy (van Walraven et al., 2010). However, this index has been shown to predict mortality and frequent readmissions at lower thresholds and at better accuracy in younger than older individuals. Age should be taken into account when using the LACE index to identify patients at a high risk (Fry et al., 2020).

Variables found to be independently associated with risk of readmission or death after discharge from hospital included length of stay (“L”), acuity of the admission (“A”), patient comorbidity (as measured using the Charlson comorbidity index score; “C”), and emergency department use (i.e., number of visits in the 6 months before admission; “E”). The LACE index may be used to quantify risk of death or unplanned readmission within 30 days after discharge from the hospital (van Walraven et al., 2010). Another prediction model, referred to as the HOSPITAL score, uses seven independent factors: hemoglobin at discharge, discharge from an oncology service, sodium level at discharge, procedure during the index admission, index type of admission, number of admissions during the last 12 months, and length of stay. This model was developed to identify the risk of potentially avoidable 30-day readmission in medical patients before discharge (Donzé et al., 2013). Both the LACE index and HOSPITAL score models have been used effectively to identify patients at a high risk of readmission (Su et al., 2020) and to predict mortality in multimorbid older patients (Aubert et al., 2022). However, the characteristics of these high-risk patients are not addressed in these models. In this study, eight criteria are used to determine the need for implementing discharge planning. These include ADL score less than 60, having indwelling catheters or tubes, having pressure ulcers or unclean wounds, having poor control of chronic diseases, readmission to the hospital within 14 days because of the same condition, having financial problems, and having major caregivers or living alone. Although these are high-risk indicators for long-term care needs, the primary task of implementing discharge planning is to identify high-risk cases early to help facilitate postdischarge planning (Hoyer et al., 2019). Although discharge planning has been promoted for many years in Taiwan, the number of recipients receiving discharge planning remains limited because of manpower availability and the priorities of each hospital. Comprehensive screening for major cases is insufficient in most institutions.

The findings of previous studies have shown discharge planning to achieve a small reduction in hospital length of stay for hospitalized older adults (Gonçalves-Bradley et al., 2016), and nursing diagnoses as the modality of admission and male gender have been shown to be significant determinants of length of hospital stay (D'Agostino et al., 2019). Results of this study indicate that being male, having an ADL score ≤ 60, and having indwelling catheters or tubes are significantly associated with longer hospital stays. Moreover, disease severity and complexity may prolong hospitalization duration. Aging is accompanied by various chronic conditions that often require hospitalization (Dharmarajan et al., 2016). In this study, we found that, whereas the ratio of > 75-year-old admissions was high, the proportion of hospitalizations that lasted over 30 or 60 days and were considered excessively long was relatively small, showing that long-term hospitalization factors for elderly patients relate to more complicated variables such as chronic diseases, medical history, and poor social support structure in addition to the basic physiological conditions of age and gender.

Decreasing the rate of hospital readmissions is a high-priority task in efforts to improve hospital healthcare quality. In Taiwan, the most common causes for patients being readmitted to the hospital are disease factors such as progressive chronic disease and the exacerbation of chronic conditions (Dai et al., 2002). Significant associations have been found between readmission factors and the demographic and clinical characteristics of patients (Coffey et al., 2019). Careful analysis has shown that case management promotes the efficacy of discharge planning in nursing units and also offers the potential for making improvements to further reduce patient readmission rates (Henke et al., 2017; Kucharczuk et al., 2022). In this study, being male, having indwelling catheters and tubes, and living alone or having different caregivers were associated with a higher risk of readmission. Discharge planning may not affect the proportion of patients discharged home rather than to residential care (Gonçalves-Bradley et al., 2016). In contrast, elderly patients and patients with lower ADL scores were found to have decreased readmission rates when they received hospital discharge planning. Lower readmission rates have been noted in the discharge planning group in studies of high-risk elderly patients with chronic medical conditions (Gonçalves-Bradley et al., 2016). Early interventions starting during the hospital stay and continuing after discharge are more effective in reducing readmissions (Braet et al., 2016; Luther et al., 2019). Therefore, discharge planning helps ensure a safe transition through improved clinician communication, patient education, information technology systems, involvement of community-based providers, and arrangements for prompt follow-up.

It remains uncertain whether discharge planning has an effect on postdischarge mortality, complication rates, or patient health status at follow-up (Gonçalves-Bradley et al., 2016; Khera et al., 2020). Early discharge planning with acutely admitted older adults has been associated with fewer hospital readmissions, but not with changes in length of hospital stay or mortality (Wu et al., 2019). However, discharge planning may positively affect the satisfaction of patients and healthcare professionals (Gonçalves-Bradley et al., 2016). The analysis in this study showed that patients aged ≥ 75 years, with prolonged hospital stays, with readmission within 14 days of discharge, with ADL scores ≤ 60, with indwelling catheters or tubes, or with pressure ulcers or unclean wounds experienced higher mortality rates within 30 days after discharge. In addition, type of caregiver was found to have no effect on postdischarge mortality, although a previous study found older adults with lower performance status and with caregivers to experience higher rates of postdischarge mortality (Rezaei-Shahsavarloo et al., 2020). In addition, prolonged hospital stays and readmission were found to be strongly associated with multiple comorbidities, which increases the risk of mortality after discharge (López Pardo et al., 2016; Wu et al., 2019).

This study was affected by several limitations. First, the retrospective study design used limits patients' long-term follow-up. In addition, database access was restricted by the care management information system database of the Ministry of Health and Welfare, with data access limited to the application list, application date, evaluation date, and application items. This limited the amount of follow-up data that could be accessed for the discharged patients. Second, selection bias may have influenced the case study and case selection. Importantly, we could not consistently distinguish among individual differences in discharge effectiveness because information on different patient experiences during admission or with death after discharge were not available for all patients. Thus, the generalization of results to other populations or geographic areas may be limited.

In conclusion, the results support that advanced age, underlying comorbidities, and specific disease factors decrease the efficacy of discharge planning. In addition, implementing case management in discharge planning was found to have no substantial effect on reducing the length of hospitalization or on the readmission status and prognosis of patients after discharge. However, the active intervention of discharge planning, emphasizing nurse-led early discharge planning and self-management education as well as instructional discharge letters and medication plans from attending physicians, will help prepare patients to resume daily life activities and maintain their health after discharge. Effective management of transitional care from the hospital to the community will help discharged patients and their family continue to conduct prescribed health recovery and promotion activities, access and use related healthcare resources after returning home, and improve overall quality of care.


This research was supported by the National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan (NTUHYL107.S011).

Author Contributions

Study conception and design: HWP, YWC

Data collection: HWP, FRL, HJC, MLH

Data analysis and interpretation: HWP, CYC, YWC

Drafting of the article: HWP, CYC, YWC

Critical revision of the article: JJH, YWC


Aubert C. E., Rodondi N., Terman S. W., Feller M., Schneider C., Oberle J., Dalleur O., Knol W., O'Mahony D., Aujesky D., Donzé J. (2022). HOSPITAL score and LACE index to predict mortality in multimorbid older patients. Drugs & Aging, 39(3), 223–234. https://doi.org/10.1007/s40266-022-00927-0
Braet A., Weltens C., Sermeus W. (2016). Effectiveness of discharge interventions from hospital to home on hospital readmissions: A systematic review. JBI Database of Systematic Reviews and Implementation Reports, 14(2), 106–173. https://doi.org/10.11124/jbisrir-2016-2381
Case Management Society of America. (2016). Standards of practice for case management. Author.
Coffey A., Leahy-Warren P., Savage E., Hegarty J., Cornally N., Day M. R., Sahm L., O'Connor K., O'Doherty J., Liew A., Sezgin D., O'Caoimh R. (2019). Interventions to promote early discharge and avoid inappropriate hospital (re)admission: A systematic review. International Journal of Environmental Research and Public Health, 16(14), Article 2457. https://doi.org/10.3390/ijerph16142457
D'Agostino F., Vellone E., Cocchieri A., Welton J., Maurici M., Polistena B., Spandonaro F., Zega M., Alvaro R., Sanson G. (2019). Nursing diagnoses as predictors of hospital length of stay: A prospective observational study. Journal of Nursing Scholarship, 51(1), 96–105. https://doi.org/10.1111/jnu.12444
Dai Y. T., Wu S. C., Weng R. (2002). Unplanned hospital readmission and its predictors in patients with chronic conditions. Journal of the Formosan Medical Association, 101(11), 779–785.
Damery S., Combes G. (2017). Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: A retrospective cohort study. BMJ Open, 7(7), Article e016921. https://doi.org/10.1136/bmjopen-2017-016921
Dharmarajan K., Strait K. M., Tinetti M. E., Lagu T., Lindenauer P. K., Lynn J., Krukas M. R., Ernst F. R., Li S. X., Krumholz H. M. (2016). Treatment for multiple acute cardiopulmonary conditions in older adults hospitalized with pneumonia, chronic obstructive pulmonary disease, or heart failure. Journal of the American Geriatrics Society, 64(8), 1574–1582. https://doi.org/10.1111/jgs.14303
Donzé J., Aujesky D., Williams D., Schnipper J. L. (2013). Potentially avoidable 30-day hospital readmissions in medical patients: Derivation and validation of a prediction model. JAMA Internal Medicine, 173(8), 632–638. https://doi.org/10.1001/jamainternmed.2013.3023
Fry C. H., Heppleston E., Fluck D., Han T. S. (2020). Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults. Internal and Emergency Medicine, 15(7), 1319–1325. https://doi.org/10.1007/s11739-020-02448-3
Gonçalves-Bradley D. C., Lannin N. A., Clemson L. M., Cameron I. D., Shepperd S. (2016). Discharge planning from hospital. Cochrane Database of Systematic Reviews, 2016(1), Article CD000313. https://doi.org/10.1002/14651858.CD000313.pub5
Henke R. M., Karaca Z., Jackson P., Marder W. D., Wong H. S. (2017). Discharge planning and hospital readmissions. Medical Care Research and Review, 74(3), 345–368. https://doi.org/10.1177/1077558716647652
Hoffmann F., Allers K. (2016). Age and sex differences in hospitalisation of nursing home residents: A systematic review. BMJ Open, 6(10), Article e011912. https://doi.org/10.1136/bmjopen-2016-011912
Hoyer E. H., Young D. L., Friedman L. A., Brotman D. J., Klein L. M., Friedman M., Needham D. M. (2019). Routine inpatient mobility assessment and hospital discharge planning. JAMA Internal Medicine, 179(1), 118–120. https://doi.org/10.1001/jamainternmed.2018.5145
Khera R., Wang Y., Bernheim S. M., Lin Z., Krumholz H. M. (2020). Post-discharge acute care and outcomes following readmission reduction initiatives: National retrospective cohort study of Medicare beneficiaries in the United States. British Medical Journal, 15, Article 368. https://doi.org/10.1136/bmj.l6831
Kucharczuk C., Lightheart E., Kodan A., Haynes C., Rabatin S., Burke J., Senger J., Lee L., Brinley S., Decena M. A., Cruz J. M., Hirsh R., McCauley K. (2022). Standardized discharge planning tool leads to earlier discharges and fewer readmissions. Journal of Nursing Care Quality, 37(1), 54–60. https://doi.org/10.1097/NCQ.0000000000000558
López Pardo P., Socorro García A., Baztán Cortés J. J. (2016). Influence of length of hospital stay on mortality after discharge in older patients with acute medical diseases. Gaceta Sanitaria, 30(5), 375–378. https://doi.org/10.1016/j.gaceta.2016.04.008
Luther B., Wilson R. D., Kranz C., Krahulec M. (2019). Discharge processes: What evidence tells us is most effective. Orthopaedic Nursing, 38(5), 328–333. https://doi.org/10.1097/NOR.0000000000000601
Mabire C., Dwyer A., Garnier A., Pellet J. (2018). Meta-analysis of the effectiveness of nursing discharge planning interventions for older inpatients discharged home. Journal of Advanced Nursing, 74(4), 788–799. https://doi.org/10.1111/jan.13475
Ministry of the Interior, Taiwan, ROC. (2019). Statistical yearbook of interior. https://www.moi.gov.tw/english/Default.aspx
Ministry of the Interior, Taiwan, ROC. (2021). Statistical yearbook of interior. https://www.moi.gov.tw/english/Default.aspx
National Health Insurance Administration, Ministry of Health and Welfare. (2021). Outpatient and inpatient medical benefit claims. https://www.nhi.gov.tw/english/
Nunes H. J., Queirós P. J. (2017). Patient with stroke: Hospital discharge planning, functionality and quality of life. Revista Brasileira de Enfermagem, 70(2), 415–423. https://doi.org/10.1590/0034-7167-2016-0166
Pellett C. (2016). Discharge planning: Best practice in transitions of care. British Journal of Community Nursing, 21(11), 542–548. https://doi.org/10.12968/bjcn.2016.21.11.542
Rezaei-Shahsavarloo Z., Atashzadeh-Shoorideh F., Gobbens R. J. J., Ebadi A., Ghaedamini Harouni G. (2020). The impact of interventions on management of frailty in hospitalized frail older adults: A systematic review and meta-analysis. BMC Geriatrics, 20(1), Article No. 526. https://doi.org/10.1186/s12877-020-01935-8
Su M. C., Wang Y. J., Chen T. J., Chiu S. H., Chang H. T., Huang M. S., Hu L. H., Li C. C., Yang S. J., Wu J. C., Chen Y. C. (2020). Assess the performance and cost-effectiveness of LACE and hospital re-admission prediction models as a risk management tool for home care patients: An evaluation study of a medical center affiliated home care unit in Taiwan. International Journal of Environmental Research and Public Health, 17(3), Article 927. https://doi.org/10.3390/ijerph17030927
United Nations. (2022). World population prospects 2022―Summary of results. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf
van Walraven C., Dhalla I. A., Bell C., Etchells E., Stiell I. G., Zarnke K., Austin P. C., Forster A. J. (2010). Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ: Canadian Medical Association Journal, 182(6), 551–557. https://doi.org/10.1503/cmaj.091117
Vlassoff C. (2007). Gender differences in determinants and consequences of health and illness. Journal of Health, Population, and Nutrition, 25(1), 47–61.
World Health Organization, Organisation for Economic Co-operation and Development, & The World Bank. (2018). Delivering quality health services: A global imperative for universal health coverage. https://www.who.int/publications/i/item/9789241513906
Wu Z., Kim M. S., Broad J. B., Zhang X., Bloomfield K., Connolly M. J. (2019). Association between post-discharge secondary care and risk of repeated hospital presentation, entry into long-term care and mortality in older people after acute hospitalization. Geriatrics & Gerontology International, 19(10), 1048–1053. https://doi.org/10.1111/ggi.13766
Xiao S., Tourangeau A., Widger K., Berta W. (2019). Discharge planning in mental healthcare settings: A review and concept analysis. International Journal of Mental Health Nursing, 28(4), 816–832. https://doi.org/10.1111/inm.12599

discharge planning; nursing care; hospital length of stay; readmission; survival

Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc.