Scheduled and Unscheduled Hospital Readmissions Among Patients With Diabetes

October 15, 2010
Hongsoo Kim, PhD, MPH
Hongsoo Kim, PhD, MPH

,
Joseph S. Ross, MD, MHS
Joseph S. Ross, MD, MHS

,
Gail D. Melkus, EdD, C-NP
Gail D. Melkus, EdD, C-NP

,
Zhonglin Zhao, MD, MPH
Zhonglin Zhao, MD, MPH

,
Kenneth Boockvar, MD, MS
Kenneth Boockvar, MD, MS

Volume 16, Issue 10

This population-based study examines socioeconomic and clinical factors associated with scheduled and unscheduled readmissions after discharge among older patients with diabetes.

Objectives:

To describe rates of scheduled and unscheduled readmissions among midlife and older patients with diabetes and to examine associated socioeconomic and clinical factors.

Study Design:

Population-based data set study.

Methods:

Using the 2006 California State Inpatient Dataset, we identified 124,967 patients 50 years or older with diabetes who were discharged from acute care hospitals between April and September 2006 and examined readmissions in the 3 months following their index hospitalizations.

Results:

About 26.3% of patients were readmitted within the 3-month period following their index hospitalizations, 87.2% of which were unscheduled readmissions. Patients with unscheduled readmissions were more likely to have a higher comorbidity burden, be members of racial/ethnic minority groups with public insurance, and live in lower-income neighborhoods. Having a history of hospitalization in the 3 months preceding the index hospitalization was also a strong predictor of unscheduled readmissions. Almost one-fifth of unscheduled readmissions (constituting approximately 27,500 inpatient days and costing almost $72.7 million) were potentially preventable based on definitions of Prevention Quality Indicators by the Agency for Healthcare Research and Quality. Scheduled readmissions were less likely to occur among patients 80 years or older, the uninsured, and those with an unscheduled index hospitalization.

Conclusions:

The predictors of scheduled and unscheduled readmissions are different. Transition care to prevent unscheduled readmissions in acutely ill patients with diabetes may help reduce rates, improving care. Further studies are needed on potential disparities in scheduled readmissions.

(Am J Manag Care. 2010;16(10):760-767)

This study used a population-based data set to examine socioeconomic and clinical factors associated with scheduled and unscheduled readmissions after hospital discharge among older patients with diabetes.

  • A substantial percentage (12.8%) of readmissions were scheduled at least 24 hours in advance.
  • The causes and predictors of scheduled and unscheduled readmissions were distinct, suggesting that scheduled readmissions should be separated from unscheduled readmissions in studies of readmission.
  • The lower likelihood of scheduled readmissions among older and uninsured patients implies less access to advanced coordinated healthcare services for these groups.

Diabetes mellitus affects about 23.6 million individuals in the United States and is the seventh leading cause of death.1 Because of the complexity of the disease and its management, patients with diabetes are more likely to use healthcare services than the general population,1 with a total direct medical cost of about $116 billion and a mean cost that is 2.3 times higher than that of individuals without diabetes.1,2 Moreover, almost 6.3 million hospital stays in 2004 were by patients with diabetes as a principal or coexisting condition, costing almost $57.8 billion, or about 20% of total hospital costs, while hospitalizations principally for diabetes cost about $3.9 billion.2

Many of these hospital stays included readmissions of the same patients, and hospital readmission has been a major quality concern in diabetes care. However, there are few population-based studies on readmission among patients with diabetes and its risk factors. Jiang et al3 reported that more than 30% of older patients with diabetes were readmitted during the same year as their index hospitalization in 5 states in 1999. Readmissions accounted for 55.2% of total hospital stays, and the mean total hospital cost for patients with readmissions was 2.5 times higher than that for patients without readmissions. They also reported a high risk of readmission among racial/ethnic minority groups and variations in readmission rates by insurance type.4,5 Robbins et al6 and Robbins and Webb7,8 observed racial/ethnic disparities among hospital readmissions in Pennsylvania hospitals between 1994 and 2001 and noted the positive effect of public health clinics in reducing hospital readmissions among low-income patients with diabetes.

A gap in the literature on hospital readmissions among patients with diabetes has been the lack of differentiation between scheduled and unscheduled readmissions.3-5 Readmissions are often assumed to be unscheduled, but this may not be true, especially for patients with existing complex conditions such as diabetes. The need for attention to the type of readmission has been raised,9 but few studies6,7 have explicitly focused on unscheduled readmissions only. No study seems to have assessed what the reasons for and the factors associated with scheduled readmissions are, and whether they are similar to those associated with unscheduled readmissions.

Another gap in the literature is that studies have often focused on readmissions within 1 month of hospital discharge6,7,10 or on readmissions among Medicare patients.10,11 This short observation window is because hospital readmission has been widely studied in terms of its relationship to hospital performance.7,12 However, this may be too narrow an approach to evaluate readmission risk from the perspective of patients, for whom avoiding a later readmission is as important as avoiding an early readmission. Investigations focusing on patient risk for readmission have often used a 3-month window following hospital discharge,5 which we adopted.

The objectives of this study were to examine and compare the demographic, socioeconomic, and clinical factors associated with scheduled and unscheduled readmissions in midlife and older patients with diabetes using a population-based data set from California. We also examined common reasons for scheduled and unscheduled readmissions and estimated the cost and extent of potentially preventable readmissions using Prevention Quality Indicators (PQIs) of the Agency for Healthcare Research and Quality (AHRQ).13

METHODS

We analyzed the 2006 California State Inpatient Dataset (SID), developed as part of the Healthcare Cost and Utilization Project (HCUP) sponsored by the AHRQ.14 The publicly available California SID includes hospital discharge abstract data (such as demographic characteristics, clinical information, resource use, and payers) from short-term, nonfederal, general, and specialty hospitals. California was selected for the analysis because it has a large and ethnically/racially diverse population15 and provides in its SID an encrypted unique patient identifier (UPI) that is used to link hospital readmissions to a distinct patient. The California Office of Statewide Health Planning and Development (OSHPD)16 routinely monitors the quality of hospital inpatient data and corrects or edits the data if necessary. They also ask reporting institutions to make corrections when their reports do not meet the error tolerance level (now 2%) that the state has established.

Our sample included patients 50 years or older with diabetes as the principal or a secondary diagnosis, identified using the AHRQ’s Clinical Classifications Software (CCS 45 [diabetes mellitus without complications] and CCS 50 [diabetes mellitus with complications]),17 all of whom were admitted to California hospitals at least once between April and September 2006. We defined the first admission as the index hospitalization and identified readmissions within 3 months of the index hospitalization using the encrypted UPI. As did Jencks et al,10 we counted no more than 1 readmission for each patient and focused on the characteristics of the first readmission (ie, scheduled or unscheduled) in the analysis. Similar to the approach by Jiang et al,3 we excluded patients with a missing UPI, patients with the same UPI but with an inconsistent age (differing by >1 year) or sex, and patients with a missing admission or discharge month.

The outcome variables of this study were scheduled and unscheduled readmissions. We used the SCHED variable in the California SID,18 which refers to whether an admission is scheduled at least 24 hours in advance. Therefore, we defined unscheduled readmissions as readmissions that were not scheduled at least 24 hours in advance and scheduled readmissions as those that were scheduled at least 24 hours in advance. The SCHED variable is derived from the type of admission in the California hospital inpatient data report collected by the California OSHPD, from which the California SID is developed for the HCUP. The California OSHPD16 provides detailed guidelines for each variable in the report, including the type of admission, and conducts multistep validation checks and edits.

We examined patient sociodemographic and clinical characteristics that were potentially associated with readmission variables, representing the 3 major categories of the Behavioral Model of Health Services Use.19 This model posits that potential and realized health services use is determined by interactions among predisposing, enabling, and need characteristics of individuals and the healthcare systems in the communities where they reside. Predisposing factors are individual characteristics existing before illness that determine the likelihood of seeking care and are measured by age, sex, and race/ethnicity. Enabling factors provide the means to use health services and include individual-level and community-level factors measured by primary payer type, resident location, and neighborhood median income. The California SID does not provide the patient’s exact income level, so we used as a proxy variable the median household income for the patient’s zip code in 2006. Perceived health status and evaluated health status determine the need for health services measured by comorbidities, prior hospitalization history, and variables related to index hospitalizations. The severity of conditions was measured by the number of chronic conditions in the 18 body systems using the Chronic Condition Indicator developed by the AHRQ.20 We counted only 1 chronic condition per body system, as did Wolff et al,21 although some patients might have had more than 1.

For data analysis, we first computed unadjusted rates of scheduled and unscheduled readmissions within 3 months of the index hospitalization by patient demographic, socioeconomic, and clinical characteristics. Second, logistic regression models were developed to examine the predictors of scheduled and unscheduled hospital readmissions. The reference group for odds ratios was patients with no readmissions in the 0 to 3 months following their index hospitalizations. Third, we compared common clinical conditions between the scheduled and unscheduled readmission groups using the CCS for the International Classification of Diseases, Ninth Revision, Clinical Modification developed by the AHRQ.17 The CCS is a uniform and standardized coding system for aggregating more than 14,000 diagnosis codes into smaller, mutually exclusive, clinically meaningful categories. We ran a single-level CCS algorithm against our data, which categorized all the diagnoses into about 300 clinical conditions, among which we selected and compared the top 15 conditions for the scheduled and unscheduled readmission groups.

Fourth, the effects of potentially preventable unscheduled readmissions were examined using the length of stay and hospital cost. Potentially preventable readmissions refers to hospital readmissions for ambulatory care—sensitive conditions that should not require in-hospital treatment if better discharge planning, care coordination, and timely appropriate outpatient care are provided.5,22 We defined potentially preventable readmissions based on the following 8 PQIs of the AHRQ that are applicable to readmissions among older individuals22: bacterial pneumonia, chronic obstructive pulmonary disease, dehydration, congestive heart failure, hypertension, short-term diabetic complications, uncontrolled diabetes, and urinary tract infection. Length of stay was obtained from the California SID, and hospital cost per readmission was computed by multiplying hospital charges per readmission from the California SID by the ratio of cost to charge provided by the HCUP.14 Total hospital cost for a certain PQI condition was obtained by summing hospital costs for all unscheduled readmissions for the PQI condition. The mean hospital cost was computed by dividing the total hospital cost by the total number of potentially preventable unscheduled readmissions. The grand total cost of potentially preventable unscheduled readmissions was the sum of the total hospital costs for unscheduled readmissions for all 8 PQI conditions observed in this study.

RESULTS

Table 1

Our sample included 124,967 patients 50 years or older with diabetes who were discharged alive from California hospitals between April and September 2006. Most were aged between 65 and 79 years, female, of white race/ethnicity, and Medicare beneficiaries (). The patients had on average 3.8 chronic conditions each (data not shown); about 16.2% had a hospitalization history in the 3 months before the index hospitalization; and only about one-fifth (18.4%) of their index hospitalizations were scheduled at least 24 hours in advance. The most common reason for the index hospitalization was congestive heart failure (7.9%), followed by diabetes mellitus with complications (7.5%), coronary atherosclerosis (4.9%), and pneumonia (4.1%).

Approximately 26.3% (n = 32,857) of patients were readmitted within 0 to 3 months of their index hospitalizations, and most readmissions (87.2%) were unscheduled (Table 1). The risks for unscheduled and scheduled readmissions varied by patients’ demographic, socioeconomic, and clinical characteristics. Adjusting for other factors, patients 80 years or older were slightly more likely than those aged 50 to 64 years to have an unscheduled readmission. Blacks and Hispanics were also more likely to have an unscheduled readmission than whites. Patients with public insurance had a higher risk for an unscheduled readmission than patients with private insurance. Patients residing in urban areas versus rural areas and those residing in lower-income neighborhoods versus higher-income neighborhoods had higher risks for an unscheduled readmission. The risk for an unscheduled readmission consistently increased as the number of chronic conditions that a patient had increased: patients with 7 or more chronic conditions were almost 3 times more likely to have an unscheduled readmission than those with diabetes only. An unscheduled readmission was more likely to occur among patients who had had 1 or more hospitalizations in the 3 months preceding the index hospitalization. Also, the risk for an unscheduled readmission increased when the index hospitalization was an unscheduled admission or when it ended with a transfer to another postacute or long-term institution. As length of stay rose, the likelihood of an unscheduled readmission increased.

Some factors predicting unscheduled readmissions also predicted scheduled readmissions in the same way: compared with men, women were less likely to have a scheduled readmission, but individuals with Medicare as the primary payer were more likely to have a scheduled readmission (Table 1). The number of chronic conditions and length of stay also positively predicted the odds for both unscheduled and scheduled readmissions. Two other factors predicted both scheduled and unscheduled readmissions, but the directions were opposite: individuals 80 years or older versus those aged 50 to 64 years and individuals with unscheduled index hospitalizations versus scheduled index hospitalizations were more likely to have an unscheduled readmission but were less likely to have a scheduled readmission. In contrast, factors that positively predicted an unscheduled readmission but not a scheduled readmission were racial/ethnic minority group status (vs white), Medicaid as the primary payer (vs private insurance), resident location, median income of the neighborhood, and disposition destination. Uninsured status negatively predicted a scheduled readmission only compared with individuals having private insurance.

Table 2

The most common 15 diagnoses for unscheduled and scheduled readmissions, covering more than 50% of all readmissions, are listed in . The most frequent condition among patients with an unscheduled readmission was congestive heart failure (8.8%), followed by diabetes mellitus with complications (7.2%), septicemia (5.7%), and pneumonia (3.9%). Among patients with scheduled readmissions, diabetes mellitus with complications was the third most common diagnosis (7.1%), following coronary atherosclerosis (11.4%) and complications of device, implant, or graft (7.3%). Only 6 of 15 conditions (congestive heart failure, diabetes mellitus with complications, complications of surgical procedures or medical care, coronary atherosclerosis, cardiac dysrhythmias, and complications of device, implant, or graft) were common to both unscheduled and scheduled readmissions. In addition, 3 of those 6 conditions (congestive heart failure, coronary atherosclerosis, and complications of device, implant, or graft) were in very different ranks between unscheduled and scheduled readmissions.

Table 3

Last, about 19.0% of unscheduled readmissions (n = 5432) were potentially preventable (). Among 8 conditions that can be managed in the ambulatory setting according to the AHRQ’s PQI definitions, congestive heart failure, bacterial pneumonia, and urinary tract infection were the 3 most frequent among our sample of hospitalized midlife and older patients with diabetes. These potentially preventable unscheduled readmissions constituted approximately 27,500 inpatient days and cost almost $72.7 million. Among 4208 patients with scheduled readmissions, only 140 (3.3%) were categorized as potentially preventable using the same PQI definitions; these comprised about 862 inpatient days and $1.9 million (data not shown).

DISCUSSION

In this population-based study using the 2006 California SID, more than 1 in every 4 (26.3%) hospitalized midlife and older patients with diabetes were readmitted within 3 months of their index hospitalization, and most readmissions (87.2%) were unscheduled, which implies potential issues in quality and coordination of care for this vulnerable population.23 Unscheduled readmissions were disproportionately higher for low-income, high-comorbid, racial/ethnic minority patients with diabetes having public insurance and living in urban areas. Disparities in diabetes care have been reported due to lower access to preventive and primary care and a higher rate of hospitalization among socioeconomically disadvantaged groups.6,24

Our study confirms that such disparities result in discrepancies in unscheduled readmissions. Disparities in hospital readmissions among patients with diabetes were reported in studies2,3,7 analyzing data from the 1990s, and our study demonstrates that such disparities continue.

We found that about one-fifth of unscheduled readmissions were potentially preventable, for which approximately 27,500 inpatient days and $72.7 million were spent. Those readmissions might have been avoided if good inpatient and transition care and outpatient follow-up had been provided.5,6 Considering the large variations among states in readmission rates, lengths of stay, and costs for inpatient care, it would be difficult to determine national estimates based on our data. Nevertheless, the economic burden of caring for individuals with diabetes, along with patient-level safety concerns related to unscheduled readmissions, reinforces the need for continuing efforts to innovate transition and chronic care models and the policies necessary to support them.

Our study is the first population-based study to date reporting that the predictors of scheduled and unscheduled hospital readmissions among patients with diabetes are dissimilar. Several patient demographic and socioeconomic factors (such as race/ethnicity, living in an urban area, and residing in a lower-income neighborhood) predicted unscheduled readmissions, but none predicted scheduled readmissions. Having no insurance, experiencing an unscheduled index hospitalization, and being among the older elderly (>80 years) decreased the odds of a scheduled readmission. A study by Kossovsky et al25 seems to be the only published article to date on scheduled and unscheduled readmissions. Examining 31-day readmissions among patients discharged from medical units in a single hospital in Geneva, Switzerland, over 1 year between 1995 and 1996, the authors reported findings consistent with ours: individuals 75 years or older and those admitted through the emergency department for an index hospitalization in their study were less likely to have a scheduled readmission.

This is important because scheduled readmissions may be an indicator of better access to advanced procedures or treatments (eg, elective chemotherapy, radiation therapy, or inpatient diagnostic procedures) and to better care coordination (ie, the patient has a relationship with a provider who arranges a scheduled admission, avoiding an emergency admission). If so, controlling for all other factors (including comorbidities), the lower rates of scheduled readmissions among the older elderly (>80 years), the uninsured, and patients with unscheduled index hospitalizations suggest adverse disparity in care for these groups. Therefore, more empirical studies are needed on the extent and characteristics of scheduled readmissions and on potential disparities in receiving scheduled readmissions.

Limitations of this study should be mentioned. We analyzed publicly available hospital discharge data sets; as in any secondary data analysis, inaccurate or missing codes may exist, limiting the reliability of reports of scheduled and unscheduled readmissions (the type of admission variable) and of comorbidities. We counted all-cause readmissions and could not determine whether and to what extent a readmission was related to an index admission, which would be an important topic for future research. The validity of PQIs as the readmission quality indicators should be evaluated further. Because the data set does not allow the linking of patient data across years, we examined 1 year of data. We had data on discharge and admission months, but exact dates were unavailable. No detailed data were available on the type and severity of diabetes or on the availability of primary care physicians and discharge planning services. Last, we analyzed California data only.

Despite these limitations, this study addressed several gaps in the literature on hospital readmissions of patients with diabetes. Using a recent large population—based data set that included midlife and older patients with diabetes having all types of insurance, we reported several socioeconomic and clinical factors associated with scheduled and unscheduled readmissions and showed that predictors of the 2 types of readmissions were not parallel. The lower rates of scheduled readmissions in the older and uninsured groups imply potential lower access to advanced coordinated follow-up services for these groups. The consistently prevalent unscheduled readmissions among midlife and older patients with diabetes provide a rationale for continuing efforts to improve the organization and process of acute and chronic diabetes care and to promote quality and timely primary care, thereby ultimately saving unnecessary social costs and ensuring patient safety.

Acknowledgment

We thank Dr Robert Norman at New York University College of Dentistry, New York City, for his guidance in statistical analysis.

Author Affiliations: Graduate School of Public Health and Institute of Health and Environment (HK), Seoul National University, Seoul, South Korea; James J. Peters VA Medical Center (JSR, KB), Bronx, NY; Mount Sinai School of Medicine (JSR, KB), New York, NY; College of Nursing (GDM) and College of Dentistry (ZZ), New York University, New York, NY.

Funding Source: The authors report no external funding for this research.

Author Disclosures: The authors (HK, JSR, GDM, ZZ, KB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (HK, JSR, GDM, ZZ, KB); acquisition of data (HK); analysis and interpretation of data (HK, JSR, GDM, ZZ, KB); drafting of the manuscript (HK, GDM); critical revision of the manuscript for important intellectual content (HK, JSR, KB); and statistical analysis (HK).

Address correspondence to: Hongsoo Kim, PhD, MPH, Graduate School of Health and Environment, Seoul National University, 599 Kwanak-Ro, Kwanak-Gu, Seoul, 151-742, South Korea. E-mail: hk65.snu@gmail.com.

1. Centers for Disease Control and Prevention. Diabetes public health resource: 2007 national diabetes fact sheet. http://www.cdc.gov/diabetes/ pubs/estimates07.htm#1. Accessed January 5, 2010.

2. Healthcare Cost and Utilization Project. Statistical brief #17: hospital stays among patients with diabetes, 2004. http://www.hcup-us.ahrq. gov/reports/statbriefs/sb17.pdf. Accessed January 5, 2010.

3. Jiang HJ, Stryer D, Friedman B, Andrews R. Multiple hospitalizations for patients with diabetes. Diabetes Care. 2003;26(5):1421-1426.

4. Jiang HJ, Friedman B, Andrews R. Changes in hospital readmissions for diabetes-related conditions. Differences by payer. Manag Care Interface. 2008;21(1):24-30.

5. Jiang HJ, Andrews R, Stryer D, Friedman B. Racial/ethnic disparities in potentially preventable readmissions: the case of diabetes. Am J Public Health. 2005;95(9):1561-1567.

6. Robbins JM, Valdmanis VG, Webb DA. Do public health clinics reduce rehospitalizations?—the Urban Diabetes Study. J Health Care Poor Underserved. 2008;19(2):562-573.

7. Robbins JM, Webb DA. Diagnosing diabetes and preventing rehospitalizations: the urban diabetes study. Med Care. 2006;44(3):292-296.

8. Robbins JM, Webb DA. Hospital admission rates for a racially diverse low-income cohort of patients with diabetes: the Urban Diabetes Study. Am J Public Health. 2006;96(7):1260-1264.

9. Smith DM. Predicting readmissions of elderly patients. J Gen Intern Med. 1991;6(5):483-484.

10. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428.

11. Aranda JM Jr, Johnson JW, Conti JB. Current trends in heart failure readmission rates: analysis of Medicare data. Clin Cardiol. 2009;32(1):47-52.

12. Balla U, Malnick S, Schattner A. Early readmissions to the department of medicine as a screening tool for monitoring quality of care problems. Medicine (Baltimore). 2008;87(5):294-300.

13. Agency for Healthcare Research and Quality (AHRQ) Quality Indicators. Prevention Quality Indicators: technical specifications. http:// www.qualityindicators.ahrq.gov/archives/pqi/pqi_technical_specs_v32. pdf. Accessed January 8, 2010.

14. Agency for Healthcare Research and Quality. Introduction to the HCUP State Inpatient Databases (SID). http://www.hcup-us.ahrq.gov/ db/state/siddist/Introduction_to_SID.pdf. Accessed September 7, 2010.

15. US Census Bureau. Select fact sheet by state. http://factfinder. census.gov/jsp/saff/SAFFInfo.jsp?_pageId=gn10_select_state. Accessed January 8, 2010.

16. California Office of Statewide Health Planning and Development. California Inpatient Data Reporting Manual. 7th ed. Version 7.1. March 2010. http://www.oshpd.ca.gov?HID/MIRCal/Text_pdfs/ManualsGuides/ IPManual/TofC.pdf. Accessed June 8, 2010.

17. Agency for Healthcare Research and Quality. Release of the Fiscal Year 2009 Clinical Classifications Software (CCS) for ICD-9-CM and CCS-MHSA (January 2009). http://www.hcup-us.ahrq.gov/news/ announcements/ccs_0109.jsp. Accessed December 15, 2009.

18. Healthcare Cost and Utilization Project State Inpatient Databases (SID). Central distributor SID: availability of data elements. 2006. http://www.hcup-us.ahrq.gov/db/state/siddist/siddistvarnote2006.jsp. Accessed December 18, 2009.

19. Andersen RM. National health surveys and the Behavioral Model of Health Services Use. Med Care. 2008;46(7):647-653.

20. Healthcare Cost and Utilization Project. Chronic Condition Indicator (CCI) for ICD-9-CM. http://www.hcup-us.ahrq.gov/toolssoftware/ chronic/chronic.jsp. Accessed December 16, 2009.

21. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162(20):2269-2276.

22. Werner RM, Konetzka RT, Stuart EA, Norton EC, Polsky D, Park J. Impact of public reporting on quality of postacute care. Health Serv Res. 2009;44(4):1169-1187.

23. MEDPAC. Payment policy for inpatient readmissions. In: Report to the Congress: Promoting Greater Efficiency in Medicare. Washington, DC: Medicare Payment Advisory Commission; June 2007. pp. 103-120.

24. Niefeld MR, Braunstein JB, Wu AW, et al. Preventable hospitalization among elderly Medicare beneficiaries with type 2 diabetes. Diabetes Care. 2003;26(5):1344-1349.

25. Kossovsky MP, Perneger TV, Sarasin FP, Bolla F, Borst F, Gaspoz JM. Comparison between planned and unplanned readmissions to a department of internal medicine. J Clin Epidemiol. 1999;52(2):151-156.