Effectiveness of a Brief Care Management Intervention for Reducing Psychiatric Hospitalization Readmissions Carole Taylor, MSN, RN Brandi Holsinger, MSW, LCSW Jenny V. Flanagan, MA, LPC Amanda M. Ayers, MSPH Shari L. Hutchison, MS Lauren Terhorst, PhD Abstract This study examines a recovery-focused care management bridging strategy implemented during time of inpatient stay with the goal to increase engagement in aftercare and reduce early psychiatric readmissions. The sample included 195 individuals who received care from a large psychiatric specialty hospital. Eighty-seven individuals were assigned to receive the intervention, while 108 individuals were assigned to the control group. Individuals in the intervention group received a brief interview prior to inpatient discharge plus usual care, and individuals in the control group received usual care. After controlling for age, living situation, and utilization, individuals in the control group were more likely to be readmitted within 30 days of an index readmission than individuals in the intervention group (OR=2.44, p=.02). Bridging strategies utilized prior to discharge for individuals at higher risk of early mental health inpatient readmission may be used as an effective alternative to more costly interventions.
Introduction Repeated psychiatric hospitalizations are a burden to both individuals in treatment and the health-care delivery system due to high costs, disruption of individuals’ lives, and undesirable Address correspondence to Carole Taylor, MSN, RN, Community Care Behavioral Health Organization, One Chatham Center, Suite 700, 112 Washington Place, Pittsburgh, PA 15219, USA. Email: [email protected]. Brandi Holsinger, MSW, LCSW, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Jenny V. Flanagan, MA, LPC, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Amanda M. Ayers, MSPH, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Shari L. Hutchison, MS, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Lauren Terhorst, PhD, Community Care Behavioral Health Organization, Pittsburgh, PA, USA.
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Journal of Behavioral Health Services & Research, 2014. 1–9. c 2014 National Council for Behavioral Health. DOI 10.1007/s11414-014-9400-4
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consequences such as a decreased ability to live independently.1,2 Individuals with repeated hospitalizations are considered high risk for readmission within 1 year of a prior discharge as one factor that has been shown to predict readmission rates is the number of previous psychiatric hospitalizations.2–5 Furthermore, individuals with repeated psychiatric hospitalizations are more vulnerable to an “early readmission,” that is, a readmission within 30 to 90 days of discharge.6,7 Because the time period immediately after discharge is one of high susceptibility to readmission, utilization of timely interventions and bridging strategies aimed at improving continuity of care and decreasing readmissions can provide needed support by facilitating the transition from inpatient to outpatient care.8–11 Previous research has established that a small number of inpatient users consume a disproportionately large amount of total inpatient resources,3,12–14 and the advent of managed care has increased attention to individuals with costly and inappropriate service use.14 In a study of a managed care population, it was found that 31.2% of all inpatient users accounted for 75% of paid Medicaid claims, and only 6.9% of clients accounted for 42% of total inpatient admissions during the study period.13 Use of managed care organization (MCO) care managers to facilitate bridging strategies known to improve connection to aftercare may be effective in reducing early readmissions; however, prior studies of MCO care management initiatives have yielded varying results. Receipt of care management and other services provided by MCOs has been found to increase the number of individuals receiving community-based mental health services.15 Cuffel15 examined the effects of enhanced and intensive discharge planning versus usual care on readmission in a managed behavioral health-care organization. While there were no differences between the usual care and discharge planning groups, individuals discharged to partial hospitalization or those who were authorized for immediate care but did not attend were more likely to be readmitted within 30, 60, and 180 days. Nelson10 found that hospitalized individuals who kept at least one community-based mental health service appointment following discharge were half as likely to be readmitted as those who did not receive any outpatient treatment. Time was a critical factor for the individuals who did not keep an appointment; that is, as time since the initial admission increased, the rate of readmission increased. As the time since the initial admission passes, different factors impact readmission rates, such as cyclical course of mental illness, access to support, and environmental factors.8 Effective care management interventions to connect individuals to community-based treatment are often timely, occurring during the inpatient stay and/or immediately following discharge. One such model of engagement is Critical Time Intervention (CTI).16–18 CTI has been found to be effective in linking individuals to outpatient treatment19 and preventing psychiatric rehospitalization.20 The traditional CTI model is a 9-month care coordination intervention that is designed to smooth the transition from institutional to community living18; a 3-month version of the model (BCTI) has also been found effective in transitioning individuals from inpatient psychiatric care to community living.19 Though highly effective, the model requires that care coordinators with small caseloads work with individuals for a minimum of 3 months.18 Care coordinators may not have the time or resources to deliver such an intensive model to all of the individuals they serve. Therefore, research into brief, less resource-intensive interventions is warranted. Bridging strategies provide a means to explore aspects of clinical and planning issues. Lyons21 suggested that readmissions may be associated with a lack of discharge planning and failure to monitor outpatient follow-up. Bridging strategies implemented prior to discharge can connect individuals with appropriate outpatient services.22 Boyer et al. found that communication about discharge plans between inpatient and outpatient staff, starting outpatient programs before discharge, and family involvement during hospital stay increased the odds of successful linkage to outpatient care.22 Additionally, the incorporation of bridging strategies may help to identify individuals in need of extra support. The individuals may be discharged to psychiatric day or partial hospitalization programs,23,24 which have served as effective alternatives to conventional
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inpatient care.25 Durbin8 suggested that standardization of the discharge planning approach would provide a consistent review of planning around issues such as clinical acuity, medication management, and postdischarge care needs. The current prospective study was an examination of a recovery-focused bridging strategy intervention during the time of inpatient stay with the goal to increase engagement in aftercare and reduce early psychiatric readmissions. The intervention consisted of a brief interview that addressed goals and barriers to treatment which was administered by care managers of a managed behavioral health organization prior to the individuals’ discharge. The hypothesis was that individuals who received the intervention would have lower rates of 30-day readmissions than individuals who did not receive the intervention. The 30-day span was utilized to explore the effectiveness of the recovery-focused bridging strategy on early readmission rates. Additionally, the impact of demographic and service utilization factors on early readmission rates was explored. This study adds to the existing literature because it explores the impact of a novel recovery-focused bridging strategy intervention administered prior to discharge in addition to clinical and demographic factors on 30-day readmission rates.
Method Sample Data were obtained from adults readmitted to inpatient psychiatric care within 30 days prior to the index readmission. The study sample included 195 individuals who received care from a large psychiatric specialty hospital between April 2011 and November 2012. Medicaid-eligible adults who were readmitted to an inpatient psychiatric hospital within 30 days of a prior psychiatric hospital admission were identified as individuals eligible for the intervention. Individuals unavailable to participate in the interview due to medical issues, intellectual difficulties, or clinical milieu were excluded from the intervention. Procedure Care managers of a managed behavioral health-care organization (MBHCO) identified individuals with a psychiatric inpatient 30-day readmission (index readmission) and conducted a brief intervention to address barriers and motivations for continuing treatment and facilitate referrals and information for aftercare. Each day, the high-risk care management team received automated notifications of individuals who were readmitted to an inpatient psychiatric hospital within 30 days of a prior psychiatric hospital admission. Eight care managers with clinical experience conducted in-person interviews with readmitted individuals at several units of one psychiatric hospital. A unit that specialized in schizophrenia disorders and another that specialized in co-occurring mental health and substance abuse disorders were included as well as several units for the general adult population. A quasi-experimental, two-group design was utilized; one group was assigned to the interview (intervention group), and the other was assigned to usual care (control group). The assignment to the interview group was based on an alternating week schedule, resulting in 87 individuals assigned to receive the interview and 108 individuals not assigned to receive the interview based on their readmission date. Activities were approved by the University of Pittsburgh Institutional Review Board as a quality improvement initiative.
Intervention Development and Protocol The intervention was developed by the first author with the intent to reduce readmission rates and promote effective engagement postdischarge. The interview tool was based on several key
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elements of recovery models and focuses on the opportunities for self-improvement through the three cornerstones of recovery—hope, willingness, and responsible action.26 The intervention was created with the purpose of developing a nonintrusive, nonthreatening bridging strategy to build a rapport (willingness of the individual to meet and discuss options) between the care manager and the individual prior to discharge. The interview promotes self-reflection by the individual and stresses that care and recovery must not stop at hospital discharge but must continue in the community (responsible action). The on-site intervention was a one-time semistructured interview which lasted approximately 10– 20 min. The face-to-face interview consisted of six open-ended discussion areas as follows: (1) the reason for the current admission, (2) barriers to increasing community tenure, (3) how barriers to community tenure may be overcome, (4) discussion and use of a crisis plan, (5) factors that would help to keep the individual safe, and (6) needs during inpatient stay that would assist with a transition to the community. These six elements provide the individual an opportunity to develop a responsible plan of action and hope for continued community tenure. Additionally, the care manager would coordinate with the individuals’ current community-based case management team or assess to refer to an acute service care coordinator. All topics were addressed during each interview, but each session was unique because it was driven by the individual’s goals and needs. This is consistent with a key principle of recovery that each person’s experiences and needs are unique.26 The intervention group received the recovery-focused interview plus usual care, while the control group received usual care. Usual care included discharge planning by hospital staff with referrals to behavioral health services such as drug and alcohol rehabilitation, case management supports (service coordination), and a referral to appropriate housing for the member. In the control condition, care managers did not meet with individuals during the index readmission but did help to facilitate appropriate aftercare.
Measures Sociodemographic characteristics and mental health service utilization Sociodemographic information and behavioral health service utilization in the 30 days following the index readmission were obtained through administrative data and paid behavioral health claims. Sociodemographic variables included gender, race, and age. Additional variables of interest were obtained from care management administrative data including commitment status (voluntary/ involuntary), diagnosis (dual diagnosis/no dual diagnosis), living situation (homeless/not homeless), and legal status (legal issues/no legal issues). Dual diagnosis was defined as a substance use disorder diagnosis in conjunction with a mental health diagnosis. Readmission Readmission was determined based on information from paid behavioral health service claims. Individuals with a psychiatric hospitalization within 30 days of the index readmission were identified as readmitted, while those without a psychiatric hospitalization were labeled as not readmitted, forming a dichotomous outcome variable.
Statistical Analysis The first readmission for individuals with multiple 30-day readmissions during the evaluation period was selected so that each unique individual was represented in the data analyses only once. Sociodemographic characteristics were first explored to determine if group distributions (intervention vs. control) were equitable using chi-square tests. Additionally, chi-square tests were
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utilized to compare characteristics between those readmitted versus those not readmitted. Next, a logistic regression model was formed using variables with a significant association with readmission in our sample as predictors and the dichotomous variable of readmission (yes=1, no=0) as the outcome and group (intervention vs. control) as the independent variable, controlling for any variables that had significant between-group differences. The Hosmer-Lemeshow (H-L) test statistic was utilized as a goodness-of-fit test, while the Likelihood Ratio, Score, and Wald tests were examined to evaluate the model.27 A nonsignificant H-L test provides evidence that the model fits the data appropriately, while significant likelihood ratio, score, and Wald tests determine that the model with covariates is more appropriate than the null (intercept only) model.28 Two additional measures of fit were produced: the Cox and Snell29 R2 and the Nagelkerke30 R2. These estimates are not interpretable in the same manner as the R2 of an ordinary least squares regression and are used as complementary measures to the model fit tests. The c statistic measure of association was assessed to determine the degree to which predicted probabilities agreed with actual outcomes; a c statistic of 1 represents perfect model discrimination. Parameter estimates with standard errors, Wald chi-square test statistics, and odds ratios with 95% Wald confidence intervals were examined and interpreted. Statistical tests were performed using a significance level of α≤05. All analyses were performed using SAS® software* (SAS, 2012).
Results Table 1 reports the frequencies of the sociodemographic and mental health service utilization variables for the control and the intervention groups. The sample was comprised of more males than females and was primarily white. Age ranged from 18 to 64 years with a median age of 34 years. Only 5% of the sample was involuntarily readmitted; due to a large amount of unknown data for this variable, it was removed from further analyses. Service categories utilized by more than 10% of the sample within 30 days of the index readmission are listed in Table 1. A high proportion of individuals utilized outpatient mental health (OMH) services (34%), followed by blended case management (BCM) and acute service care coordination (ASC). Other services utilized by more than 10% of the sample were crisis, assertive community treatment (ACT), medication management (Medck), and outpatient drug and alcohol services (ODA). Next, the characteristics of the readmitted (n=67) sample were compared to those without a readmission (n=128), regardless of the intervention group. There were larger proportions in the readmitted group for individuals under the age of 26 (p=.01), and for those who utilized ACT (p=.03), crisis (pG.001), and OMH (pG.001) services within 30 days of the index readmission (see Table 2). The logistic regression model contained the four variables that had a significant association with readmission (age under 26 years, ACT utilization, crisis utilization, and OMH utilization) and group (intervention and control) assignment as predictors and readmission as the outcome. The HL goodness-of-fit test was not significant (p=.08), indicating that the model fit was appropriate for the data.27 Additionally, the likelihood ratio, score, and Wald tests were significant (pG.001), which revealed that the model containing the covariates was superior to the null model (see Table 3). The Cox and Snell and Negelkerke R2 statistics were .27 and .37, respectively. These two tests of model fit show the improvement of the model with the covariates over the null model. Values closer to one indicate better model fit.29,30 The c statistic was .821, representing the level at which the model correctly discriminates the outcome (that is, the proportion of individuals who were correctly classified as having a higher probability for readmission).28
*SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.
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Table 1 Frequencies and comparisons between control and intervention groups Control (n=108)
Gender, male Race, white Age Less than 26 26 to 35 36 to 45 Greater than 45 Diagnosis, % with dual diagnosis Living situation, % homeless Legal status, % with legal issues Utilization ACT ASC BCM Crisis Medck ODA OMH Readmission
Intervention (n=87)
n
%
n
%
p value
58 62
54 57
50 54
46 62
.39 .81
24 31 29 24 26 11 18
22 29 27 22 24 10 26
24 26 16 21 24 4 19
28 30 18 24 27 5 22
.38 .86 .16 .75 .57 .14 .06
16 24 35 23 16 13 37 44
15 22 32 21 15 12 34 41
11 21 25 17 14 14 30 23
13 24 29 20 16 16 35 26
.66 .75 .58 .76 .81 .42 .97 .03*
ACT assertive community treatment, ASC acute service coordination, BCM blended case management, Medck medication check, ODA outpatient drug and alcohol, OMH outpatient mental health *p≤.05
Table 3 reports the parameter estimates (β) with standard errors, the Wald chi-square test statistics, and the odds ratios with 95% Wald confidence intervals. After controlling for age, living situation, and utilization, those in the control group were 2.44 times more likely to be readmitted than those in the intervention group. Additionally, after controlling for the other variables in the model, individuals less than 26 years of age were 2.77 times more likely to be readmitted than those 26 and older, and individuals who utilized ACT (OR=4.91), crisis (OR=4.29), or OMH (OR=6.18) services were more likely to be readmitted than those who did not utilize these services.
Discussion Findings from the current investigation suggest that utilization of a recovery-focused brief interview by care managers before discharge may be an effective bridging strategy to reduce early readmission in individuals at higher risk for readmission. In the current sample of individuals with more frequent readmissions, those who did not receive the interview were more than twice as likely to be readmitted within 30 days as the individuals who did receive the interview. Additional factors that were associated with early readmission were age and service utilization. Individuals under 26 years of age as well as individuals utilizing services such as ACT, crisis, and OMH were more likely to be readmitted. Gender, race, diagnosis, living situation, legal status, and ASC, BCM,
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Table 2 Frequencies and comparisons between readmitted and not readmitted Not readmitted (n=128)
Gender, male Race, white Age Less than 26 26 to 35 36 to 45 Greater than 45 Diagnosis, % with dual diagnosis Living situation, % homeless Legal status, % with legal issues Utilization ACT ASC BCM Crisis Medck ODA OMH
Readmitted (n=67)
n
%
n
%
p value
68 77
53 60
40 39
59 58
.38 .79
24 38 29 24 32 12 41
19 30 23 27 25 9 32
24 18 15 9 18 4 23
36 27 22 14 27 6 34
.01* .59 .89 .06 .78 .51 .81
13 27 33 23 16 19 47
10 21 26 11 18 15 21
14 18 25 25 6 8 40
21 27 38 37 9 12 60
.03* .36 .08 G.001* .07 .57 G.001*
ACT assertive community treatment, ASC acute service coordination, BCM blended case management, Medck medication check, ODA outpatient drug and alcohol, OMH outpatient mental health *p≤.05
Medck, and ODA service utilization were not associated with an early readmission for the current sample. The results of the current investigation are consistent with previous research that supports the association between discharge preparedness and early readmission.8 In a review of predictors of early readmission by Durbin,8 individuals who left inpatient care without specific follow-up plans were more susceptible to an early readmission. The current study also emphasized care management activities to help facilitate appropriate aftercare, which has been shown to impact readmission rates.10,15 The bridging strategy utilized in the current study allowed for individuals to reflect upon barriers that may have contributed to readmission, which prompted them to think about plans to address these barriers. The brief interview intervention reduced early readmission by providing a method to organize plans for follow-up before discharge. The intervention of the current study was administered prior to discharge and provided a supplement to usual care discharge planning activities. This procedure differed from that used in the study of Cuffel,15 which investigated the effects of varying levels of discharge planning and did not find differences in readmission rates between individuals assigned to enhanced or intensive discharge planning versus individuals assigned to usual care. Current findings indicate that the recovery-focused interview of the current investigation was an effective addition to discharge planning; therefore, bridging strategies implemented prior to discharge may deter early readmissions.
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Table 3 Logistic regression analysis: sociodemographic and clinical predictors of psychiatric hospital readmission in the sample Variable Group Age ACT utilization Crisis utilization OMH utilization
Level
OR
Intervention Control 26 or older Less than 26 No Yes No Yes No Yes
1.00 (ref) 2.44 1.00 (ref) 2.77 1.00 (ref) 4.91 1.00 (ref) 4.29 1.00 (ref) 6.18
95% CI 1.17–5.11
.02
1.24–6.17
.01
1.81–13.26
.01
1.79–10.24
.01
2.88–13.25 χ2 60.35 55.21 41.51 11.17
Likelihood ratio test Score test Wald test Hosmer-Lemeshow test
p value
G.001 p value G.001 G.001 G.001 .08
Cox and Snell R2 =.27, Nagelkerke R2 =.37, c statistic=82.1%
The finding that utilization of certain services (ACT, crisis, and OMH) was associated with readmission differed from the finding of Nelson,10 who suggested that engagement in at least one OMH service decreased risk of readmission. However, the population of the present study differed from that of the previous study10 in that it was comprised entirely of individuals who had at least one psychiatric inpatient readmission within 30 days of their most recent discharge. OMH may not be the most appropriate service for these high-risk individuals upon discharge. The current study also differentiated types of community-based services received, whereas the previous study10 focused on global outpatient service utilization. The current finding that individuals who utilized ACT and crisis services were more likely to have an early readmission was not unexpected as these services are likely to be utilized by individuals at an increased risk for readmission.
Limitations There are several limitations to the current study. First, collecting data from only one urban psychiatric specialty hospital does not provide a sample of diverse individuals, and the results may not be applicable to rural environments. The use of one location also limited the ability to look at geographic location as a predictor in the logistic regression model. Due to the scope of the intervention occurring within the activities of a MBHCO, certain aspects of the design were less robust, and the number of observed variables was limited. The assignment to groups was based on alternating weeks; therefore, individuals were not randomized to groups. Information on the severity of psychiatric diagnosis and additional variables that could influence readmission, such as stigma and adherence to treatment, were not able to be measured in the current effort. The use of the 30-day readmission time frame to investigate early readmission was limiting, and the interview’s impact on longer periods to readmission was not assessed.
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Additionally, implementation was not measured beyond whether or not the intervention was delivered, and the study design did not allow for the blinding of care managers.
Suggestions for Future Research The intervention described in the current investigation can be modified for use with other populations. It has been modified by the investigators for use with individuals receiving substance use services in substance use facilities. This intervention could also be used with individuals with high health-care service utilization including emergency room use and readmission to general medical setting for noncatastrophic medical disorders. Future goals include a qualitative analysis of the interviews and collection of quantitative indicators of reliability and validity of the intervention. Future analyses may include a broader study with randomization to groups, more robust measures of implementation, longer time periods to readmission, and quantitative and qualitative measurement of individuals’ mental and physical health status, quality of life, and future goals. The associations between sample clinical and demographic characteristics and readmission rates could be examined to provide a profile of individuals most at risk for future readmissions. Additional factors that are associated with readmission such as mental health diagnosis and pharmacy utilization should also be investigated.
Implications for Behavioral Health The current findings have implications for appropriately targeting intensive practices to individuals at highest risk for readmission as a means to work within the Medicaid mental health system in which resources are becoming more limited and practices aimed at improving quality of care more valued. The bridging strategy utilized in the current study allowed the individual and the care manager to work together to form a recovery plan and differed from a strict discussion of discharge planning appointments and medications. This recovery-focused model with a concentration on building a rapport between the care manager and the individual is an efficient alternative to more time-consuming, costly interventions.
Acknowledgments The authors would like to acknowledge Dr. Jane Kogan and the UPMC Center for High Value Health Care. Conflict of Interest The authors have no conflict of interest to report.
References 1. Byrne SL, Hooke GR, Newnham EA, et al. The effects of progress monitoring on subsequent readmission to psychiatric care: a sixmonth follow-up. Journal of Affective Disorders. 2012;137(1-3):113-116. 2. Klinkenberg WD, Calsyn RJ. Predictors of receipt of aftercare and recidivism among persons with severe mental illness: a review. Psychiatric Services. 1996;47(5):487-496. 3. Geller JL, Fisher WH, McDermeit M, et al. The effects of public managed care on patterns of intensive use of inpatient psychiatric services. Psychiatric Services. 2000;51(11):1385-1391. 4. Machado V, Leonidas C, Santos MA, et al. Psychiatric readmission: an integrative review of the literature. International Nursing Review. 2012;59(4):447-457. 5. Moran PW, Doerfler LA, Scherz J, et al. Rehospitalization of psychiatric patients in a managed care environment. Mental Health Services Research. 2000;2(4):191-199. 6. Craig TJ, Fennig S, Tanenberg-Karant M, et al. Rapid versus delayed readmission in first- admission psychosis: quality indicators for managed care? Annals of Clinical Psychiatry. 2000;12(4):233-238.
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7. Heggestad T. Operating conditions of psychiatric hospitals and early readmission‐effects of high patient turnover. Acta Psychiatrica Scandinavica. 2001;103(3):196-202. 8. Durbin J, Lin E, Layne C, et al. Is readmission a valid indicator of the quality of inpatient psychiatric care? The Journal of Behavioral Health Services & Research. 2007;34(2):137-150. 9. Olfson M, Mechanic D, Boyer CA, et al. Linking inpatients with schizophrenia to outpatient care. Psychiatric Services. 1998;49(7):911-917. 10. Nelson EA, Maruish ME, Axler JL. Effects of discharge planning and compliance with outpatient appointments on readmission rates. Psychiatric Services. 2000;51(7):885- 889. 11. Moore C. Acute in-patient care could do better, says survey. Nursing Times. 1998;94(3):54- 56. 12. Roick C, Gartner A, Heider D, et al. Heavy use of psychiatric inpatient care from the perspective of the patients affected. International Journal of Social Psychiatry. 2006;52(5):432-446. 13. Hadley TR, Culhane DP, McGurrin MC. Identifying and tracking “heavy users” of acute psychiatric inpatient services. Administration and Policy in Mental Health. 1992;19(4):279-290. 14. Hadley TR, McGurrin MC, Pulice RT, et al. Using fiscal data to identify heavy service users. Psychiatric Quarterly. 1990;61(1):41-48. 15. Cuffel BJ, Held M, Goldman W. Predictive models and the effectiveness of strategies for improving outpatient follow-up under managed care. Psychiatric Services. 2002;53(11):1438-1443. 16. Susser E, Valencia E, Conover S, et al. Preventing recurrent homelessness among mentally ill men: a “critical time” intervention after discharge from a shelter. American Journal of Public Health. 1997;87(2):256-262. 17. Herman D, Opler L, Felix A, et al. A critical time intervention with mentally ill homeless men: impact on psychiatric symptoms. The Journal of Nervous and Mental Disease. 2000;188(3):135-140. 18. Herman D, Conover S, Felix A, et al. Critical Time Intervention: an empirically supported model for preventing homelessness in high risk groups. The Journal of Primary Prevention. 2007;28(3-4):295-312. 19. Dixon L, Goldberg R, Iannone V, et al. Use of a critical time intervention to promote continuity of care after psychiatric inpatient hospitalization. Psychiatric Services. 2009;60(4):451-458. 20. Tomita A, Herman DB. The impact of critical time intervention in reducing psychiatric rehospitalization after hospital discharge. Psychiatric Services. 2012;63(9):935-937. 21. Lyons JS, O’Mahoney MT, Miller SI, et al. Predicting readmission to the psychiatric hospital in a managed care environment: implications for quality indicators. The American Journal of Psychiatry. 1997;154(3):337-340. 22. Boyer CA, McAlpine DD, Pottick KJ, et al. Identifying risk factors and key strategies in linkage to outpatient psychiatric care. The American Journal of Psychiatry. 2000;157(10):1592-1598. 23. Craig TJ, Bracken J. A case-control study of rapid readmission in a state hospital population. Annals of Clinical Psychiatry. 1995;7(2):79-85. 24. Horvitz-Lennon M, Normand SL, Gaccione P, et al. Partial versus full hospitalization for adults in psychiatric distress: a systematic review of the published literature (1957-1997). The American Journal of Psychiatry. 2001;158(5):676-685. 25. Kallert TW, Priebe S, McCabe R, et al. Are day hospitals effective for acutely ill psychiatric patients? A European multicenter randomized controlled trial. Journal of Clinical Psychiatry. 2007; 68(2): 278-287. 26. Deegan PE. Recovery: The lived experience of rehabilitation. Psychosocial Rehabilitation Journal. 1988;11(4):11-19. 27. Hosmer DW, Lemeshow S, Sturdivant RX. (3rd ed). Applied logistic regression. New Jersey: Wiley and Sons, 2013. 28. Ambrosius WT. Topics in biostatistics. New Jersey: Humana Press, 2007. 29. Cox DR, Snell EJ. (2nd ed). The analysis of binary data. London: Chapman and Hall, 1989. 30. Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika. 1991(78):691-692.
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Introduction Repeated psychiatric hospitalizations are a burden to both individuals in treatment and the health-care delivery system due to high costs, disruption of individuals’ lives, and undesirable Address correspondence to Carole Taylor, MSN, RN, Community Care Behavioral Health Organization, One Chatham Center, Suite 700, 112 Washington Place, Pittsburgh, PA 15219, USA. Email: [email protected]. Brandi Holsinger, MSW, LCSW, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Jenny V. Flanagan, MA, LPC, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Amanda M. Ayers, MSPH, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Shari L. Hutchison, MS, Community Care Behavioral Health Organization, Pittsburgh, PA, USA. Lauren Terhorst, PhD, Community Care Behavioral Health Organization, Pittsburgh, PA, USA.
)
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consequences such as a decreased ability to live independently.1,2 Individuals with repeated hospitalizations are considered high risk for readmission within 1 year of a prior discharge as one factor that has been shown to predict readmission rates is the number of previous psychiatric hospitalizations.2–5 Furthermore, individuals with repeated psychiatric hospitalizations are more vulnerable to an “early readmission,” that is, a readmission within 30 to 90 days of discharge.6,7 Because the time period immediately after discharge is one of high susceptibility to readmission, utilization of timely interventions and bridging strategies aimed at improving continuity of care and decreasing readmissions can provide needed support by facilitating the transition from inpatient to outpatient care.8–11 Previous research has established that a small number of inpatient users consume a disproportionately large amount of total inpatient resources,3,12–14 and the advent of managed care has increased attention to individuals with costly and inappropriate service use.14 In a study of a managed care population, it was found that 31.2% of all inpatient users accounted for 75% of paid Medicaid claims, and only 6.9% of clients accounted for 42% of total inpatient admissions during the study period.13 Use of managed care organization (MCO) care managers to facilitate bridging strategies known to improve connection to aftercare may be effective in reducing early readmissions; however, prior studies of MCO care management initiatives have yielded varying results. Receipt of care management and other services provided by MCOs has been found to increase the number of individuals receiving community-based mental health services.15 Cuffel15 examined the effects of enhanced and intensive discharge planning versus usual care on readmission in a managed behavioral health-care organization. While there were no differences between the usual care and discharge planning groups, individuals discharged to partial hospitalization or those who were authorized for immediate care but did not attend were more likely to be readmitted within 30, 60, and 180 days. Nelson10 found that hospitalized individuals who kept at least one community-based mental health service appointment following discharge were half as likely to be readmitted as those who did not receive any outpatient treatment. Time was a critical factor for the individuals who did not keep an appointment; that is, as time since the initial admission increased, the rate of readmission increased. As the time since the initial admission passes, different factors impact readmission rates, such as cyclical course of mental illness, access to support, and environmental factors.8 Effective care management interventions to connect individuals to community-based treatment are often timely, occurring during the inpatient stay and/or immediately following discharge. One such model of engagement is Critical Time Intervention (CTI).16–18 CTI has been found to be effective in linking individuals to outpatient treatment19 and preventing psychiatric rehospitalization.20 The traditional CTI model is a 9-month care coordination intervention that is designed to smooth the transition from institutional to community living18; a 3-month version of the model (BCTI) has also been found effective in transitioning individuals from inpatient psychiatric care to community living.19 Though highly effective, the model requires that care coordinators with small caseloads work with individuals for a minimum of 3 months.18 Care coordinators may not have the time or resources to deliver such an intensive model to all of the individuals they serve. Therefore, research into brief, less resource-intensive interventions is warranted. Bridging strategies provide a means to explore aspects of clinical and planning issues. Lyons21 suggested that readmissions may be associated with a lack of discharge planning and failure to monitor outpatient follow-up. Bridging strategies implemented prior to discharge can connect individuals with appropriate outpatient services.22 Boyer et al. found that communication about discharge plans between inpatient and outpatient staff, starting outpatient programs before discharge, and family involvement during hospital stay increased the odds of successful linkage to outpatient care.22 Additionally, the incorporation of bridging strategies may help to identify individuals in need of extra support. The individuals may be discharged to psychiatric day or partial hospitalization programs,23,24 which have served as effective alternatives to conventional
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inpatient care.25 Durbin8 suggested that standardization of the discharge planning approach would provide a consistent review of planning around issues such as clinical acuity, medication management, and postdischarge care needs. The current prospective study was an examination of a recovery-focused bridging strategy intervention during the time of inpatient stay with the goal to increase engagement in aftercare and reduce early psychiatric readmissions. The intervention consisted of a brief interview that addressed goals and barriers to treatment which was administered by care managers of a managed behavioral health organization prior to the individuals’ discharge. The hypothesis was that individuals who received the intervention would have lower rates of 30-day readmissions than individuals who did not receive the intervention. The 30-day span was utilized to explore the effectiveness of the recovery-focused bridging strategy on early readmission rates. Additionally, the impact of demographic and service utilization factors on early readmission rates was explored. This study adds to the existing literature because it explores the impact of a novel recovery-focused bridging strategy intervention administered prior to discharge in addition to clinical and demographic factors on 30-day readmission rates.
Method Sample Data were obtained from adults readmitted to inpatient psychiatric care within 30 days prior to the index readmission. The study sample included 195 individuals who received care from a large psychiatric specialty hospital between April 2011 and November 2012. Medicaid-eligible adults who were readmitted to an inpatient psychiatric hospital within 30 days of a prior psychiatric hospital admission were identified as individuals eligible for the intervention. Individuals unavailable to participate in the interview due to medical issues, intellectual difficulties, or clinical milieu were excluded from the intervention. Procedure Care managers of a managed behavioral health-care organization (MBHCO) identified individuals with a psychiatric inpatient 30-day readmission (index readmission) and conducted a brief intervention to address barriers and motivations for continuing treatment and facilitate referrals and information for aftercare. Each day, the high-risk care management team received automated notifications of individuals who were readmitted to an inpatient psychiatric hospital within 30 days of a prior psychiatric hospital admission. Eight care managers with clinical experience conducted in-person interviews with readmitted individuals at several units of one psychiatric hospital. A unit that specialized in schizophrenia disorders and another that specialized in co-occurring mental health and substance abuse disorders were included as well as several units for the general adult population. A quasi-experimental, two-group design was utilized; one group was assigned to the interview (intervention group), and the other was assigned to usual care (control group). The assignment to the interview group was based on an alternating week schedule, resulting in 87 individuals assigned to receive the interview and 108 individuals not assigned to receive the interview based on their readmission date. Activities were approved by the University of Pittsburgh Institutional Review Board as a quality improvement initiative.
Intervention Development and Protocol The intervention was developed by the first author with the intent to reduce readmission rates and promote effective engagement postdischarge. The interview tool was based on several key
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elements of recovery models and focuses on the opportunities for self-improvement through the three cornerstones of recovery—hope, willingness, and responsible action.26 The intervention was created with the purpose of developing a nonintrusive, nonthreatening bridging strategy to build a rapport (willingness of the individual to meet and discuss options) between the care manager and the individual prior to discharge. The interview promotes self-reflection by the individual and stresses that care and recovery must not stop at hospital discharge but must continue in the community (responsible action). The on-site intervention was a one-time semistructured interview which lasted approximately 10– 20 min. The face-to-face interview consisted of six open-ended discussion areas as follows: (1) the reason for the current admission, (2) barriers to increasing community tenure, (3) how barriers to community tenure may be overcome, (4) discussion and use of a crisis plan, (5) factors that would help to keep the individual safe, and (6) needs during inpatient stay that would assist with a transition to the community. These six elements provide the individual an opportunity to develop a responsible plan of action and hope for continued community tenure. Additionally, the care manager would coordinate with the individuals’ current community-based case management team or assess to refer to an acute service care coordinator. All topics were addressed during each interview, but each session was unique because it was driven by the individual’s goals and needs. This is consistent with a key principle of recovery that each person’s experiences and needs are unique.26 The intervention group received the recovery-focused interview plus usual care, while the control group received usual care. Usual care included discharge planning by hospital staff with referrals to behavioral health services such as drug and alcohol rehabilitation, case management supports (service coordination), and a referral to appropriate housing for the member. In the control condition, care managers did not meet with individuals during the index readmission but did help to facilitate appropriate aftercare.
Measures Sociodemographic characteristics and mental health service utilization Sociodemographic information and behavioral health service utilization in the 30 days following the index readmission were obtained through administrative data and paid behavioral health claims. Sociodemographic variables included gender, race, and age. Additional variables of interest were obtained from care management administrative data including commitment status (voluntary/ involuntary), diagnosis (dual diagnosis/no dual diagnosis), living situation (homeless/not homeless), and legal status (legal issues/no legal issues). Dual diagnosis was defined as a substance use disorder diagnosis in conjunction with a mental health diagnosis. Readmission Readmission was determined based on information from paid behavioral health service claims. Individuals with a psychiatric hospitalization within 30 days of the index readmission were identified as readmitted, while those without a psychiatric hospitalization were labeled as not readmitted, forming a dichotomous outcome variable.
Statistical Analysis The first readmission for individuals with multiple 30-day readmissions during the evaluation period was selected so that each unique individual was represented in the data analyses only once. Sociodemographic characteristics were first explored to determine if group distributions (intervention vs. control) were equitable using chi-square tests. Additionally, chi-square tests were
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utilized to compare characteristics between those readmitted versus those not readmitted. Next, a logistic regression model was formed using variables with a significant association with readmission in our sample as predictors and the dichotomous variable of readmission (yes=1, no=0) as the outcome and group (intervention vs. control) as the independent variable, controlling for any variables that had significant between-group differences. The Hosmer-Lemeshow (H-L) test statistic was utilized as a goodness-of-fit test, while the Likelihood Ratio, Score, and Wald tests were examined to evaluate the model.27 A nonsignificant H-L test provides evidence that the model fits the data appropriately, while significant likelihood ratio, score, and Wald tests determine that the model with covariates is more appropriate than the null (intercept only) model.28 Two additional measures of fit were produced: the Cox and Snell29 R2 and the Nagelkerke30 R2. These estimates are not interpretable in the same manner as the R2 of an ordinary least squares regression and are used as complementary measures to the model fit tests. The c statistic measure of association was assessed to determine the degree to which predicted probabilities agreed with actual outcomes; a c statistic of 1 represents perfect model discrimination. Parameter estimates with standard errors, Wald chi-square test statistics, and odds ratios with 95% Wald confidence intervals were examined and interpreted. Statistical tests were performed using a significance level of α≤05. All analyses were performed using SAS® software* (SAS, 2012).
Results Table 1 reports the frequencies of the sociodemographic and mental health service utilization variables for the control and the intervention groups. The sample was comprised of more males than females and was primarily white. Age ranged from 18 to 64 years with a median age of 34 years. Only 5% of the sample was involuntarily readmitted; due to a large amount of unknown data for this variable, it was removed from further analyses. Service categories utilized by more than 10% of the sample within 30 days of the index readmission are listed in Table 1. A high proportion of individuals utilized outpatient mental health (OMH) services (34%), followed by blended case management (BCM) and acute service care coordination (ASC). Other services utilized by more than 10% of the sample were crisis, assertive community treatment (ACT), medication management (Medck), and outpatient drug and alcohol services (ODA). Next, the characteristics of the readmitted (n=67) sample were compared to those without a readmission (n=128), regardless of the intervention group. There were larger proportions in the readmitted group for individuals under the age of 26 (p=.01), and for those who utilized ACT (p=.03), crisis (pG.001), and OMH (pG.001) services within 30 days of the index readmission (see Table 2). The logistic regression model contained the four variables that had a significant association with readmission (age under 26 years, ACT utilization, crisis utilization, and OMH utilization) and group (intervention and control) assignment as predictors and readmission as the outcome. The HL goodness-of-fit test was not significant (p=.08), indicating that the model fit was appropriate for the data.27 Additionally, the likelihood ratio, score, and Wald tests were significant (pG.001), which revealed that the model containing the covariates was superior to the null model (see Table 3). The Cox and Snell and Negelkerke R2 statistics were .27 and .37, respectively. These two tests of model fit show the improvement of the model with the covariates over the null model. Values closer to one indicate better model fit.29,30 The c statistic was .821, representing the level at which the model correctly discriminates the outcome (that is, the proportion of individuals who were correctly classified as having a higher probability for readmission).28
*SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.
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Table 1 Frequencies and comparisons between control and intervention groups Control (n=108)
Gender, male Race, white Age Less than 26 26 to 35 36 to 45 Greater than 45 Diagnosis, % with dual diagnosis Living situation, % homeless Legal status, % with legal issues Utilization ACT ASC BCM Crisis Medck ODA OMH Readmission
Intervention (n=87)
n
%
n
%
p value
58 62
54 57
50 54
46 62
.39 .81
24 31 29 24 26 11 18
22 29 27 22 24 10 26
24 26 16 21 24 4 19
28 30 18 24 27 5 22
.38 .86 .16 .75 .57 .14 .06
16 24 35 23 16 13 37 44
15 22 32 21 15 12 34 41
11 21 25 17 14 14 30 23
13 24 29 20 16 16 35 26
.66 .75 .58 .76 .81 .42 .97 .03*
ACT assertive community treatment, ASC acute service coordination, BCM blended case management, Medck medication check, ODA outpatient drug and alcohol, OMH outpatient mental health *p≤.05
Table 3 reports the parameter estimates (β) with standard errors, the Wald chi-square test statistics, and the odds ratios with 95% Wald confidence intervals. After controlling for age, living situation, and utilization, those in the control group were 2.44 times more likely to be readmitted than those in the intervention group. Additionally, after controlling for the other variables in the model, individuals less than 26 years of age were 2.77 times more likely to be readmitted than those 26 and older, and individuals who utilized ACT (OR=4.91), crisis (OR=4.29), or OMH (OR=6.18) services were more likely to be readmitted than those who did not utilize these services.
Discussion Findings from the current investigation suggest that utilization of a recovery-focused brief interview by care managers before discharge may be an effective bridging strategy to reduce early readmission in individuals at higher risk for readmission. In the current sample of individuals with more frequent readmissions, those who did not receive the interview were more than twice as likely to be readmitted within 30 days as the individuals who did receive the interview. Additional factors that were associated with early readmission were age and service utilization. Individuals under 26 years of age as well as individuals utilizing services such as ACT, crisis, and OMH were more likely to be readmitted. Gender, race, diagnosis, living situation, legal status, and ASC, BCM,
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Table 2 Frequencies and comparisons between readmitted and not readmitted Not readmitted (n=128)
Gender, male Race, white Age Less than 26 26 to 35 36 to 45 Greater than 45 Diagnosis, % with dual diagnosis Living situation, % homeless Legal status, % with legal issues Utilization ACT ASC BCM Crisis Medck ODA OMH
Readmitted (n=67)
n
%
n
%
p value
68 77
53 60
40 39
59 58
.38 .79
24 38 29 24 32 12 41
19 30 23 27 25 9 32
24 18 15 9 18 4 23
36 27 22 14 27 6 34
.01* .59 .89 .06 .78 .51 .81
13 27 33 23 16 19 47
10 21 26 11 18 15 21
14 18 25 25 6 8 40
21 27 38 37 9 12 60
.03* .36 .08 G.001* .07 .57 G.001*
ACT assertive community treatment, ASC acute service coordination, BCM blended case management, Medck medication check, ODA outpatient drug and alcohol, OMH outpatient mental health *p≤.05
Medck, and ODA service utilization were not associated with an early readmission for the current sample. The results of the current investigation are consistent with previous research that supports the association between discharge preparedness and early readmission.8 In a review of predictors of early readmission by Durbin,8 individuals who left inpatient care without specific follow-up plans were more susceptible to an early readmission. The current study also emphasized care management activities to help facilitate appropriate aftercare, which has been shown to impact readmission rates.10,15 The bridging strategy utilized in the current study allowed for individuals to reflect upon barriers that may have contributed to readmission, which prompted them to think about plans to address these barriers. The brief interview intervention reduced early readmission by providing a method to organize plans for follow-up before discharge. The intervention of the current study was administered prior to discharge and provided a supplement to usual care discharge planning activities. This procedure differed from that used in the study of Cuffel,15 which investigated the effects of varying levels of discharge planning and did not find differences in readmission rates between individuals assigned to enhanced or intensive discharge planning versus individuals assigned to usual care. Current findings indicate that the recovery-focused interview of the current investigation was an effective addition to discharge planning; therefore, bridging strategies implemented prior to discharge may deter early readmissions.
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Table 3 Logistic regression analysis: sociodemographic and clinical predictors of psychiatric hospital readmission in the sample Variable Group Age ACT utilization Crisis utilization OMH utilization
Level
OR
Intervention Control 26 or older Less than 26 No Yes No Yes No Yes
1.00 (ref) 2.44 1.00 (ref) 2.77 1.00 (ref) 4.91 1.00 (ref) 4.29 1.00 (ref) 6.18
95% CI 1.17–5.11
.02
1.24–6.17
.01
1.81–13.26
.01
1.79–10.24
.01
2.88–13.25 χ2 60.35 55.21 41.51 11.17
Likelihood ratio test Score test Wald test Hosmer-Lemeshow test
p value
G.001 p value G.001 G.001 G.001 .08
Cox and Snell R2 =.27, Nagelkerke R2 =.37, c statistic=82.1%
The finding that utilization of certain services (ACT, crisis, and OMH) was associated with readmission differed from the finding of Nelson,10 who suggested that engagement in at least one OMH service decreased risk of readmission. However, the population of the present study differed from that of the previous study10 in that it was comprised entirely of individuals who had at least one psychiatric inpatient readmission within 30 days of their most recent discharge. OMH may not be the most appropriate service for these high-risk individuals upon discharge. The current study also differentiated types of community-based services received, whereas the previous study10 focused on global outpatient service utilization. The current finding that individuals who utilized ACT and crisis services were more likely to have an early readmission was not unexpected as these services are likely to be utilized by individuals at an increased risk for readmission.
Limitations There are several limitations to the current study. First, collecting data from only one urban psychiatric specialty hospital does not provide a sample of diverse individuals, and the results may not be applicable to rural environments. The use of one location also limited the ability to look at geographic location as a predictor in the logistic regression model. Due to the scope of the intervention occurring within the activities of a MBHCO, certain aspects of the design were less robust, and the number of observed variables was limited. The assignment to groups was based on alternating weeks; therefore, individuals were not randomized to groups. Information on the severity of psychiatric diagnosis and additional variables that could influence readmission, such as stigma and adherence to treatment, were not able to be measured in the current effort. The use of the 30-day readmission time frame to investigate early readmission was limiting, and the interview’s impact on longer periods to readmission was not assessed.
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Additionally, implementation was not measured beyond whether or not the intervention was delivered, and the study design did not allow for the blinding of care managers.
Suggestions for Future Research The intervention described in the current investigation can be modified for use with other populations. It has been modified by the investigators for use with individuals receiving substance use services in substance use facilities. This intervention could also be used with individuals with high health-care service utilization including emergency room use and readmission to general medical setting for noncatastrophic medical disorders. Future goals include a qualitative analysis of the interviews and collection of quantitative indicators of reliability and validity of the intervention. Future analyses may include a broader study with randomization to groups, more robust measures of implementation, longer time periods to readmission, and quantitative and qualitative measurement of individuals’ mental and physical health status, quality of life, and future goals. The associations between sample clinical and demographic characteristics and readmission rates could be examined to provide a profile of individuals most at risk for future readmissions. Additional factors that are associated with readmission such as mental health diagnosis and pharmacy utilization should also be investigated.
Implications for Behavioral Health The current findings have implications for appropriately targeting intensive practices to individuals at highest risk for readmission as a means to work within the Medicaid mental health system in which resources are becoming more limited and practices aimed at improving quality of care more valued. The bridging strategy utilized in the current study allowed the individual and the care manager to work together to form a recovery plan and differed from a strict discussion of discharge planning appointments and medications. This recovery-focused model with a concentration on building a rapport between the care manager and the individual is an efficient alternative to more time-consuming, costly interventions.
Acknowledgments The authors would like to acknowledge Dr. Jane Kogan and the UPMC Center for High Value Health Care. Conflict of Interest The authors have no conflict of interest to report.
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7. Heggestad T. Operating conditions of psychiatric hospitals and early readmission‐effects of high patient turnover. Acta Psychiatrica Scandinavica. 2001;103(3):196-202. 8. Durbin J, Lin E, Layne C, et al. Is readmission a valid indicator of the quality of inpatient psychiatric care? The Journal of Behavioral Health Services & Research. 2007;34(2):137-150. 9. Olfson M, Mechanic D, Boyer CA, et al. Linking inpatients with schizophrenia to outpatient care. Psychiatric Services. 1998;49(7):911-917. 10. Nelson EA, Maruish ME, Axler JL. Effects of discharge planning and compliance with outpatient appointments on readmission rates. Psychiatric Services. 2000;51(7):885- 889. 11. Moore C. Acute in-patient care could do better, says survey. Nursing Times. 1998;94(3):54- 56. 12. Roick C, Gartner A, Heider D, et al. Heavy use of psychiatric inpatient care from the perspective of the patients affected. International Journal of Social Psychiatry. 2006;52(5):432-446. 13. Hadley TR, Culhane DP, McGurrin MC. Identifying and tracking “heavy users” of acute psychiatric inpatient services. Administration and Policy in Mental Health. 1992;19(4):279-290. 14. Hadley TR, McGurrin MC, Pulice RT, et al. Using fiscal data to identify heavy service users. Psychiatric Quarterly. 1990;61(1):41-48. 15. Cuffel BJ, Held M, Goldman W. Predictive models and the effectiveness of strategies for improving outpatient follow-up under managed care. Psychiatric Services. 2002;53(11):1438-1443. 16. Susser E, Valencia E, Conover S, et al. Preventing recurrent homelessness among mentally ill men: a “critical time” intervention after discharge from a shelter. American Journal of Public Health. 1997;87(2):256-262. 17. Herman D, Opler L, Felix A, et al. A critical time intervention with mentally ill homeless men: impact on psychiatric symptoms. The Journal of Nervous and Mental Disease. 2000;188(3):135-140. 18. Herman D, Conover S, Felix A, et al. Critical Time Intervention: an empirically supported model for preventing homelessness in high risk groups. The Journal of Primary Prevention. 2007;28(3-4):295-312. 19. Dixon L, Goldberg R, Iannone V, et al. Use of a critical time intervention to promote continuity of care after psychiatric inpatient hospitalization. Psychiatric Services. 2009;60(4):451-458. 20. Tomita A, Herman DB. The impact of critical time intervention in reducing psychiatric rehospitalization after hospital discharge. Psychiatric Services. 2012;63(9):935-937. 21. Lyons JS, O’Mahoney MT, Miller SI, et al. Predicting readmission to the psychiatric hospital in a managed care environment: implications for quality indicators. The American Journal of Psychiatry. 1997;154(3):337-340. 22. Boyer CA, McAlpine DD, Pottick KJ, et al. Identifying risk factors and key strategies in linkage to outpatient psychiatric care. The American Journal of Psychiatry. 2000;157(10):1592-1598. 23. Craig TJ, Bracken J. A case-control study of rapid readmission in a state hospital population. Annals of Clinical Psychiatry. 1995;7(2):79-85. 24. Horvitz-Lennon M, Normand SL, Gaccione P, et al. Partial versus full hospitalization for adults in psychiatric distress: a systematic review of the published literature (1957-1997). The American Journal of Psychiatry. 2001;158(5):676-685. 25. Kallert TW, Priebe S, McCabe R, et al. Are day hospitals effective for acutely ill psychiatric patients? A European multicenter randomized controlled trial. Journal of Clinical Psychiatry. 2007; 68(2): 278-287. 26. Deegan PE. Recovery: The lived experience of rehabilitation. Psychosocial Rehabilitation Journal. 1988;11(4):11-19. 27. Hosmer DW, Lemeshow S, Sturdivant RX. (3rd ed). Applied logistic regression. New Jersey: Wiley and Sons, 2013. 28. Ambrosius WT. Topics in biostatistics. New Jersey: Humana Press, 2007. 29. Cox DR, Snell EJ. (2nd ed). The analysis of binary data. London: Chapman and Hall, 1989. 30. Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika. 1991(78):691-692.
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