Health Care Hotspotting: A Randomized Controlled Trial
Phase: NA
Conditions: High Utilizers of Hospital Care
Sponsor: Abdul Latif Jameel Poverty Action Lab

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Health Care Hotspotting: A Randomized Controlled Trial 
Analysis Plan 
Date:  March 7, 2014 
Abstract 
This randomized trial investigates the impact of the Camden Coalition of Healthcare Providers’ 
Care Management Program: Link2Care.  The program targets “super-utilizers” of the health care 
system - specifically adults with 2 or more hospitalizations in the last six months and 2 or more 
chronic conditions - with intensive care-management services in the one to three months 
following hospital discharge.  A team of nurses, social workers, community health workers and 
health coaches, supported by real-time data of healthcare utilization, perform home visits, 
accompany patients to doctor visits, and help patients enroll in social-service programs.  This 
approach aims to improve the self-sufficiency of patients in navigating the healthcare and social-
service systems and has the potential to reduce healthcare costs and improve patient health.  
Through random assignment to the intervention, we plan to study the impact of the Link2Care 
program, particularly on subsequent hospital use. 
PRINCIPAL INVESTIGATOR: Jeffrey Brenner1, MD and Amy Finkelstein2, PhD 
CO-INVESTIGATOR(S): 
Joseph Doyle3, PhD 
Sarah Taubman4, ScD 
Ruohua Annetta Zhou5, PhD candidate 
DEPARTMENT(S):   
1. Department of Family Medicine, Cooper Health System
2. Department of Economics, Massachusetts Institute of Technology
3. Sloan School of Management, Massachusetts Institute of Technology
4. National Bureau of Economic Research
5. PhD Program in Health Policy, Harvard University
NCT02090426

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1.   Introduction 
This document details our analysis plan for:  Health Care Hotspotting: A Randomized Controlled 
Trial.  This will serve as a record of the planned analyses to avoid concerns over searching for 
results ex post.  We anticipate that further analyses will be conducted as more outcome data 
become available, which we describe below, as well as research that is inspired by the initial 
findings.  In addition, as the plan has been prepared in advance of the pilot, it is possible that our 
planned analyses may be modified based on what we learn during the pilot. 
The structure of the plan is as follows:  Section 2 briefly describes the intervention; Section 3 
reviews the related literature and highlights the innovations of this intervention; Section 4 
provides details on the trial itself; Section 5 describes the data and the primary outcome of 
interest; Section 6 presents the main empirical models, including Tables and Figures that we will 
complete as part of the study; Section 7 reports preliminary analyses in support of the plan; 
Section 8 briefly outlines our planned next steps in terms of additional data gathering and 
analyses; and   Section 9 concludes with caveats and interpretation issues. 
 
2.  The intervention 
The Camden Coalition of Healthcare Providers’ Care Management Program, Link2Care, targets 
“super-utilizers” of the health care system.  These are individuals with medically and socially 
complex needs who have frequent hospital admissions.  Specifically, the Link2Care program 
targets patients in Camden, New Jersey hospitals who have had at least two hospital admissions 
in the prior six months and have at least two chronic conditions. 
Such heavy utilizers of hospital care account for a disproportionate share of healthcare spending.  
For example, CCHP analyzed hospital admission and emergency department use at three 
Camden hospital systems from 2002-2007 and found that 20% of patients accounted for 90% of 
the costs (Green et al., 2010).  When we compare patients admitted to Camden hospitals, in the 
year prior to an admission a typical patient targeted by the program has 2.5 times more 
admissions in the prior six months compared to other patients due to the targeting.  Such patients 
are also much more likely to be readmitted to the hospital over the year following the hospital 
stay, accruing $73,000 in hospital charges over that time compared to an average of $6600 for 
patients not targeted by the program. 
In an effort to improve the health of these patients and reduce subsequent healthcare costs, 
Link2Care provides intensive care management and coordination for up to 6 months following 
hospital discharge.  Participants are assigned to a multidisciplinary care team comprised of a 
registered nurse, a licensed practical nurse, a social worker, an intervention specialist, a 
community health worker, and health coaches.  They then plan a series of home visits, schedule 
and accompany patients to initial primary care and specialty care visits, and support individuals 

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as they navigate various social service agencies to enroll in public programs including Medicaid, 
SNAP/TANF, substance abuse and behavioral health programs, and programs that promote 
housing stability. 
More specifically, upon discharge from the hospital, the care team works to visit the patient at 
home within 3 days.  At the initial home visit, the care team (1) performs medication 
reconciliation—an inventory of the medications prescribed to gauge appropriateness and patient 
understanding, (2) conducts an assessment of the patient’s perception of the discharge experience 
and care coordination, medical/health needs, activity/mobility, service needs, and stage of 
readiness to change, and (3) collaboratively sets goals with the individual, such as compliance 
with the discharge plan.   
The care team then works closely with the patient to achieve these goals.  They aid in scheduling 
a primary care visit within 7 days of discharge and appropriate specialist visits, as necessary.  A 
member of the team will accompany the patient to those visits to ensure that such appointments 
are kept and that the patient understands any instructions.  The team also helps the participant 
complete applications for social services and coaches the patient in self-care.  Subsequent home 
visits evaluate the patient’s and the team’s progress.  The end of the intervention is determined 
based on hospital utilization, individual factors (health education/literacy, disease self-
management, skills development, level of engagement, self-efficacy) and some systemic factors 
(access to, and the quality of, care, social support, etc.).  The person receives a graduation 
certificate and is then expected to meet healthcare needs in the future through a primary care 
physician.  From October 2012 to January 2014, the median length of the intervention for those 
who completed it was 85 days. 
The approach aims to improve the self-sufficiency of patients in navigating the healthcare and 
social-service systems.  It has the potential to reduce healthcare costs and improve patient health, 
as patients learn to use primary care to prevent an escalation of symptoms that leads to 
rehospitalization.  In an earlier, non-randomized evaluation, this program has been found to 
improve health outcomes, decrease utilization of emergency and inpatient services, and decrease 
costs for a cohort of 36 “high utilizers” from $1.2 million monthly to $534,000 monthly, a 
savings of 56% over five years (Green et al., 2010). 
Due to staffing and financial constraints, Link2Care is currently administered for only a subset of 
the patients who meet the eligibility criteria, and the patients who are currently approached are 
chosen in an ad-hoc manner. This study would establish a formal process for determining – via 
random assignment – which subset of eligible individuals are offered the intervention.  This 
random assignment, which will not reduce the number of individuals who benefit from the 
services, will allow us to isolate the causal effects of the CCHP Link2Care Program.   
These findings will not only be of importance to CCHP and the Camden patients they serve, but 
will also inform any expansion of CCHP’s programs. The Link2Care program has received 

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national attention (Gawande 2011). And the CCHP’s 2012 Center for Medicare and Medicaid 
Innovation grant has funded four new sites that are implementing the Link2Care model in 
Allentown, Pennsylvania, Aurora, Colorado, Kansas City, Missouri, and San Diego, California. 
 
3.  Related literature   
3.1. Brief literature review   
Transitional care programs come in a variety of forms, and there is compelling evidence that 
these interventions can have large effects on subsequent utilization.  Naylor et al. (2011) 
reviewed the literature on these programs.  They studied 21 RCTs and found 9 that showed 
significant reductions in readmissions.   In our own review of nine RCTs of care management 
programs with at least two home visits by nurses, we also find that some programs are shown to 
significantly reduce readmissions but not all.1  Takeda et al. (2012) reviewed 25 RCTs of disease 
management interventions for chronic heart failure (CHF) patients.  They conclude that case 
management interventions led by a heart failure specialist nurse, as well as multidisciplinary 
interventions delivered by a team, are successful in reducing readmissions and mortality at 
twelve months.  
While the evidence is therefore mixed, Naylor et al. point out that there were some common 
themes among the more-successful interventions.   In particular, they tended to be engaged with 
patients for a longer period of time—typically 2-3 months—and they usually included home 
visits by nurses.  In addition, they often included “comprehensive discharge planning with 
follow-up interventions that incorporate patient and caregiver goal setting, individualized care 
planning, educational and behavioral strategies, and clinical management.”  Link2Care aims to 
implement these best practices.   
One of the more successful interventions is described by Naylor et al. (2004):  a three-month 
intervention with nurse home visits for a population over the age of 65 with congestive heart 
failure.  The treatment group had a rehospitalization or death at 52 weeks at a rate of 47.5% vs. 
61.2% for the control group, a difference that was statistically significant with a sample size of 
239.   
In terms of heterogeneity of effects, prior studies often lack the power to distinguish effects 
across subgroups.  Some studies (e.g. Shumway et al. 2008 and Jack et al. 2009) found a larger 
reduction in subsequent utilization among "high risk" subgroups, identified as either having 
higher utilization prior to the intervention or more co-morbidities.  Studies that focus on mental-
health patients tend to find little effect of interventions.  Link2Care targets patients who may be 
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1 Rich et al. (1995), Naylor et al. (1999), Naylor et al. (2004), Daly et al. (2005), and Parry (2009) find significant 
reductions in subsequent utilization;  Siu et al., (1996), Brown et al. (2006), Allen et al. (2009), and Dixon et al. 
(2009) do not find such reductions.  

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the most amenable to such an intervention:  high-cost patients who suffer from multiple 
comorbidities, excluding patients who suffer exclusively from mental health conditions. 
3.2.  Distinguishing features of Link2Care 
A few main characteristics distinguish Link2Care from interventions in the existing literature.  
First, the Link2Care model specifically targets “super-utilizers” of hospital care.  Patients have to 
have had 2 or more admissions in the past 6 months to qualify for the program.  A few studies 
targeted high-utilizers of emergency departments (Shumway et al. 2008, Spillane et al. 1996), 
but this is a very different (and lower cost) population; we have not seen any other randomized 
evaluation of an intervention that targets hospital “high-utilizers” explicitly.2  Given the right-
skewness of health care spending, an intervention that can “bend the cost curve” for the highest 
cost patients would be of great interest.   
Second, it is unique in the sense that it has both an intensive clinical component and an intensive 
social component. The typical transitional care intervention tends to be more focused on the 
clinical component, such as designing an individualized care plan, discussing patients’ needs, 
assisting patients in making follow-up appointments and sometimes accompanying them to these 
appointments.  Link2Care teams seem to make an exceptionally intense and aggressive effort to 
help patients obtain social services. For example, the intervention team not only helps patients 
apply for housing, but accompanies them to the Social Security Administration and the Motor 
Vehicle Commission offices to obtain necessary identification.   
One intervention that appears to have similarly intensive social interventions as Link2Care was 
conducted by Shumway et al. (2008).  This intervention did not include a clinical component, 
however.  It did target patients who frequented the emergency department and found a 
significant reduction in emergency department visits between 19 and 24 months after discharge.  
Other studies mention social workers used as part of the case management, but do not focus on 
their role.   
Third, whereas most existing studies focus on Medicare patients, VA patients, or patients with 
specific chronic disease diagnoses, there is no such restriction on the targeted population for 
Link2Care. The only requirement is that patients need to have some sort of insurance in order to 
be eligible. As a result, Link2Care is offered to a wide range of patients:  patients with different 
diagnoses; adults of all ages, and Spanish-speaking individuals (who are typically excluded from 
similar studies).  
 
4.  Trial details 
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2 It is worth noting that while none of the other studies specifically focus on high-utilizers, their targeted 
populations, which are typically enrolled at hospital bedside before discharge, may still have a high hospitalization 
rate in general. 

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4.1. Enrollment 
Enrollment in the trial will be conducted by CCHP in a process similar to the one currently used 
to enroll patients into Link2Care.  Patients will be enrolled at the two major Camden hospitals:  
Cooper University Hospital and Our Lady of Lourdes.   
Identification of eligible patients will rely on a Health Information Exchange (HIE) that CCHP 
helped to develop.  The hospital systems currently in the HIE include Cooper, Lourdes, and 
Virtua (a local hospital system with an emergency department in Camden).  CCHP is currently 
exploring adding two more nearby hospitals, Kennedy Hospital and Underwood Memorial 
Hospital, to the HIE.   
CCHP receives a daily data feed of individuals currently admitted to Cooper or Lourdes hospitals 
who have had two or more hospital admissions in the prior six months to any of the hospitals in 
the HIE.  A staff member reviews the HIE along with electronic medical record data for pre-
specified inclusion and exclusion criteria.   
Patients must satisfy the following criteria based on the records at the time of the index event: 
• Is currently admitted to Cooper or Lourdes hospitals (still in hospital for recruitment) 
• Is 19-80 years old 
• Resides in the following ZIP codes: 08101 (PO ZIP code), 08102-08105, 08107 
• Has >=2 hospital admissions in the past 6 months (to hospitals in the HIE3) 
• Has >=2 chronic conditions 
 
Patients must meet at least three of the following criteria based largely on the electronic medical 
record: 
• Has >=5 outpatient medications 
• Has difficulty accessing services 
• Lacks social support 
• Has a mental health co-morbidity 
• Is actively using drugs  
• Is homeless 
 
Patients will be excluded if they meet any of the following criteria: 
• Already a subject in the RCT (treatment or control) 
• Deceased or discharged prior to triage or recruitment 
• Uninsured 
• Cognitively impaired 
• Oncology patient 
• Index hospitalization is for: 
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3 This currently includes Cooper (1 hospital), Lourdes (2 hospitals), and Virtua (4 hospitals), but will change if 
additional hospitals are added to the HIE. 

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– A surgical procedure for an acute problem 
– Complications of a progressive chronic disease with limited treatments 
– A mental health issue only with no co-morbid conditions  
 
A CCHP recruiter will approach individuals deemed eligible before the candidate is discharged 
from the hospital. The recruiter will explain the purpose, process, risks and benefits of the study, 
including the randomization process and intervention.  An informed consent form will be 
provided and explained. Candidates can choose to enroll in the trial by signing the consent form. 
If the candidate enrolls, the recruiter will conduct a short survey for which the subjects will be 
compensated $20 for their time.  Only after the completion of the survey will the recruiter learn 
the results of the randomization and inform the patient.  
If an individual is randomized to the intervention, a care management team will be assigned to 
the case.  For individuals randomized to control, they will receive the usual standard of care, but 
not the additional CCHP intervention.  The standard of care in this case includes a printed 
hospital discharge plan prior to leaving the hospital.   
4.2.  Timing 
The trial will be piloted beginning in March or April 2014, and we anticipate recruiting patients 
at least through June 2015.  We plan to continue enrollment until we attain at least 800 subjects 
(400 treatment, 400 controls), as described in the next section. 
 
5.  Data sources and outcomes  
5.1.  Data sources 
This plan details the initial analyses using administrative claims data recorded from emergency 
department and inpatient stays in Camden-area hospitals that are members of the Health 
Information Exchange (HIE) described above.  These data can be matched to study participants 
using identifiers including the subject’s name, SSN, date of birth, gender and address.  The data 
include typical claims-data elements including detailed information about admission and 
discharge dates, diagnosis and procedure codes, insurance status and hospital charges and 
receipts.   
The flow of how the HIE is used to identify subjects, monitor their enrollment, and study their 
outcomes is described by the following figure: 
 
 

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Data Flow 
 
Below we describe analyses of the comprehensive nature of these data sources and our pursuit of 
complementary data sources. 
5.2.  Primary outcome 
The primary outcome we will analyze is whether Link2Care reduces hospital readmissions, as 
patients become more self-sufficient in securing medical care outside of the hospital.  In 
particular, our primary outcome is any readmission within 180 days of discharge from the 
hospital stay when the subject was enrolled in the study.  We will follow the CMS convention of 
not counting a transfer from the hospital where the patient was enrolled to another hospital as a 
readmission, as such a transfer would apply to the current episode and the patient would not be at 
risk of entering the Link2Care Program, which begins once the patient returns home.  
5.3.  Secondary outcomes 
Other outcomes of interest include measures of healthcare utilization, including emergency 
department (ED) visits, and treatment intensity associated with readmissions (number of days, 
number of procedures, hospital charges, and hospital receipts).  We are interested in measuring 
effects in the short-term while the program is actively providing services (30-days, 90-days), and 
longer-term outcomes (180-days, 365 days).   
Eligible Patients
Patients subject to inclusion and exclusion criteria (e.g. 2+ admissions 
in last six months) observed in the Health information Exchange 
Hospital systems included:  Cooper, Lourdes, and Virtua
Enrollment
Hospitals included:  Cooper and Lourdes
Outcomes Measured
Subsequent hospital utilization from claims data
Hospital systems included:  Cooper, Lourdes, and Virtua

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5.4.  Power calculations and expected sample size 
We employed historical discharge data from the two enrolling hospitals from 2003-2010 to 
perform power calculations.  First, we limited the sample to patients who are similar to those 
targeted by Link2Care:  two or more admissions in the prior six months, between the ages of 19 
and 80, two chronic conditions recorded in the diagnosis codes in the prior year, the chronic 
conditions could not all be mental-health conditions, and the patient was not diagnosed with 
cancer in the prior year.  Note that this approximates the inclusion/exclusion criteria used when 
determining eligibility through the HIE described above, but it does not include the criteria found 
in the electronic medical record. 
 
We determined that with 80% power and a test size of 0.05, a sample size of 800 would provide 
the statistical power to detect a 30% reduction in our primary outcome: 180-day hospital 
readmission.  Compared to the mean 180-day readmission rate of 0.30, this means that we would 
have the power to detect a drop to 0.21.  Although this is a large effect, it is within the set of 
estimates found in the literature for intensive case management featuring home visits described 
in the literature review. 
 
Another 180-day outcome where we would have the power to detect a 30% decline is the 
likelihood of 2 or more “hospital visits”, defined as outpatient ED visits plus inpatient 
readmissions, which averages 0.32.  Other 180-day outcomes where we have the power to detect 
a 20% change include any hospital visit (an outcome that averages 0.54) and the likelihood of 
any outpatient ED visit (an average of 0.42).4   
 
If we have the power to detect a given percentage change in an outcome at six months, we have 
the power to detect an effect at twelve months as well (e.g. 365-day readmission).  Other 
outcomes where we have the power to detect a 30% decline over the following 365 days include 
the number of hospital visits (which averages 2.7) and the likelihood of 3 or more visits (which 
averages 0.33).   
  
As a result of these power calculations, we plan to recruit until we attain a sample size of 800 
subjects.  Based on historical data on the number of eligible patients and the program capacity, 
we expect this will take 1-2 years.  CCHP is considering options that may increase the sample 
size of the study or the speed at which the target is reached (such as expanding to nearby ZIP 
codes). 
 
6.  Empirical model and planned tables 
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4 For the power calculations we could only observe outpatient ED visits, but we expect to observe all ED visits in 
the future.  Given the higher means associated with all ED visits, we will have more power to detect a given 
percentage change in this measure. 

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6.1.  Empirical model 
The main analysis will compare outcomes for those who were randomized to receive Link2Care 
compared to those who were randomized to the control group.  
Consider an outcome, ܻ, such as an indicator that the patient was readmitted to a hospital within 
180 days after discharge from the hospital.  For subject i, the estimating equation is: 
ܻ௜ൌߚ଴൅ߚଵͳሺTreatmentሻ௜൅ߚଶܺ௜൅ߝ௜ 
where ͳሺTreatmentሻ௜ is an indicator variable equal to one if the subject was randomized to the 
treatment group and zero if the subject was randomized to the control group;  ܺ௜ is a vector of 
control variables.  These control variables should be uncorrelated with the treatment indicator, 
but they can aid in the precision of the estimate.  We propose standard demographic and prior-
utilization controls:  age (5-year age bins), sex (indicator that the patient is male), race/ethnicity 
(indicators for African American and Hispanic), and measures of utilization in the 0-6 months 
prior to the enrollment admission and 7-12 months prior to the enrollment admission 
corresponding to the outcome in the model.5  For example, if the outcome is a readmission 
indicator, the prior-utilization controls will include the number of admissions over those time 
periods; if it is a hospital visit (inpatient readmission or emergency department visit), the prior- 
utilization controls will describe the number of such visits, and for hospital charges and receipts 
they will be the totals for those measures.   
ߚଵis the parameter of interest and measures the Intent to Treat:  the causal effect of being 
randomized into the treatment group.  This can differ from the effect of the program if there is 
non-compliance:  some subjects in the treatment group may not be treated and/or subjects in the 
control group may be treated.  For example, among those assigned to treatment, a patient who is 
transferred from the hospital to a long-term-care facility will no longer be eligible for home visits 
and Link2Care will never begin, or some subjects could withdraw from the study prior to 
treatment.6  We expect take up to be high, however, because it is currently close to 100% for 
those approached to be part of Link2Care, and we are randomizing subjects into treatment after 
they have consented to be part of the study.  We anticipate that subjects assigned to the control 
group will not participate in the program by design. 
To the extent that there is non-compliance, we will also estimate the effect of actually 
participating in Link2Care, using the random assignment into the treatment group as an 
instrumental variable for program participation.  That is, we will estimate for subject i:    
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5 In our power calculations, we considered variables that reduced the residual variation in the readmission outcomes, 
and we discovered that the number of admissions in the 0-6 months prior to admission for the index event, the 
number of admissions in the 7-12 months prior to the admission for the index event, age, and sex were useful in this 
regard.  We did not have the race/ethnicity variable available in our analysis dataset. 
6 We do not anticipate that the program will affect such transfers, but we can investigate by considering the rate at 
which subjects are discharged home. 

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ܻ௜ൌߨ଴൅ߨଵͳሺEnrolled in Link2Care)௜൅ߨଶܺ௜൅ߴ௜ 
This model will be estimated using Two-Stage Least Squares (2SLS), where the first stage is: 
ͳሺEnrolled in Link2Care)௜ൌߙ଴൅ߙଵͳሺTreatmentሻ௜൅ߙଶܺ௜൅߱௜ 
The resulting estimate of ߨଵ is the local average treatment effect:  the average treatment effect 
for those induced into program participation due to the random assignment into the treatment 
group. 
We will also consider time to readmission in days, which can be right-censored if the person has 
not been readmitted by the time of the analysis.  First, we will estimate Kaplan-Meier survival 
graphs associated with time to readmission for the treatment vs. the control group.  We will also 
estimate a Cox proportional hazard model for subject i: 
݄ሺݐሻ௜ൌ݄଴ሺݐሻሼߚଵͳሺTreatmentሻ௜൅ߚଶܺ௜ሽ 
where ሼߚଵሽ is the hazard ratio of interest. 
6.2.  Planned tables and figures 
The following tables list (1) variables that are available in the claims data from the hospitals that 
are part of the HIE, (2) data that we expect to capture in a baseline survey, and (3) data on the 
treatment intensity of the program that will be collected by CCHP.  Some of these variables we 
have not used in our preliminary analysis, and although we expect these standard variables will 
be of high quality, we cannot attest to the quality of every variable at this time.   
6.2.1.  Means comparison using administrative claims data 
Table 1 reports means of the characteristics of patients in the administrative claims data.  
Column 1 reports means for all inpatients admitted to the enrolling hospitals during our study 
period.  For each individual admitted over the trial period, we will randomly select one of their 
admissions to compare to the remaining columns. 
Column 2 reports means for all patients who meet the inclusion/exclusion criteria for the 
program.  The comparison of Columns 1 and 2 will highlight the extent to which the program 
targets high-cost patients.   
Our estimates provide a causal effect of treatment among the group of potential subjects who 
agree to be part of the study.  A natural question is how representative this group is compared to 
other high-cost patients who are less willing to participate.  Columns 3 and 4 compare those who 
are targeted for the intervention but do not agree to be part of the trial to those who do agree to 
participate.  The refusal rate will be the number of observations in the refused column (3) 
divided the number of observations in the targeted column (2). 

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Columns 5 and 6 compare the patients who are randomized into the treatment and the control 
groups.  This provides a check on whether the groups are similar to one another, as expected due 
to the randomization.  Column 7 reports the p-value from a test that the means in Columns 5 and 
6 are equal to one another. 
The first set of rows includes demographic characteristics including age, sex, race and ethnicity.  
We also observe the ZIP code of residence, which will be used to characterize the median 
household income of the area where the patient lives.   
 
We then characterize the index admission—the admission where the subjects are enrolled—
including the hospital of admission, and whether the admission type was categorized as an 
emergency.  Intensity of treatment at the index admission, such as length of stay, could, in 
principle, be affected by the treatment.  This will be described in Appendix Table 1 rather than in 
this table, which is meant to consider the balance of observables across the treatment and control 
groups. 
 
From the longitudinal discharge data, we can also observe hospital utilization in the year prior to 
the trial, and we will compare various summary measures across the groups.   We will also 
compare the primary payer, defined as the payer responsible for most hospital payments over the 
prior year.   
6.2.2.  Means comparisons of trial subjects at baseline 
In addition to the discharge data, upon consenting to be in the trial the enrollment staff conducts 
an intake survey.  Table 2 describes the variables that are available in the baseline survey and 
proposes to collapse some of the answers into categories for the sake of brevity.  In particular, 
the survey asks questions about the usual place where patients receive medical care, marital 
status and family support (which may be important here as Link2Care enlists the family to help 
the patient gain self-sufficiency), military service history (to aid in understanding what programs 
the patient might be eligible to receive), living arrangements, literacy, educational attainment and 
employment.  Two self-reported health questions are asked, as well as questions that verify the 
presence of certain conditions, including mental-health conditions, obesity, and substance abuse. 
To summarize the information collected at the time the participants are targeted, and from the 
baseline survey, CCHP calculates illness-severity scores, and we can compare these scores 
across the groups as well. 
6.2.3.  Outcomes 
Table 3 presents the results for the primary outcome: an indicator that the patient was readmitted 
to a hospital in the claims data within 180 days from discharge from the enrolling hospital.  

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To complement the analysis of the primary outcome, Figure 1 presents another way to 
characterize the time to a readmission:  the Kaplan-Meier estimate of the survival function.  For 
each day, D, this will plot the fraction of each group that has “survived” at least D days without a 
readmission.  At zero days both groups will have a probability of one, and the survival functions 
will fall as patients are readmitted over time.     
Of course we are interested in other outcomes as well, and although the power to detect 
differences for some of these outcomes is lower than the primary outcome, we may find effects 
that can yield insights into the sources of the main effects and inspire future research. 
Table 4 presents the results for secondary outcomes and other outcomes of interest.  Panel A of 
Table 4 includes estimates of effects on other types of hospital use within the same 180 days of 
discharge: any use of an emergency department and the combined outcome of any emergency 
department visit or inpatient readmission; as well as inpatient readmissions that are from the 
emergency department and those that are not from the emergency department, as it is possible 
that the program prevents emergency readmissions but may promote scheduled readmissions.  
We will also measure the intensity of hospital utilization by considering the number of 
readmissions, the likelihood of two or more readmissions, the number of days in the hospital, 
hospital charges, and hospital receipts.  Hospital charges and receipts reflect treatment intensity, 
but also reflect pricing decisions by hospitals and efforts to receive payment, which will impact 
the interpretation of the findings.  They are typically skewed as well, and we will report effects 
of the program on the median of these measures in robustness checks described below.  
We are interested in whether effects are found during the intervention versus longer-term 
outcomes where the power to detect effects is somewhat higher.  Panel B shows that we plan to 
consider 30-, 90- and 365-day measures of hospital use, as well as days to readmission estimated 
with a Cox proportional hazard model.   
Different types of patients may experience different casual effects of the program.  After 
consulting the literature and practitioners, Panel C describes subsets that we would like to 
investigate separately.  These include patients with more admissions in the prior year, as the 
literature suggests that effects may be larger for higher-cost patients:  we can consider whether 
this relationship continues to be found within this set of high-cost patients.  In our examination 
of historical claims data, 70.4% of patients likely targeted by the program had 3 or more visits in 
the prior year.  One innovation in this study is that we consider a large non-English speaking 
population in addition to English-speaking patients, and we can report differences in treatment 
effects across these two groups. 
Other subgroups of interest from the literature and the nature of the intervention include those 
who reported substance abuse, psychiatric disorders, and low family support at the baseline 
survey.  We will consider such groups if the subgroups represent a substantial share of patients, a 
calculation we will perform once we have the baseline survey results.  Although we are likely 

14 

underpowered to detect differences in effects across groups, we consider such comparisons 
important precursors for future research. 
6.2.4.  Program participation and treatment intensity 
Tables 3 and 4 report intent-to-treat effects associated with assignment to the treatment group.  
Table 5 describes the extent to which the treatment and control groups received CCHP services. 
First, program intensity is explored by reporting the means across the treatment and control 
groups in terms of:  whether the subject was assigned to treatment—expected to be 1 for 
treatment and 0 for controls as a check on any contamination.  Next, whether Link2Care was 
initiated will be considered.  Then we will report the length of the intervention in days and the 
number of home visits, which will include zeroes for subjects where Link2Care was not initiated.  
These estimates should be close to zero for the control group by design. 
The next set of characteristics provides some information on why Link2Care may not have been 
initiated. Patients who enter a long-term-care facility are not suitable for Link2Care, and this is 
not always known at the time of enrollment.  The claims dataset includes a discharge status, 
which reports whether the patient was discharged home or was transferred to another facility, 
which means the subject may begin the program but only once they return home.  The discharge 
status also records whether the patient expired in the hospital. 
For the treatment group, we will know more about adherence to the goals of Link2Care, which 
we will report in Panel C:  whether a visit to a PCP was achieved within 7 days, whether the 
patient visited a specialist if that was part of the discharge plan, aid in enrolling in welfare 
programs, whether transportation for appointments was arranged, enrollment in a medical day 
program for substance abuse or behavioral health, obtained an official identification or power of 
attorney, and whether the team advocated for the client over bills, court warrants, etc.  We will 
also report which of the two Link2Care teams the subject was assigned. 
6.2.5.  Robustness checks 
We will report empirical estimates using alternative empirical specifications, largely as a check 
on the robustness of the main results.   
First, Appendix Table 2 provides robustness checks for the primary-outcome result shown in 
Table 3, including estimates with and without controls, and estimates from a logit model.  In 
addition, estimates will be presented from instrumental variable (2SLS) estimation, which allows 
us to estimate the effect of Link2Care for those induced into the program due to the assignment 
to the treatment group: a local average treatment effect (LATE).  This will take into account the 
fact that some of those in the treatment group never enroll in Link2Care and cases where patients 
in the control group were somehow treated.   

15 

Appendix Tables 3A and 3B repeat the comparisons made in Table 4, but check for robustness to 
the estimator used (e.g. the logit model), the exclusion of controls, and the LATE estimates.  
Days to readmission is not considered in these tables, as this measure is censored. 
Appendix Table 4 reports intent-to-treat and LATE estimates for three hospital utilization 
outcomes:  inpatient readmission, any emergency department visit, and any hospital use 
(readmission or ED visit).  The estimates will be shown across four time horizons:  30, 90, 180, 
and 365 days.  
 
7.  Preliminary analyses 
7.1.  Who are super-utilizers? Historical data comparisons 
We analyzed a limited dataset of claims data from 2002-2011 available to CCHP for the two 
main Camden hospitals: Cooper and Lourdes.  These data contain a subset of the variables we 
will access in the future.  Consistent with the inclusion criteria, we limited the data to patients 
who are between ages 19 and 80 at the index admission and who have not been diagnosed with 
cancer.   
We then identified “super utilizers”—our proxy for patients likely targeted by Link2Care—as  
individuals who at any point had two admissions within a 6 month period (the second of these 
was designated as the index admission), and had at least 2 chronic conditions that were not 
exclusively mental health related.   
For patients with more than one admission, we randomly selected an index admission between 
2003 and 2009.  This ensures one year of pre- and 2 years of post-admission data.   
As can be seen from Appendix Table 5, the targeted patients tend to be older, less likely to be 
female, tend to come from ZIP codes with lower household incomes.  By design, they also had 
higher hospital use in the year prior to the index admissions:  2.8 inpatient admissions accruing 
close to $200,000 in hospital charges and $25,000 in hospital receipts.  These levels are 2.5 to 4 
times higher than other patients admitted to these hospitals, with 1.1 admissions, $50,000 in 
charges and $6,000 in receipts.  These differences did not converge in the year following the 
admission.  Rather, typical patients targeted by the program have 0.9 readmissions and 2.6 visits 
to the hospital when we include the emergency department.  Other patients have only 0.09 
readmissions on average, and 1 visit to the hospital.  In terms of hospital charges and receipts, 
those targeted by the program receive care that totals $73,000 in charges ($8500 in receipts) 
compared to $6600 in charges ($773 in receipts) for other patients. 
7.2.  Camden-area hospital coverage  

16 

The longitudinal discharge data that we will access includes information for the three main 
hospital systems in the Camden area: Cooper, Lourdes, and Virtua.  It is of course possible for 
subjects to be admitted to other hospitals. To the extent that the intervention impacts hospital 
choice, this could lead to different rates of Camden-area hospital readmissions between the 
treatment and control groups that could be unrepresentative of effects on readmissions to any 
hospital.  
To investigate this issue, we studied discharge data from New Jersey in 2011.  Although these 
data are not longitudinal in nature, they do allow us to identify the ZIP code of residence of 
patients admitted to each hospital. When we consider admissions of patients who reside in the 
ZIP codes where CCHP recruits patients, we find that 87.2% are admitted to Cooper, Lourdes, 
and Virtua hospitals.  Outside of these three hospital systems, the hospitals with the greatest 
share of patients from these ZIP codes are Kennedy Hospital, with 10.6%, and Underwood 
Hospital, with 0.43%.  As noted above, we are hopeful that the longitudinal discharge data will 
include these hospitals in the near future, which would bring the coverage rate up to 98.2%.7  
The next largest share is attributed to Capital Hospital, at 0.41%, which is located in Trenton, NJ:  
36 miles north of Cooper Hospital.  
Of course, it is also possible that patients are admitted to hospitals in other states, including 
hospitals across the Delaware River in Pennsylvania. We are current working to obtain discharge 
data from that state to see how often Camden-area residents are admitted to those hospitals.  
Further, we hope to consider other longitudinal data that includes all hospitals, as described in 
the next section. 
 
8.  Planned next steps 
This plan focuses on analyses using longitudinal discharge data from the main hospitals in 
Camden, NJ.  We are currently working to gain access to other data sources that will provide 
additional outcomes of interest. 
8.1. Centers for Medicare and Medicaid Services (CMS) data 
To complement the claims data from the three hospital systems, other measures of utilization 
may become available from the Centers for Medicare and Medicaid Services (CMS).  In 
particular, we are interested in enrollment into Medicaid, as this is often facilitated by 
Link2Care.  While Link2Care targets individuals who are insured, some are eligible for 
Medicaid but have not yet enrolled.   

7 For patients recruited from ZIP codes that are almost entirely within the Camden city limits (08102, 08103, 08104, 
08105), Cooper, Lourdes and Virtua hospitals admit 92.6% of patients, and adding Kennedy and Underwood 
increases the coverage to 98.3%. 

17 

For subjects who are covered by Medicare and/or Medicaid at the start of the study, we hope to 
study healthcare utilization.  First, we can revisit the readmission variable using all hospitals 
rather than the ones in our initial set.  Second, we can analyze outpatient utilization, which 
provides a broader picture of the costs of the program as well as potential mechanisms by which 
the program may reduce re-hospitalization.   
In particular, a goal of the program is to ensure that each patient sees a primary care physician 
with seven days after discharge and specialists soon after discharge if that is part of the discharge 
plan.  We hope to measure the rate at which this happens, as well as the number of physician 
visits over time.   
We anticipate that one mechanism for reducing re-hospitalization is closer monitoring of 
prescription medication, and we hope to measure the number of prescriptions filled and changes 
to the prescription regimen over time.  
Admission to a skilled nursing facility is costly, and we hope to use CMS data to estimate the 
effects of program participation on this outcome as well.   
In terms of a health outcome, we plan to consider is mortality, which is available from CMS 
data.  We believe mortality rates are not as high among this population that spans the entire adult 
age range compared to prior work that focused on the elderly, however.   
8.2 Social services data 
An innovation of the program is the coaching of subjects to navigate the social-services system 
to enroll in programs that they are eligible to receive.  We are investigating a variety of data 
sources to measure these outcomes.  From the NJ Department of Human Services, we hope to 
link the subjects to program participation data for TANF and SNAP programs.  From the NJ 
Department of Labor and the Pennsylvania Department of Labor, we hope to link subjects to 
unemployment insurance records to measure employment and wages.  From the NJ Department 
of Corrections, we hope to measure incarceration rates.   Homelessness services and housing 
stability programs administered by the Camden Housing Authority would allow us to measure 
participation as a potential mechanism for reducing hospital utilization.  We are also 
investigating other sources of mortality data, including Social Security Administration data and 
NJ Vital Statistics data. 
 
9.  Interpretation 
This trial holds the potential to evaluate the value of the Link2Care program in terms of 
subsequent hospital utilization, and provide insights into the types of patients who are most 
affected by Link2Care. 

18 

One caveat is that the program may have other benefits and costs that we are not currently 
measuring.  For example, Link2Care aims to improve health and quality of life.  We will attempt 
to measure these with mortality, healthcare utilization associated with greater limitations using 
the CMS data, and social-services data such as employment measures. 
Link2Care may also have costs beyond operational costs, including the greater use of outpatient 
care and prescription drugs.  Again, we aim to expand the data sources to include longitudinal 
data on all healthcare use. 
Second, the study will consider a program that is well established.  If we find large effects, it will 
be useful to investigate how the lessons learned by CCHP can be replicated in other areas.   
Third, the study considers the entire package of services provided by the Link2Care program.  It 
will not be possible to ascertain which components of the program are most effective or whether 
the impact is greater because they are offered together.  Subsequent research could consider the 
components separately. 
Fourth, the study is particularly appropriate to study “partial equilibrium” effects of Link2Care.  
If Link2Care were to greatly expand and dramatically affect hospital revenues, this could have 
broader effects that this study is not designed to estimate. 
 
 
 
 

19 

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better care?” The New Yorker. January 24, 2011. 
http://www.newyorker.com/reporting/2011/01/24/110124fa_fact_gawande 
Green, Steven; Singh, V; O'Byrne, W. Hope for New Jer's City Hospitals: The Camden Initiative
.  Perspectives in Health Information Management, Spring 2010. 
Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease 
rehospitalization: a randomized trial. Ann Intern Med 2009;150:178-87. 
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Naylor MD, Aiken LH, Kurtzman ET, Olds DM, Hirschman KB. The care span: The importance 
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All Inpatients
Targeted
Refused
Consented
Treatment
Control
p-value
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Age
Age under 35
Age 35-44
Age 45-54
Age 55-64
Age 65-74
Age 75+
Male
White
African American
Hispanic
ZIP Code:  Median HH Income
Admitted to Cooper Hospital
Admission type: Emergency
Number of admissions
Number of emergency 
department visits
Number of days in the hospital
Number of procedures
Hospital charges
Hospital payments
Medicaid
Medicare
Dual-eligible for Medicaid and 
Medicare
Private insurance
Self-pay
Observations
Source:  Claims data from hospitals in the Health Information Exchange.  Column (1) includes all patients admitted to Cooper and Lourdes 
Hospitals--the recruitment sources for the trial.  The primary payer will be determined by the payer responsible for the largest dollar-amount of 
claims over the year.  Column 7 reports the p-value for the null hypothesis that the means in columns (5) and (6) are equal to one another.  
Consented
Table 1: Means Comparisons
Targeted
Demographics
Primary payer
Index-admission characteristics:
Healthcare utilization in year prior 
to the admission:

Treatment
Control
p-value
(1)
(2)
(3)
Existing primary care
Has a Primary Care Physician (PCP)
Conditional on having a PCP:  Months since last visit
Most common place for care:
Doctor's office
Emergency room/Urgent care clinic
Other
Marital status
Married/Civil Union/Cohabitating
Single/Divorced/Widowed
Other/prefer not to say
Social network:  Family/friend 
supports healthcare needs
Never/rarely
Sometimes
Mostly/Always
Unable to respond
Military service
Active or Veteran
Living arrangements
Own house/apartment/room
With a friend/relative
Shelter/boarding home/streets
Other/prefer not to say
Language/Literacy
Preferred language: English
Preferred language: Spanish
Preferred method to learn new information includes 
Reading
Educational Attainment
Grades 6-8
Grades 9-12
GED
HS diploma
Some College/Associates Degree
Bachelor's degree +
Other/prefer not to say
Employment
Currently employed
Refused/Don't know
Self-reported health status
Excellent
 during past 12 months
Very Good
Good
Fair
Poor
Must stay in bed/need help of another person getting 
around
Not limited in any way
Common conditions
Mental health condition
Congestive Heart Failure
COPD/Emphysema
Coronary Artery Disease
Diabetes
HIV/AIDS
Obesity
Substance Abuse
Other recorded condition
CCHP aggregation
Pre-enrollment severity score from targeting info
Pre-enrollment severity score from baseline survey
Observations
Table 2: Baseline Survey Comparison:  Treatment vs. Control
Source:  Baseline Survey.  Column 3 reports the p-value for the null hypothesis that the means in columns (1) and (2) are equal to one another.  

Control group 
mean
Intent to Treat
Dependent Variable:
(1)
(2)
180-day any hospital readmission
Observations
Table 3:  Main Outcome: 180-day Readmission
Source:  Claims data from hospitals in the Health Information Exchange.  The 
intent to treat effect estimate comes from an OLS regression that includes 
controls for controls for age (5-year age bins), sex (indicator that the patient is 
male), race/ethnicity (indicators for African American and Hispanic), the  
number of admissions in the 0-6 months and 7-12 months prior to the 
enrollment admission, as described in the text.  Standard errors in parentheses.  
* significant at 10%; ** significant at 5%;  *** significant at 1%.

0.2
0.4
0.6
0.8
1
1.2
(survived at least D days to readmission)
Figure 1:  Kaplan-Meier Survival Function:  Days to Readmission
Treatment
Control
0
0
30
60
90
120
150
180
210
240
270
300
330
360
P(
D days to Readmission

Control group mean
Intent to Treat
(1)
(2)
Dependent Variable:
Hospital visits
Any emergency department (ED) use
Any hospital use (inpatient or ED)
Inpatient readmission from the ED
Inpatient readmission not from the ED
Treatment intensity
Number of readmissions
2+ readmissions
Number of days in the hospital
Hospital charges
Hospital receipts
B.  Time horizons
30-day readmission
90-day readmission
365-day readmission
365-day any hospital use (inpatient or ED)
Time to readmission (days):  hazard ratio
C.  Subgroups
Dependent Variable:  180-day readmission
Patients with 3+ readmissions in the prior year
Patients with 2 readmissions in the prior year
English speaking
Non-English speaking
Observations
A.  Additional 180-day Outcomes
Source:  Claims data from hospitals in the Health Information Exchange.  Time to readmission hazard ratio is estimated with 
Cox proportional hazard model; all other models are estimated using Ordinary Least Squares.  All models include controls for 
age (5-year age bins), sex (indicator that the patient is male), race/ethnicity (indicators for African American and Hispanic), the 
corresponding 0-6 month and 7-12 month prior-utilization controls as described in the text.      Standard errors in parentheses.  
* significant at 10%; ** significant at 5%;  *** significant at 1% for a test that the coefficient equals zero or the hazard ratio 
equals one.  
Table 4: Further Outcomes and Subgroups

Treatment
Control
p-value
(1)
(2)
(3)
Assigned to treatment group
Enrolled in Link2Care
Length of the intervention (days)
Number of home visits
home
transferred to another facility
expired
Treatment
Dependent Variable:
(1)
Visited PCP within 7 days of returning home
Visited specialist within 30 days as part of discharge plan
Enrolled in Medicaid
Enrolled in SNAP/TANF
Arranged transportation
Secured official identification/power of attorney
Enrolled in medical day programs (substance abuse & behavioral health)
Enrolled in disability/SSI
Enrolled in housing stability program
Table 5: Program Participation & Intensity
A.  Program intensity
B.  Discharge status from index admission
C.  Link2Care Goals and Services Provided
Advocated for patient (bills/court warrants/etc.)
Assigned to Team A
Observations
Source:  Panels A and C: CCHP program data; Panel B: Claims data from hospitals in the Health Information Exchange.  Column 3 
reports the p-value for the null hypothesis that the means in columns (1) and (2) are equal to one another. 

Treatment
Control
p-value
(1)
(2)
(3)
Length of stay in hospital
Number of procedures
Hospital charges
Hospital payments
Observations
Appendix Table 1:  Hospital Utilization at Index Admission
Source:  Claims data from hospitals in the Health Information Exchange.  Column 3 reports the p-
value for the null hypothesis that the means in columns (1) and (2) are equal to one another.  

Model
(1)
(2)
OLS
Logit
Model
(1)
(2)
2SLS
Controls
No
Yes
Control group mean
Observations
Appendix Table 2:  Checks for Main Outcome: 180-day Readmission
Source:  Claims data from hospitals in the Health Information Exchange.  Panel A 
includes estimates from a logit model, and the average marginal effect is reported.  
Controls are those desribed in Table 3.   Panel C is estimated using  2SLS, and the 
parameter estimate represents the average treatment effect for those iduced to 
participate in the program as a result of the random assignment to the treatment group.  
Standard errors in parentheses.  * significant at 10%; ** significant at 5%;  *** 
significant at 1%.
Dependent Variable: 180-day any hospital readmission
B.  Local average treatment effect (LATE)
Intent to Treat
LATE
A.  Intent to Treat

Model
Control group 
mean
Intent to Treat 
Estimate
(1)
(2)
Dependent Variable:
Hospital 
visits
Any emergency department (ED) use
Logit
Any hospital use (inpatient or ED)
Logit
Inpatient readmission from the ED
Logit
Inpatient readmission not from the ED
Logit
Treatment 
intensity
Number of readmissions
Poisson
2+ readmissions
Logit
Number of days in the hospital
Poisson
Hospital charges
Median regression
Hospital receipts
Median regression
B.  Time horizons
30-day readmission
Logit
90-day readmission
Logit
365-day readmission
Logit
365-day any hospital use (inpatient or ED)
Logit
C.  Subgroups
Dependent Variable:  180-day readmission
Patients with 3+ readmissions in the prior year
Logit
Patients with 2 readmissions in the prior year
Logit
English speaking
Logit
Non-English speaking
Logit
Observations
Appendix Table 3A:  Further Outcome Checks:  Alternative Estimation Models
A.  Additional 180-day Outcomes
Source:  Claims data from hospitals in the Health Information Exchange.  This table includes the analyses with controls 
as in Table 4 but using alternative estimators.  Average marginal effects are reported.  Standard errors in parentheses.  * 
significant at 10%; ** significant at 5%;  *** significant at 1%.  

Control 
group mean
(1)
(2)
(3)
(4)
(5)
Dependent Variable:
Hospital 
visits
Any emergency department (ED) use
Any hospital use (inpatient or ED)
Inpatient readmission from the ED
Inpatient readmission not from the ED
Treatment 
intensity
Number of readmissions
2+ readmissions
Number of days in the hospital
Hospital charges
Hospital receipts
B.  Time horizons
30-day readmission
90-day readmission
365-day readmission
365-day any hospital use (inpatient or ED)
C.  Subgroups
Dependent Variable:  180-day readmission
Patients with 3+ readmissions in the prior year
Patients with 2 readmissions in the prior year
English speaking
Non-English speaking
Controls
No
Yes
No
Yes
Observations
Appendix Table 3B: Further Outcomes and Sub-groups: LATE
A.  Additional 180-day Outcomes
Source:  Claims data from hospitals in the Health Information Exchange.  This table includes OLS and 2SLS estimates with controls correspoding 
to the analyses in Table 4.  For the 2SLS results, the parameter estimates represent the local average treatment effect (LATE) for those iduced to 
participate in the program as a result of the random assignment to the treatment group.  Standard errors in parentheses.  * significant at 10%; ** 
significant at 5%;  *** significant at 1%.  
Intent to Treat
LATE

Intent to Treat
Intent to Treat
LATE
LATE
(1)
(2)
(3)
(4)
Inpatient Readmission
30-day
90-day 
180-day
365-day 
Any emergency department (ED) use
30-day
90-day 
180-day
365-day 
Any hospital use (inpatient or ED)
30-day
90-day 
180-day
365-day 
Controls
No
Yes
No
Yes
Observations
Appendix Table 4:  Time Horizons for Additional Outcomes
Source:  Claims data from hospitals in the Health Information Exchange.  This table includes OLS estimates with 
controls similar to Table 4.  Standard errors in parentheses.  * significant at 10%; ** significant at 5%;  *** 
significant at 1%.  
Dependent Variable:

All Inpatients Not Targeted
Targeted
(1)
(2)
(3)
Demographics
Age
42.88
40.31
53.55
Female
0.62
0.65
0.53
Median Income
ZIP Code:  Distribution 
of top 5 most common
$23,719
08102
0.10
0.10
0.12
$27,980
08103
0.21
0.20
0.22
$24,631
08104
0.26
0.26
0.25
$31,896
08105
0.30
0.31
0.27
$51,599
08110
0.08
0.08
0.08
Number of admissions
1.43
1.1
2.81
Number of ED visits, not-
admitted
1.25
1.05
2.04
Number of hospital visits 
(admitted or ED)
2.68
2.15
4.85
Hospital charges
79212
50277
198896
Hospital receipts
9644
5975
24822
within 6 months
0.14
0.05
0.52
within 12 months
0.24
0.09
0.87
within 24 months
0.43
0.19
1.4
within 6 months
0.71
0.54
1.47
within 12 months
1.31
1.01
2.55
within 24 months
2.38
1.9
4.33
Hospital charges
within 6 months
11150
3205
44011
within 12 months
19594
6603
73332
within 24 months
32946
13139
114875
Hospital receipts
within 6 months
1303
375
5139
within 12 months
2269
773
8460
within 24 months
3863
1592
13257
Observations
25574
20595
4979
Source:  Historical claims data from Cooper Hospital and Our Lady of Lourdes Hospital 
admitted between 2003 and 2009.  Sample is restricted to patients between the ages of 19 and 80 
who have not been diagnosed with cancer.  Each observation is a randomly selected admission 
for an individual admitted to these hospitals.  Our proxy for "targeted" using claims data are 
those with two admissions within a 6 month period and had at least 2 chronic conditions that 
were not exclusively mental-health related.     
Number of hospital 
readmissions
Number of hospital visits 
(admitted or ED)
Appendix Table 5:  All Inpatients vs. Targeted Inpatients
Hospital use in the year prior to the index admission
Hospital use after the index admission