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Original Investigation |

Patient-Centered Community Health Worker Intervention to Improve Posthospital Outcomes A Randomized Clinical Trial FREE

Shreya Kangovi, MD, MS1,2,3,4; Nandita Mitra, PhD4,5; David Grande, MD, MPA1,4,6; Mary L. White2; Sharon McCollum2; Jeffrey Sellman, BA1; Richard P. Shannon, MD1; Judith A. Long, MD1,4,6,7
[+] Author Affiliations
1Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia
2Penn Center for Community Health Workers, University of Pennsylvania, Philadelphia
3Spectrum Health Services, Inc, Philadelphia, Pennsylvania
4Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
5Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia
6Robert Wood Johnson Foundation Clinical Scholars Program, University of Pennsylvania, Philadelphia
7Philadelphia Veterans Affairs Center for Health Equities Research and Promotion, Philadelphia, Pennsylvania
JAMA Intern Med. 2014;174(4):535-543. doi:10.1001/jamainternmed.2013.14327.
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Published online

Importance  Socioeconomic and behavioral factors can negatively influence posthospital outcomes among patients of low socioeconomic status (SES). Traditional hospital personnel often lack the time, skills, and community linkages required to address these factors.

Objective  To determine whether a tailored community health worker (CHW) intervention would improve posthospital outcomes among low-SES patients.

Design, Setting, and Participants  A 2-armed, single-blind, randomized clinical trial was conducted between April 10, 2011, and October 30, 2012, at 2 urban, academically affiliated hospitals. Of 683 eligible general medical inpatients (ie, low-income, uninsured, or Medicaid) that we screened, 237 individuals (34.7%) declined to participate. The remaining 446 patients (65.3%) were enrolled and randomly assigned to study arms. Nearly equal percentages of control and intervention group patients completed the follow-up interview (86.6% vs 86.9%).

Interventions  During hospital admission, CHWs worked with patients to create individualized action plans for achieving patients’ stated goals for recovery. The CHWs provided support tailored to patient goals for a minimum of 2 weeks.

Main Outcomes and Measures  The prespecified primary outcome was completion of primary care follow-up within 14 days of discharge. Prespecified secondary outcomes were quality of discharge communication, self-rated health, satisfaction, patient activation, medication adherence, and 30-day readmission rates.

Results  Using intention-to-treat analysis, we found that intervention patients were more likely to obtain timely posthospital primary care (60.0% vs 47.9%; P = .02; adjusted odds ratio [OR], 1.52; 95% CI, 1.03-2.23), to report high-quality discharge communication (91.3% vs 78.7%; P = .002; adjusted OR, 2.94; 95% CI, 1.5-5.8), and to show greater improvements in mental health (6.7 vs 4.5; P = .02) and patient activation (3.4 vs 1.6; P = .05). There were no significant differences between groups in physical health, satisfaction with medical care, or medication adherence. Similar proportions of patients in both arms experienced at least one 30-day readmission; however, intervention patients were less likely to have multiple 30-day readmissions (2.3% vs 5.5%; P = .08; adjusted OR, 0.40; 95% CI, 0.14-1.06). Among the subgroup of 63 readmitted patients, recurrent readmission was reduced from 40.0% vs 15.2% (P = .03; adjusted OR, 0.27; 95% CI, 0.08-0.89).

Conclusions and Relevance  Patient-centered CHW intervention improves access to primary care and quality of discharge while controlling recurrent readmissions in a high-risk population. Health systems may leverage the CHW workforce to improve posthospital outcomes by addressing behavioral and socioeconomic drivers of disease.

Trial Registration  clinicaltrials.gov Identifier: NCT01346462

Figures in this Article

The Affordable Care Act holds health systems accountable for patient outcomes, such as primary care access,1 patient-reported quality of care,2 and hospital readmission.3 Patients with low socioeconomic status (SES) are at highest risk by these measures, particularly in the posthospital period: they have lower access to posthospital primary care,4,5 poorer quality of hospital care,6 and elevated risks of all-cause readmission and death after hospital discharge.711 To improve these outcomes, hospitals will need to address socioeconomic and behavioral risk factors that occur in low-SES patients’ homes and communities. The American Hospital Association12(p1) has urged hospitals to “evolve into health systems partnering with community organizations” and to consider acute illness as a signal that triggers patient-centered care extending beyond hospitalization. However, the current health care workforce may not be equipped to operationalize this shift. Traditional personnel often lack the time, skills, and community linkages required to address underlying socioeconomic drivers of disease. In addition, several studies have shown that low-SES patients may harbor mistrust of these personnel13,14 and prefer “empathic support”15,16 from someone with whom they can relate.

Community health workers (CHWs) are trained laypeople who share socioeconomic backgrounds with their patients, which facilitates the workers’ ability to provide support consistent with patients’ values and needs.17 There is evidence that CHWs can improve outcomes for patients with chronic conditions,1823 increase linkages to primary care after emergency visits for hypertension,24 and reduce hospital use for children with asthma.25 This evidence has prompted increased use of CHWs in new models of care, such as the Oregon Coordinated Care Organization.26 However, integration of CHWs into the health care workforce has been hampered by 3 factors. First, CHW interventions—including hiring, supervision, and work practices—are often poorly standardized and difficult for health systems to reproduce. Second, most published CHW interventions are disease specific, limiting generalizability to a growing patient population with multiple chronic diseases. Third, according to the Agency for Healthcare Research and Quality,27 many studies of CHW interventions within the United States are of low methodologic quality.

Our objective was to create a standardized, exportable CHW model—Individualized Management for Patient-Centered Targets (IMPaCT)—and conduct a randomized clinical trial to test its effect on posthospital outcomes among low-income, uninsured, or Medicaid patients hospitalized with varied conditions. These patients are at high risk for poor outcomes7,11 but, unlike Medicare patients, are often ineligible for care transition services. We hypothesized that, compared with patients receiving usual care, those receiving the intervention would have higher rates of posthospital primary care access and better posthospital outcomes, including higher quality of discharge communication, self-rated health, satisfaction, patient activation, and medication adherence and lower 30-day readmission rates.

This study was a 2-armed, single-blind, randomized clinical trial. The study was approved by the institutional review board of the University of Pennsylvania. Participants provided written informed consent and received a total of $30 in prepaid gift cards as study honoraria.

Study Setting and Participants

The study was conducted at 2 academically affiliated Philadelphia hospitals. Eligible patients were (1) aged 18 to 64 years; (2) observation patients or inpatients on the general medicine service between April 10, 2011, and October 30, 2012; (3) uninsured or insured by Medicaid; (4) English speaking; (5) on the day of admission, expected to be discharged to home as opposed to a postacute care facility; and (6) residents of 5 Philadelphia zip codes in which more than 30% of the residents lived below the federal poverty level and which accounted for 35% of all readmissions to study hospitals. One of the original inclusion criteria, the recommendation to follow up with a primary care provider (PCP) after hospital discharge, was removed on May 23, 2011, because this determination would have occurred after randomization.

Patient Enrollment

Each day, trained research assistants (RAs) used the electronic medical record to create a list of newly admitted eligible patients, ordered the list using a random number generator, and then approached patients in their hospital rooms until they enrolled 3 patients per day. After confirming eligibility and obtaining written informed consent, RAs conducted a verbal survey to obtain sociodemographic and health information. The RAs then notified a member of the study team (S.K.) not involved with outcomes assessment who assigned each patient to 1 of 2 study arms using a previously obtained computer-generated randomization algorithm using permuted variable block sizes with a concealed sequence.28 This study team member contacted CHWs when a participant was randomized to the intervention. A researcher external to the study team held a copy of the randomization algorithm for monitoring purposes. Patients were recruited between April 10, 2011, and October 30, 2012, at which time the trial was stopped because we had reached our prespecified sample size target. Follow-up surveys were completed on December 18, 2012.

Procedures

Control arm patients received routine hospital care, including daily multidisciplinary rounds to discuss patients’ discharge needs. Nurses reconciled medication changes and handed patients written discharge instructions and prescriptions. Primary care physicians within the health system received an electronic discharge summary within 30 days of discharge.

The intervention arm also received IMPaCT, a CHW model developed by the study team based on qualitative participatory action research with high-risk, low-SES patients.15,29 The IMPaCT model consisted of 3 elements that are described in detailed protocols and available online30: (1) recruitment and hiring guidelines, (2) training, and (3) standardized work practices. For recruitment and hiring, the IMPaCT protocol specified minimum educational requirements (high school diploma) and guidelines for selecting CHWs with personality traits that low-SES patients identified as important (eg, active listening). For training, the IMPaCT protocol included a college-accredited month-long training course designed to give CHWs the skills necessary to address barriers reported by patients. Other topics included motivational interviewing, professional boundaries, and core competencies of community health work.31

Finally, the IMPaCT protocol defined standardized work practices that CHWs use to provide tailored care in 3 stages: goal setting, goal support, and connection with primary care. On the day of hospital admission, CHWs used an in-depth semistructured interview guide to help patients set goals for recovery. This interview began with the open-ended question: “What do you think you will need to do to stay healthy after discharge?” The CHWs then worked with patients to create an individualized action plan for achieving each of these patient-centered goals. Each plan comprises 4 elements: a measureable goal, patient confidence in achieving the goal,32 resources, and a step-by-step plan for goal achievement. This approach builds on the technique developed by the Community Health Access Project33 but allows patients and CHWs to develop individualized action plans rather than selecting from prespecified standard templates.

During hospitalization, CHWs served as liaisons between patients and the inpatient care team, explaining patients’ goals to the team and ensuring that patients could execute the team’s discharge instructions. During and after hospitalization, CHWs provided tailored support based on patients’ goals using telephone calls, text messages, and visits. For example, if a patient’s stated goal was to quit smoking, the CHW might have accompanied the patient to a smoking cessation class. Finally, CHWs connected patients to primary care. If patients lacked a PCP or were dissatisfied with their current PCP, CHWs helped patients select suitable providers based on referrals from other patients and features, such as cost or proximity to public transportation. The CHWs coached patients to schedule and attend appointments and offered to accompany patients to the first posthospital appointment. During this appointment, CHWs provided PCPs with the medical discharge summary and an overview of patients’ action plans. The intervention lasted until the posthospital primary care appointment and a minimum of 14 days.

For this study, 2 CHWs (M.L.W. and S.M.) were hired as full-time employees earning $14 per hour. These CHWs were initially supervised by the principal investigator (S.K.) and subsequently supervised by a master’s-level social worker with experience in case management and community organizing. The manager followed standardized supervision manuals to provide oversight through real-time consultation and biweekly individual meetings, telephone contact, and documentation review. During biweekly meetings, the manager reviewed the action plans for each patient in the CHWs’ active caseload, providing suggestions to facilitate goal achievement. The manager also facilitated a weekly team meeting with all CHWs to discuss patient cases and provide ongoing training on topics that CHWs deemed important to their work.

Baseline Measures

Upon enrollment, RAs collected the following baseline data: age, race,34 ethnicity,34 employment,34 household income and size,34 unmet or delayed need for medical care,35 number of emergency department visits and hospital admissions in the prior 12 months,35 and usual source of care.35 In addition, the baseline questionnaire included the 12-Item Short Form Health Survey,36 the Patient Activation Measure,37 the Enhancing Recovery In Coronary Heart Disease Social Support Inventory,38 the Single Item Literacy Screener,39 single-item drug40 and alcohol41 screening questions, and questions assessing homelessness and food insecurity.42 Additional clinical information collected from the electronic medical record included patient International Classification of Diseases, Ninth Revision codes and the Emergency Severity Index.43,44

Outcome Measures

The prespecified primary outcome was patient-reported completion of a follow-up appointment with a PCP within 14 days of discharge. We chose this as our primary outcome based on prior qualitative work15,29 suggesting that low-SES patients viewed lack of access to posthospital primary care as a key barrier to recovery.

Prespecified secondary outcomes were defined and measured according to the licensing instructions of validated instruments. We were interested in measuring the effectiveness of the intervention in improving outcomes that were of interest to low-SES patients15 and reflected domains of enhanced patient experience, improved health, and controlled acute care use.45 We measured high-quality discharge communication, defined as a positive answer to the Hospital Consumer Assessment of Healthcare Providers and Systems survey2 question, “During this hospital stay, did doctors, nurses, or other hospital staff talk with you about whether you would have the help you needed when you left the hospital?” We measured the mean change in patient activation37 between the baseline and follow-up surveys according to licensing instructions for the Patient Activation Measure.37 We calculated mean satisfaction on a 5-point scale using the general satisfaction domain of the Patient Satisfaction Questionnaire Short Form.46 We defined perfect medication adherence as a score of zero on the Morisky Medication Adherence scale.47 We measured health outcomes using mean change in self-rated mental and physical health48 between baseline and follow-up using the licensing instructions for the complete 12-Item Short Form Health Survey instrument. Finally, we summarized rates of all-cause 30-day readmission to any hospital in Pennsylvania in 2 prespecified ways: the proportion of patients readmitted within 30 days after the index discharge and the proportion of readmitted patients who experienced multiple 30-day readmissions. After determining that our data source could not consistently collect information on emergency department visits, this secondary outcome was removed on June 3, 2011.

Research assistants blinded to study arms and hypotheses used standardized interview guides to collect patient-reported outcomes 14 days after discharge. The RAs made 4 telephone calls and 1 home visit attempt before considering patients lost to follow-up. Readmission data were collected by linking patient identifiers with the Pennsylvania Health Care Cost Containment Council49 statewide database for all hospital discharges across Pennsylvania.

Process Measures

For intervention patients, CHWs documented process measures, including initiation of intervention, CHW presence during discharge instructions and PCP appointment, and notes on progress toward achieving patients’ goals. The RAs who were not blinded conducted brief exit interviews with intervention patients asking their “thoughts on the IMPaCT Project” and the intervention component they found most helpful.

Statistical Analysis

We based our sample size estimate on a 15% increase in completed follow-up with a PCP within 14 days following hospital discharge in the intervention arm compared with a previously reported4,50 follow-up estimate of 49% in the control arm. We estimated requiring 192 patients per arm to allow detection of this difference with 80% power and a type I error rate of 5%.

Descriptive comparisons between group baseline characteristics and outcomes used χ2 tests for categorical variables and 2-tailed, unpaired t tests, as well as the Wilcoxon rank-sum test for continuous variables. Confidence intervals for absolute risk differences were calculated using the Agresti-Caffo method.51 Multivariable regression models, logistic for binary outcomes and linear for continuous outcomes, included study arm and all baseline covariates differing between arms, with the threshold set at P < .10 (number of usual care providers and insurance status). We also included baseline health and activation scores in the linear regression models for change in health and activation, respectively.

We performed an intention-to-treat analysis based on random assignment and used multiple imputation for missing data,52 simulating a multivariate normal distribution for all variables in the primary analysis model as well as those associated with missing outcome information. Each of 5 imputed data sets was analyzed and the results were combined for inference. To assess the robustness of our results, we performed 3 additional analyses: using multiple imputation with chained equations,53 including only patients with complete data, and assuming loss to follow-up as failure. The results of these sensitivity analyses were similar to those of the original model, and we report only the results of the original model.

We analyzed open-ended feedback from intervention arm patients using a grounded theory approach.54 First, transcribed data were imported into NVivo 10.0 (QSR International Pty Ltd), a qualitative analysis software program.55 We then developed a coding structure that included major ideas that emerged from a close reading of the data. Two independent members of the study team (S.K. and J.S.) coded transcribed data and resolved discrepancies by consensus.

Study Patients

Of the 709 patients we screened, 26 were found to be ineligible because they were non-English speaking, did not reside in study zip codes, or were expected to be discharged to a postacute care facility. Of the remaining 683 eligible patients, 237 (34.7%) declined to participate and 446 (65.3%) were enrolled (Figure). Participants and nonparticipants were similar in age (P = .49) and sex (P = .51). The mean age of the cohort was 44.5 years, 40.6% were male, 93.5% were black, 81.0% had an annual household income below $15 000, and 39.4% had unmet health needs. The 2 study arms were similar in all baseline characteristics with the exception of insurance status (P = .09) and percentage with a usual care provider (P = .04) (Table 1).

Place holder to copy figure label and caption
Figure.
Participant Flow

This figure outlines the Consolidated Standards for Reporting of Trials Diagram for this randomized clinical trial. IMPaCT indicates Individualized Management for Patient-Centered Targets.

Graphic Jump Location
Table Graphic Jump LocationTable 1.  Baseline Sociodemographic and Health Characteristics for 446 Participantsa

Nearly equal percentages of control and intervention patients completed the follow-up interview (86.6% vs 86.9%; P = .92). The 59 noncompleters were more likely than the 387 completers to be homeless (15.3% vs 2.3%; P < .001), to not be black (13.6% vs 5.4%; P = .02), to use illicit drugs (35.1% vs 19.6%; P = .01), and to have a longer index length of stay (mean [SD], 3.7 [5.7] vs 2.3 [2.6]; P = .003). Readmissions data were available for 98.7% of the enrolled patients.

Outcome Measures

A greater percentage of patients in the intervention arm completed posthospital primary care follow-up within 14 days of discharge compared with the control arm (60.0% vs 47.9%; P = .02). After adjustment for covariates imbalanced between groups, intervention patients had a 52% higher odds of completing posthospital primary care follow-up within 14 days (odds ratio [OR], 1.52; 95% CI, 1.03-2.23) (Table 2). Among unadjusted secondary outcomes, patients in the intervention arm were more likely to report high-quality verbal discharge communication (91.3% vs 78.7%; P = .002) and greater improvements in mental health (mean, 6.7 vs 4.5; P = .02) and activation (3.4 vs 1.6; P = .05). There was no significant difference between the arms in satisfaction with medical care (3.4 vs 3.4; P = .85), improvement in physical health (5.5 vs 4.8; P = .62), or perfect medication adherence (63.7% vs 59.3%; P = .37). There were 45 total readmissions in the control arm compared with 38 readmissions in the intervention arm. A similar proportion of patients in both arms experienced at least one 30-day readmission (15.0% vs 13.6%; P = .68). However, patients in the intervention arm were less likely to have multiple readmissions (2.3% vs 5.5%; P = .08). Among the subgroup of patients who were readmitted, the intervention decreased recurrent readmissions from 40.0% to 15.2% (P = .03).

Table Graphic Jump LocationTable 2.  Outcomes in 446 Participantsa
Process Measures

The CHWs met with 99.0% of patients during hospitalization within 24 hours of admission. Action plans commonly related to psychosocial issues (47.3%), insurance (23.9%), and medication costs (23.5%) (Table 3). Patients had a mean of 2.6 action plans. The CHWs helped patients to achieve a mean of 50.0% of their action plan goals. The CHWs were present at the time of discharge for 40.0% of intervention patients and communicated by telephone with an additional 49.5% within 48 hours of discharge. The CHWs were present at the initial visit with a PCP for 36.1% of the patients in the intervention arm. We compared all process and outcome measures between CHWs and found no significant differences.

Table Graphic Jump LocationTable 3.  Process Measures for 222 Participantsa

Most (79.7%) intervention patients provided positive open-ended feedback (Table 4). Patients appreciated that the intervention was tailored to their preferences. They described CHWs as advocates who were highly accessible, especially compared with other members of the health care team. Numerous patients said that CHWs were effective in “getting things done” that might have otherwise “slipped through the cracks.” Patients explained that CHWs worked “outside the box” in addressing psychosocioeconomic determinants of health. Patients thought that the most helpful component of the intervention was social support (54.2%) followed by establishing primary care (23.8%). Patients provided examples of instrumental (67.1%), emotional (19.3%), and informational (5.8%) social support provided by CHWs. Patients reflected on skills or traits that enabled CHWs to be effective: 18.5% described personal traits, 5.4% described organizational skills, and 3.2% described CHWs’ knowledge. Negative feedback was reported by 12.1% of the patients. Patients who already had support systems in place considered CHW support to be redundant. Others stated that their CHW was too busy with other patients to provide necessary support. Several patients thought that the intervention duration was too short.

Table Graphic Jump LocationTable 4.  Open-Ended Feedback From 222 Intervention Patients

Our results demonstrate that a brief CHW intervention improved posthospital primary care access, discharge communication, patient activation, mental health, and recurrent readmissions for a population of high-risk hospitalized patients with varied conditions. This study has 3 main findings that may help health systems improve the value of health care.

First, many interventions improve one dimension of health care—patient experience, health outcomes, or cost—at the expense of others. For example, prior interventions that increased primary care access for underserved patients led to significantly increased rates of readmission,50 possibly by increasing surveillance56 without addressing underlying drivers of poor health. This intervention simultaneously improved patient experience and health outcomes while controlling costly hospital use.

Second, we observed a modest reduction in recurrent hospital readmission in the overall cohort but a substantial reduction among the subgroup of readmitted patients. These findings raise questions about population management: should health systems strive to improve a variety of outcomes in a broader population of vulnerable patients or target a narrow population at high risk for a particular outcome (ie, hospital readmission)? A targeted strategy has 2 prerequisites. First, health systems require accurate real-time readmission risk prediction models, which are currently lacking.57 Second, health systems must forego benefit to patients who are not at high risk for readmission but may be at risk for other outcomes, such as lack of primary care access or poor health status. Further cost-effectiveness analysis will help inform the optimal targeting strategy for this intervention.

Third, the intervention offers health systems a scalable strategy for implementing the principle of patient-centered care. The CHWs used a standardized approach to create tailored action plans for achieving patients’ individualized goals. The fact that the intervention was patient rather than disease centered will enable health systems to use the same CHW recruitment, training, and work practice protocols for a range of patients across diseases and settings. In this study, patients often described psychosocial issues as barriers to recovery and appreciated the element of social support the most among components of the intervention. The CHWs provided creative types of social support, such as going with patients to local recreation centers or helping families budget their limited finances. This type of support is often absent from health care and hospital care in particular, suggesting that CHWs offer value because they deliver a kind of service that conventional health professionals do not.

This study has limitations. First, the intervention was brief, perhaps explaining why the effect sizes for improvement in mental health and activation were slightly lower than the 3-point minimal important difference thresholds for these scales. Also, persistence of benefit from this intervention depends on longitudinal care provided by PCPs. However, we believed that bridging care gaps during the high-risk posthospital period was an important first step. Second, because CHWs worked with inpatient care teams, there may have been some spillover of benefit from the intervention to control patients. This effect would bias our results toward the null. Third, we did not have access to data on emergency department visits, which would have been useful because low-SES patients are at high risk for requiring emergency department care.58 Fourth, this was a single-center study in a highly vulnerable population, which limits the generalizability of our findings. However, a strength of the study compared with many other CHW interventions is that we did not limit inclusion to patients with specific diseases. Finally, the primary outcome in this study was based on patient self-reported completion of the follow-up visit. Other studies59,60 have demonstrated high correlation between self-reports and documented health care use, particularly within a short recall period.

A patient-centered CHW intervention improved key outcomes among a population of low-SES adults with varied conditions. Hospitals have been challenged to transform into comprehensive health systems capable of responding to acute illness with proactive, patient-centered, and community-based care. This study may help inform health systems as they redesign their workforces and care practices to achieve this goal.

Accepted for Publication: November 16, 2013.

Corresponding Author: Shreya Kangovi, MD, MS, Department of Medicine, University of Pennsylvania Perelman School of Medicine, 1233 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104 (kangovi@mail.med.upenn.edu).

Published Online: February 10, 2014. doi:10.1001/jamainternmed.2013.14327.

Author Contributions: Dr Kangovi had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Kangovi, Grande, White, McCollum, Shannon, Long.

Acquisition of data: White, McCollum, Sellman, Long.

Analysis and interpretation of data: Kangovi, Mitra, Grande, Long.

Drafting of the manuscript: Kangovi, Long.

Critical revision of the manuscript for important intellectual content: Mitra, Grande, White, McCollum, Sellman, Shannon, Long.

Statistical analysis: Kangovi, Mitra, Sellman.

Obtained funding: Kangovi, Shannon, Long.

Administrative, technical, or material support: Grande, White, McCollum, Shannon, Long.

Study supervision: Mitra, Grande, Long.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by the Penn Center for Health Improvement and Patient Safety, the Leonard Davis Institute of Health Economics, the Penn Clinical and Translational Science Community-Based Research Grant, the Eisenberg Scholar Research Award, the Penn Department of Medicine, the Penn Presbyterian Department of Medicine, the Armstrong Founders Award, and the Penn Presbyterian Bach Fund.

Role of the Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Previous Presentations: Study findings were presented at the 2013 National Meeting for the Society of General Internal Medicine; April 2013; Denver, Colorado; and received the Mack S. Lipkin Award. Findings were also presented at the United Hospital Fund and Greater New York Hospital Association 24th Annual Symposium on Health Care Services in New York: Research and Practice.

Additional Contributions: David Asch, MD, MBA, Department of Medicine, University of Pennsylvania Perelman School of Medicine, provided insightful review of the article, and Christopher Wirtalla, BA, Leonard Davis Institute of Health Economics, University of Pennsylvania, provided independent analysis.

Correction: This article was corrected on February 14, 2014, to fix a URL in a reference.

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Lujan  J, Ostwald  SK, Ortiz  M.  Promotora diabetes intervention for Mexican Americans. Diabetes Educ. 2007;33(4):660-670.
PubMed   |  Link to Article
Heisler  M, Vijan  S, Makki  F, Piette  JD.  Diabetes control with reciprocal peer support versus nurse care management: a randomized trial. Ann Intern Med. 2010;153(8):507-515.
PubMed   |  Link to Article
Levine  DM, Bone  LR, Hill  MN,  et al.  The effectiveness of a community/academic health center partnership in decreasing the level of blood pressure in an urban African-American population. Ethn Dis. 2003;13(3):354-361.
PubMed
Brownstein  JN, Chowdhury  FM, Norris  SL,  et al.  Effectiveness of community health workers in the care of people with hypertension. Am J Prev Med. 2007;32(5):435-447.
PubMed   |  Link to Article
Bone  LR, Mamon  J, Levine  DM,  et al.  Emergency department detection and follow-up of high blood pressure: use and effectiveness of community health workers. Am J Emerg Med. 1989;7(1):16-20.
PubMed   |  Link to Article
Krieger  JK, Takaro  TK, Allen  C,  et al.  The Seattle–King County Healthy Homes Project: implementation of a comprehensive approach to improving indoor environmental quality for low-income children with asthma. Environ Health Perspect. 2002;110(suppl 2):311-322.
PubMed   |  Link to Article
Angus L, Cheney C, Clark S, Gilmer R, Wang E. Draft Recommendations for Core Competencies and Education and Training Requirements for Community Health Workers, Peer Wellness Specialists, and Personal Health Navigators. 2013. Oregon Health Policy Board. http://www.oregon.gov/oha/oei/Documents/nthw-report-120106.pdf. Accessed December 18, 2013.
Viswanathan  M, Kraschnewski  J, Nishikawa  B,  et al.  Outcomes of community health worker interventions. Evid Rep Technol Assess (Full Rep). 2009;(181):1-144, A141-142, B141-B114, passim.
PubMed
Piantadosi S. Block Stratified Randomization: Windows Version 6.0. West Hollywood, CA: Cedars-Sinai Medical Center, 2010.
Kangovi  S, Barg  FK, Carter  T, Long  JA, Shannon  R, Grande  D.  Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood). 2013;32(7):1196-1203.
PubMed   |  Link to Article
Kangovi S, Leri D, Clayton C, et al. Penn Center for community health workers. 2013. http://chw.upenn.edu. 2013. Accessed December 18, 2013.
Berthold  TA, Miller  J, Avila-Esparza  A. Foundations for Community Health Workers. San Francisco, CA: John Wiley & Sons, Inc; 2009.
MacGregor  K, Wong  S, Sharifi  C, Handley  M, Bodenheimer  T.  The action plan project: discussing behavior change in the primary care visit. Ann Fam Med. 2005;3(suppl 2):S39-S40.
PubMed   |  Link to Article
Redding M, Redding S. Community Health Access Project: pathways building a community outcome production model, 2010. http://chap-ohio.net/. Accessed August 2, 2012.
Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. http://www.cdc.gov/Brfss/. Accessed January 3, 2011.
Health Tracking Household Survey, 2007 Users Guide for Public-Use Data. Ann Arbor, MI: Inter-University Consortium for Political and Social Research; 2007. http://www.icpsr.umich.edu/cgi-bin/file?comp=none&study=26001&ds=1&file_id=1047080. Accessed December 18, 2013.
Maruish  ME, Kosinski  M. A Guide to the Development of Certified Short Form Interpretation and Reporting Capabilities. Lincoln, RI: Quality Metric Inc; 2009.
Hibbard  JH, Stockard  J, Mahoney  ER, Tusler  M.  Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4, pt 1):1005-1026.
PubMed   |  Link to Article
Mitchell  PH, Powell  L, Blumenthal  J,  et al.  A short social support measure for patients recovering from myocardial infarction: the ENRICHD Social Support Inventory. J Cardiopulm Rehabil. 2003;23(6):398-403.
PubMed   |  Link to Article
Morris  NS, MacLean  CD, Chew  LD, Littenberg  B.  The Single Item Literacy Screener: evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006;7:21. doi:10.1186/1471-2296-7-21.
PubMed   |  Link to Article
Smith  PC, Schmidt  SM, Allensworth-Davies  D, Saitz  R.  A single-question screening test for drug use in primary care. Arch Intern Med. 2010;170(13):1155-1160.
PubMed   |  Link to Article
Smith  PC, Schmidt  SM, Allensworth-Davies  D, Saitz  R.  Primary care validation of a single-question alcohol screening test. J Gen Intern Med. 2009;24(7):783-788.
PubMed   |  Link to Article
Gelberg  L, Gallagher  TC, Andersen  RM, Koegel  P.  Competing priorities as a barrier to medical care among homeless adults in Los Angeles. Am J Public Health. 1997;87(2):217-220.
PubMed   |  Link to Article
Gilboy  N, Travers  D, Wuerz  R.  Re-evaluating triage in the new millennium: a comprehensive look at the need for standardization and quality. J Emerg Nurs. 1999;25(6):468-473.
PubMed   |  Link to Article
Wuerz  RC, Milne  LW, Eitel  DR, Travers  D, Gilboy  N.  Reliability and validity of a new five-level triage instrument. Acad Emerg Med. 2000;7(3):236-242.
PubMed   |  Link to Article
Berwick  DM, Nolan  TW, Whittington  J.  The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27(3):759-769.
PubMed   |  Link to Article
Marhall  GN, Hays  RD. The Patient Satisfaction Questionnaire Short Form (PSQ-18). Santa Monica, CA: RAND; 1994.
Morisky  DE, Green  LW, Levine  DM.  Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67-74.
PubMed   |  Link to Article
Ware  J  Jr, Kosinski  M, Keller  SD.  A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233.
PubMed   |  Link to Article
Martin J. Pennsylvania Health Care Cost Containment Council. http://www.phc4.org/. Published 2012. Accessed December 18, 2013.
Weinberger  M, Oddone  EZ, Henderson  WG; Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission.  Does increased access to primary care reduce hospital readmissions? N Engl J Med. 1996;334(22):1441-1447.
PubMed   |  Link to Article
Agresti  A. Categorical Data Analysis.3rd ed. Hoboken, NJ: Wiley; 2013.
Little  RJA, Rubin  DB. Statistical Analysis With Missing Data.2nd ed. Hoboken, NJ: Wiley; 2002.
Donders  AR, van der Heijden  GJ, Stijnen  T, Moons  KG.  Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087-1091.
PubMed   |  Link to Article
Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin NK, Lincoln YS, eds. Handbook of Qualitative Research. Thousand Oaks, CA: Sage; 2000:1-14.
Guizzo  BS, Krziminski  CdeO, de Oliveira  DL.  The software QSR NVivo 2.0 in qualitative data analysis: a tool for health and human sciences researches [in Portuguese]. Rev Gaucha Enferm. 2003;24(1):53-60.
PubMed
McWilliams  JM, Zaslavsky  AM, Meara  E, Ayanian  JZ.  Impact of Medicare coverage on basic clinical services for previously uninsured adults. JAMA. 2003;290(6):757-764.
PubMed   |  Link to Article
Kansagara  D, Englander  H, Salanitro  A,  et al.  Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688-1698.
PubMed   |  Link to Article
Tang  N, Stein  J, Hsia  RY, Maselli  JH, Gonzales  R.  Trends and characteristics of US emergency department visits, 1997-2007. JAMA. 2010;304(6):664-670.
PubMed   |  Link to Article
Patel  A, Rendu  A, Moran  P, Leese  M, Mann  A, Knapp  M.  A comparison of two methods of collecting economic data in primary care. Fam Pract. 2005;22(3):323-327.
PubMed   |  Link to Article
Kennedy  AD, Leigh-Brown  AP, Torgerson  DJ, Campbell  J, Grant  A.  Resource use data by patient report or hospital records: do they agree? BMC Health Serv Res. 2002;2:2. doi:10.1186/1472-6963-2-2.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Participant Flow

This figure outlines the Consolidated Standards for Reporting of Trials Diagram for this randomized clinical trial. IMPaCT indicates Individualized Management for Patient-Centered Targets.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Baseline Sociodemographic and Health Characteristics for 446 Participantsa
Table Graphic Jump LocationTable 2.  Outcomes in 446 Participantsa
Table Graphic Jump LocationTable 3.  Process Measures for 222 Participantsa
Table Graphic Jump LocationTable 4.  Open-Ended Feedback From 222 Intervention Patients

References

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Lehrman B, Goldstein L. Hospital consumer assessment of healthcare providers and systems: 2012. http://www.hcahpsonline.org/surveyinstrument.aspx. Accessed August 6, 2012.
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Asplin  BR, Rhodes  KV, Levy  H,  et al.  Insurance status and access to urgent ambulatory care follow-up appointments. JAMA. 2005;294(10):1248-1254.
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Cleary  PD, Edgman-Levitan  S, Roberts  M,  et al.  Patients evaluate their hospital care: a national survey. Health Aff (Millwood). 1991;10(4):254-267.
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Forsythe S, Chetty VK, Anthony D, Johnson A, Greenwald J, Paasche-Orlow M. Risk score to predict readmission after discharge. Presented at: North American Primary Care Research Group Conference; October 15-18, 2006; Tucson, AZ.
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Elixhauser A, Au DH, Podulka J. Readmissions for chronic obstructive pulmonary disease, 2008: statistical brief 121. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD: Agency for Healthcare Research and Quality; September 2011.
Rodríguez-Artalejo  F, Guallar-Castillón  P, Herrera  MC,  et al.  Social network as a predictor of hospital readmission and mortality among older patients with heart failure. J Card Fail. 2006;12(8):621-627.
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Foraker  RE, Rose  KM, Suchindran  CM, Chang  PP, McNeill  AM, Rosamond  WD.  Socioeconomic status, Medicaid coverage, clinical comorbidity, and rehospitalization or death after an incident heart failure hospitalization: Atherosclerosis Risk in Communities cohort (1987 to 2004). Circ Heart Fail. 2011;4(3):308-316.
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Combes J. American Hospital Association Primary Care Workforce Roundtable (white paper). Chicago, IL: American Hospital Association; 2011.
Halbert  CH, Armstrong  K, Gandy  OH  Jr, Shaker  L.  Racial differences in trust in health care providers. Arch Intern Med. 2006;166(8):896-901.
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Armstrong  K, McMurphy  S, Dean  LT,  et al.  Differences in the patterns of health care system distrust between blacks and whites. J Gen Intern Med. 2008;23(6):827-833.
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Kangovi  S, Barg  FK, Carter  T,  et al.  Challenges faced by patients with low socioeconomic status during the post-hospital transition [published online August 6, 2013]. J Gen Intern Med. doi:10.1007/s11606-013-2571-5.
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Thoits  PA.  Stress, coping, and social support processes: where are we? what next? J Health Soc Behav. 1995(special issue):53-79.
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Israel  BA.  Social networks and social support: implications for natural helper and community level interventions. Health Educ Q. 1985;12(1):65-80.
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Long  JA, Jahnle  EC, Richardson  DM, Loewenstein  G, Volpp  KG.  Peer mentoring and financial incentives to improve glucose control in African American veterans: a randomized trial. Ann Intern Med. 2012;156(6):416-424.
PubMed   |  Link to Article
Gary  TL, Bone  LR, Hill  MN,  et al.  Randomized controlled trial of the effects of nurse case manager and community health worker interventions on risk factors for diabetes-related complications in urban African Americans. Prev Med. 2003;37(1):23-32.
PubMed   |  Link to Article
Lujan  J, Ostwald  SK, Ortiz  M.  Promotora diabetes intervention for Mexican Americans. Diabetes Educ. 2007;33(4):660-670.
PubMed   |  Link to Article
Heisler  M, Vijan  S, Makki  F, Piette  JD.  Diabetes control with reciprocal peer support versus nurse care management: a randomized trial. Ann Intern Med. 2010;153(8):507-515.
PubMed   |  Link to Article
Levine  DM, Bone  LR, Hill  MN,  et al.  The effectiveness of a community/academic health center partnership in decreasing the level of blood pressure in an urban African-American population. Ethn Dis. 2003;13(3):354-361.
PubMed
Brownstein  JN, Chowdhury  FM, Norris  SL,  et al.  Effectiveness of community health workers in the care of people with hypertension. Am J Prev Med. 2007;32(5):435-447.
PubMed   |  Link to Article
Bone  LR, Mamon  J, Levine  DM,  et al.  Emergency department detection and follow-up of high blood pressure: use and effectiveness of community health workers. Am J Emerg Med. 1989;7(1):16-20.
PubMed   |  Link to Article
Krieger  JK, Takaro  TK, Allen  C,  et al.  The Seattle–King County Healthy Homes Project: implementation of a comprehensive approach to improving indoor environmental quality for low-income children with asthma. Environ Health Perspect. 2002;110(suppl 2):311-322.
PubMed   |  Link to Article
Angus L, Cheney C, Clark S, Gilmer R, Wang E. Draft Recommendations for Core Competencies and Education and Training Requirements for Community Health Workers, Peer Wellness Specialists, and Personal Health Navigators. 2013. Oregon Health Policy Board. http://www.oregon.gov/oha/oei/Documents/nthw-report-120106.pdf. Accessed December 18, 2013.
Viswanathan  M, Kraschnewski  J, Nishikawa  B,  et al.  Outcomes of community health worker interventions. Evid Rep Technol Assess (Full Rep). 2009;(181):1-144, A141-142, B141-B114, passim.
PubMed
Piantadosi S. Block Stratified Randomization: Windows Version 6.0. West Hollywood, CA: Cedars-Sinai Medical Center, 2010.
Kangovi  S, Barg  FK, Carter  T, Long  JA, Shannon  R, Grande  D.  Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood). 2013;32(7):1196-1203.
PubMed   |  Link to Article
Kangovi S, Leri D, Clayton C, et al. Penn Center for community health workers. 2013. http://chw.upenn.edu. 2013. Accessed December 18, 2013.
Berthold  TA, Miller  J, Avila-Esparza  A. Foundations for Community Health Workers. San Francisco, CA: John Wiley & Sons, Inc; 2009.
MacGregor  K, Wong  S, Sharifi  C, Handley  M, Bodenheimer  T.  The action plan project: discussing behavior change in the primary care visit. Ann Fam Med. 2005;3(suppl 2):S39-S40.
PubMed   |  Link to Article
Redding M, Redding S. Community Health Access Project: pathways building a community outcome production model, 2010. http://chap-ohio.net/. Accessed August 2, 2012.
Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. http://www.cdc.gov/Brfss/. Accessed January 3, 2011.
Health Tracking Household Survey, 2007 Users Guide for Public-Use Data. Ann Arbor, MI: Inter-University Consortium for Political and Social Research; 2007. http://www.icpsr.umich.edu/cgi-bin/file?comp=none&study=26001&ds=1&file_id=1047080. Accessed December 18, 2013.
Maruish  ME, Kosinski  M. A Guide to the Development of Certified Short Form Interpretation and Reporting Capabilities. Lincoln, RI: Quality Metric Inc; 2009.
Hibbard  JH, Stockard  J, Mahoney  ER, Tusler  M.  Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4, pt 1):1005-1026.
PubMed   |  Link to Article
Mitchell  PH, Powell  L, Blumenthal  J,  et al.  A short social support measure for patients recovering from myocardial infarction: the ENRICHD Social Support Inventory. J Cardiopulm Rehabil. 2003;23(6):398-403.
PubMed   |  Link to Article
Morris  NS, MacLean  CD, Chew  LD, Littenberg  B.  The Single Item Literacy Screener: evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006;7:21. doi:10.1186/1471-2296-7-21.
PubMed   |  Link to Article
Smith  PC, Schmidt  SM, Allensworth-Davies  D, Saitz  R.  A single-question screening test for drug use in primary care. Arch Intern Med. 2010;170(13):1155-1160.
PubMed   |  Link to Article
Smith  PC, Schmidt  SM, Allensworth-Davies  D, Saitz  R.  Primary care validation of a single-question alcohol screening test. J Gen Intern Med. 2009;24(7):783-788.
PubMed   |  Link to Article
Gelberg  L, Gallagher  TC, Andersen  RM, Koegel  P.  Competing priorities as a barrier to medical care among homeless adults in Los Angeles. Am J Public Health. 1997;87(2):217-220.
PubMed   |  Link to Article
Gilboy  N, Travers  D, Wuerz  R.  Re-evaluating triage in the new millennium: a comprehensive look at the need for standardization and quality. J Emerg Nurs. 1999;25(6):468-473.
PubMed   |  Link to Article
Wuerz  RC, Milne  LW, Eitel  DR, Travers  D, Gilboy  N.  Reliability and validity of a new five-level triage instrument. Acad Emerg Med. 2000;7(3):236-242.
PubMed   |  Link to Article
Berwick  DM, Nolan  TW, Whittington  J.  The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27(3):759-769.
PubMed   |  Link to Article
Marhall  GN, Hays  RD. The Patient Satisfaction Questionnaire Short Form (PSQ-18). Santa Monica, CA: RAND; 1994.
Morisky  DE, Green  LW, Levine  DM.  Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67-74.
PubMed   |  Link to Article
Ware  J  Jr, Kosinski  M, Keller  SD.  A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233.
PubMed   |  Link to Article
Martin J. Pennsylvania Health Care Cost Containment Council. http://www.phc4.org/. Published 2012. Accessed December 18, 2013.
Weinberger  M, Oddone  EZ, Henderson  WG; Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission.  Does increased access to primary care reduce hospital readmissions? N Engl J Med. 1996;334(22):1441-1447.
PubMed   |  Link to Article
Agresti  A. Categorical Data Analysis.3rd ed. Hoboken, NJ: Wiley; 2013.
Little  RJA, Rubin  DB. Statistical Analysis With Missing Data.2nd ed. Hoboken, NJ: Wiley; 2002.
Donders  AR, van der Heijden  GJ, Stijnen  T, Moons  KG.  Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087-1091.
PubMed   |  Link to Article
Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin NK, Lincoln YS, eds. Handbook of Qualitative Research. Thousand Oaks, CA: Sage; 2000:1-14.
Guizzo  BS, Krziminski  CdeO, de Oliveira  DL.  The software QSR NVivo 2.0 in qualitative data analysis: a tool for health and human sciences researches [in Portuguese]. Rev Gaucha Enferm. 2003;24(1):53-60.
PubMed
McWilliams  JM, Zaslavsky  AM, Meara  E, Ayanian  JZ.  Impact of Medicare coverage on basic clinical services for previously uninsured adults. JAMA. 2003;290(6):757-764.
PubMed   |  Link to Article
Kansagara  D, Englander  H, Salanitro  A,  et al.  Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688-1698.
PubMed   |  Link to Article
Tang  N, Stein  J, Hsia  RY, Maselli  JH, Gonzales  R.  Trends and characteristics of US emergency department visits, 1997-2007. JAMA. 2010;304(6):664-670.
PubMed   |  Link to Article
Patel  A, Rendu  A, Moran  P, Leese  M, Mann  A, Knapp  M.  A comparison of two methods of collecting economic data in primary care. Fam Pract. 2005;22(3):323-327.
PubMed   |  Link to Article
Kennedy  AD, Leigh-Brown  AP, Torgerson  DJ, Campbell  J, Grant  A.  Resource use data by patient report or hospital records: do they agree? BMC Health Serv Res. 2002;2:2. doi:10.1186/1472-6963-2-2.
PubMed   |  Link to Article

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The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
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