Informatics’ Role in Integrating Population and Patient-level Knowledge to Improve Care Transitions in Complex Chronic Disease


Hewner, S. & Seo, J.Y. (2014). Informatics’ Role in Integrating Population and Patient-level Knowledge to Improve Care Transitions in Complex Chronic Disease. Available in the Online Journal of Nursing Informatics (OJNI), 18 (2).


Hospital discharges are times of increased vulnerability for people with chronic disease and often result in costly re-hospitalizations because of inadequate care coordination. Although population and patient-centered models of complexity use different data sources, definitions, and methods, they share hospitalization rates as an outcome measure. This paper proposes a Complexity Segmentation and Care Integration Model (CSCIM) that integrates population and primary-care data, using a chronic-disease algorithm that divides the population into cohorts based on complexity and comorbidity. Based on the CSCIM, opportunities to improve care transitions for complex cases using health informatics are identified at the health system, population, and primary-practice level. The goal is to improve care continuity for complex patients as they move across settings through proactive, timely, holistic, coordinated, and patient-centered care. Nurse care coordinators, supported with informatics, lead in this model; ultimately, resulting in reduced rates of hospitalization, cost savings, improved quality, and greater satisfaction with care.

Keywords: chronic disease, care coordination, severity of illness, care transitions, population health, health informatics.

More than 60 million Americans are medically complex, based on the presence of multiple chronic conditions (Parekh, Goodman, Gordon, & Koh, 2011), and their healthcare services account for about three-quarters of total health spending (Paez, Zhao, & Hwang, 2009; Thorpe & Howard, 2006). Chronically-ill patients are especially vulnerable during care transitions between healthcare providers (Naylor, 2012; Parry, Min, Chugh, Chalmers, & Coleman, 2009). However, well-managed care-transition programs can reduce unnecessary re-hospitalization and cost-effectively improve the quality of care (Jack et al., 2009; Naylor & Sochalski, 2010). In contrast, poorly coordinated care transitions after hospital discharge for individuals with multiple chronic conditions are associated with adverse outcomes (Halasyamani et al., 2006; Hennessey & Suter, 2011) and threaten patient safety, often leading to cognitive and physical functional decline and severe medical complications (Arbaje et al., 2010). In addition, uncoordinated care during transitions causes high rates of healthcare utilization due to emergency-department visits and rehospitalizations, ultimately increasing healthcare costs associated with preventable utilization (Berwick, Nolan, & Whittington, 2008; Craig, Eby, & Whittington, 2011; Naylor, Aiken, Kurtzman, Olds, & Hirschman, 2011).

Despite their role in chronic-disease management, focus on patient-centered care, building long-term relationships with patients, and proximity to the community, professionals in the primary- care setting often work in isolation from other providers (Baker, Johnson, Macaulay, & Birnbaum, 2011; Bodenheimer, Chen, & Bennett, 2009; Naylor, 2012; Peek, 2009). Regardless of improvements in the use of electronic health records (EHR) in both hospitals and primary-care settings, it is unusual to find electronic health information that goes across practice settings. Even when discharge summaries are included in Regional Health Information Organization (RHIO) data, it can take up to seven days for summaries to be accessible in primary-care offices. Typically, the discharged patient is instructed to arrange follow-up care; thus, providers in primary care are often unaware the patient has been hospitalized or has visited the emergency room for some time — perhaps not until the patient returns to have prescriptions refilled a month later — and the opportunity to make minor corrections that could prevent re-hospitalization is lost. Thus, employing health informatics to improve care continuity across settings could reduce readmissions in the population with chronic disease.

This paper presents a Complexity Segmentation and Care Integration Model (CSCIM) that links population and patient-centered information to provide direction and to identify opportunities to improve transitions for complex patients. At the population level, existing claims support that information can be used to identify patients with multiple chronic diseases who have a high risk of readmission. Patient-specific information from primary care includes knowledge about current health status, medications, and the home environment. The hospital has information on the current episode of illness and the discharge plan. Unfortunately, this knowledge is not interoperable; that is, EHR information is not available to providers in different settings. The majority of patients, that is, those with only minor or no chronic disease, responds to usual care practices and does not need fully integrated care. However, for the patient with multiple chronic diseases, failure to integrate knowledge can lead to re-hospitalization and excess utilization (Berwick & Hackbarth, 2012). The CSCIM suggests that knowing which patients are likely to require additional coordination beyond usual care allows a provider to proactively address their needs. Informational continuity, based on interoperable and integrated EHR-and-claims data, facilitates identification of high-risk cases and improved care management in primary-care settings, where long-term relationships with patients are greatest. The integrated data is used to identify specific opportunities where simple health informatics approaches could improve transition coordination. These approaches would have the potential to transform care from reactive to proactive intervention at the time of care transitions, resulting in lower hospitalization rates, improved quality and satisfaction, and lower costs.

Complexity at Individual and Population Levels

At the primary-care level, patients are considered complex when they do not respond to usual care (Peek, Baird, & Coleman, 2009; Weiss, 2007). Defining complexity as interference with standard care, Peek and colleagues (2009) identified five person-specific domains associated with complexity, based on social determinants of health. These domains include illness, readiness to change, social, health system, and resources; each domain is further divided into two attributes, with each attribute scored on a four-point scale. The Minnesota Complexity Assessment method (MCA) measures complexity at the individual level in order to address the challenges providers face in the primary-care setting. Based on a score ranging from 0 to 30, the authors suggest the level of care coordination needed to address the individual’s needs. This type of person-centered knowledge is most likely to be available in primary-care settings (Peek, 2009), but its retrieval could be enhanced by health-information technology (Kvedar, Coye, & Everett, 2014). Primary care focused on complexity rather than specific chronic disease considers the individual and the social context holistically-- areas closely aligned with nursing’s expertise.

Population health management divides the total population into cohorts or a segment based on their level of health risk and customizes interventions to improve health outcomes (Felt-Liks & Higgins, 2011). Table 1 compares three population segmentation hierarchies. The health promotion model uses public health levels of prevention and health promotion (primary, secondary, and tertiary) to divide the population into four large cohorts (Homer & Hirsch, 2006). A second model called Bridges to Health (Lynn, Straube, Bell, Jencks, & Kambic, 2007), divides the population into eight groups in order to identify priorities for the use of interoperable health information. Both approaches utilize population segments to identify the health promotion and disease prevention needs of the population. However, a limitation of these models is that they do not include data definitions to create the segments, and they are not linked to health outcomes such as utilization and cost.

Table 1

Comparison of Population Segmentation Models based on Complexity

Health Promotion Levels (Homer & Hirsch, 2006) Bridges to Health Model (Lynn et al., 2007) Complexity Segments and Care Integration Model (CSCIM)
Safer healthier people Healthy Healthy, without chronic disease
Vulnerable People Maternal and infant health; Acutely ill At-risk / Minor Chronic Disease (hypertension or hyperlipidemia)
Afflicted without complications Chronic conditions, normal function; Stable but serious disability Uncomplicated Chronic / Major Chronic Disease (COPD, depression, DM, CAD)
Afflicted with complications Short period of decline before dying; Limited reserve and exacerbations; Frailty, with or without dementia Complex Chronic / Organ System Failure (HF, CKD)

Note. COPD = Chronic Obstructive Pulmonary Disease or Asthma; CAD = Coronary Artery Disease; DM = Diabetes Mellitus; HF = Heart Failure; CKD = Chronic Kidney Disease.

In contrast, the CSCIM uses a chronic disease complexity clinical algorithm, the COMPLEXedex™ (Hewner, in press), to rank individuals with multiple chronic diseases hierarchically into four complexity segments: healthy or without chronic disease, at-risk or minor chronic disease, major chronic disease without system failure, and complex chronic disease or organ system failure. These segments divide the population in standardized groups that control for variation in chronic-disease prevalence and severity. The COMPLEXedex™ complexity hierarchy is based on chronic-disease comorbidity for nine chronic diseases using 12 months of administrative-claims data. A major strength of the COMPLEXedex™ classification is that standardization of patients for complexity can be completed in any EHR by using the problem list, and standardization of complexity creates a common language between facilities. Another advantage of integrating claims data is the ability to evaluate health outcomes such as inpatient utilization and cost. Creating standardized segments allows evaluation of health outcomes and program effectiveness over time or across populations.

The power of the COMPLEXedex™ to evaluate health outcomes, such as readmissions, was demonstrated in a regional managed-care organization in 2009 (Hewner, in press). Nurses and pharmacists, supported by a claims-based electronic health record and alerts for complex cases, made outreach phone calls to recently discharged patients. In the outreach calls, nurses reconciled medications before and after discharge, arranged for primary-care follow-up, assessed for unmet patient needs, and offered additional services if needed. Table 2 lists chronic diseases associated with each segment and shows that complex cases are more likely to be hospitalized and more likely to avoid admissions with systematic care-transitions coordination. The program resulted in reduction in hospitalization rates (4.8 percent) and cost (over $16 M) for Medicare beneficiaries with chronic disease.

Table 2

Click to expand.

Relationship of Complexity Segments to Disease Categories and Prevented Hospitalization in Medicare Population, 2009 (N=59,319)

Note. Source is author’s unpublished data. Chronic disease includes uncomplicated & complex segment. HTN = Hypertension; HL = Hyperlipidemia; CV = Cardiovascular; COPD = Chronic Obstructive Pulmonary Disease; CAD = Coronary Artery Disease; DM = Diabetes Mellitus; HF = Heart Failure; CKD = Chronic Kidney Disease..
*Avoided admissions are calculated by subtracting the 2009 hospitalization rate from the 2008 rate and multiplying by number of cases in the disease category, then dividing by 1,000. Adapted from “Financial and Quality Outcomes Improve with Implementation of Population-based Care Transitions Management for Chronically Ill Elders,” by S. Hewner, in press.

The CSCIM links individual and population levels and suggests that more complex segments of the population have differing needs for health services and integration of care. At an individual level, both collaboration between providers and the need for biopsychosocial support varies based on complexity, with complex cases needing close collaboration in a fully integrated system (Peek, 2009). The at-risk segment of the population needs simple integration or linkage to services, while those with chronic illness may require care coordination. The most complex cases need fully integrated care, which includes managerial, relational, and informational continuity (DiStanislao, Visca, Caracci, & Moirano, 2011). In managerial continuity, providers across the health-delivery system work in a coordinated fashion, and in informational continuity, health information, including personal preferences and characteristics and social context, as well as information about the clinical condition, is shared among providers. Care coordination with a trusting, long-term coaching relationship, enhanced by “sophisticated clinical support and information management capability” is both the most effective (Craig et al., 2011, p. 4) and an example of relational continuity. Thus, health information technology and nursing informatics can support integrated care during transitions by linking data across settings. Specifically, nurses in the role of care coordinators, utilize knowledge about the discharge to provide immediate telephonic follow-up to high-risk clients.

Population and individual perspectives are synthesized in the CSCIM (see Figure 1). Based on the population-health segments created by the COMPLEXedex™, the model describes levels of integration need, including informational, relational, and managerial continuity for individuals with chronic disease. The level of integration ranges from wellness activities for the healthy or without chronic disease population, to linkage for the at risk with minor chronic disease patients, to care coordination for those with major chronic disease, and full integration for those with complex chronic disease. The CSCIM suggests that care integration can reduce healthcare system fragmentation by incorporating relational, management, and informational continuity (DiStanislao et al., 2011).

Relationship between population complexity segments (COMPLEXedex™), population health promotion, and care integration and continuity requirements in the Complexity Segment and Care Integration Model (CSCIM)

Figure 1. Relationship between population complexity segments (COMPLEXedex™), population health promotion, and care integration and continuity requirements in the Complexity Segment and Care Integration Model (CSCIM). Click to expand.

Role of Health Informatics

Informatics can support the integration of population and individual level data about chronic disease complexity. Population health informatics highlights trends in disease prevalence, impacts of health disparities, and geographic variation in risk (Mastrian, McGonigle, & Pavlekovsky, 2007). Historic claims are one source of population-based information, which can identify both support needs and risk levels, as well as compare program effectiveness in real-world situations through monitoring of quality measures, utilization, cost, and satisfaction in the target population over time (Foote, 2003). The potential for integrated health information to improve population health is enormous; however, as in the healthcare system, health information is discontinuous between providers of care, such as hospitals, pharmacies, outpatient EHRs, and public-health departments (Bakken, 2006; Kuhn et al., 2008; Marcotte, Kirtane, Lynn, & McKethan, 2014; Snee & McCormick, 2004). Although there has been progress in integrating administrative data at the federal, state, and regional levels through RHIOs, access to all-payer integrated administrative data is currently unfeasible; thus, the data mining into regional disease prevalence and trends in expense remains a patchwork of analyses.

At the population level, informatics can play a major role in improving care transitions through the development of standardized processes, improved communication, evaluation of performance measures, establishment of accountability, and strong care coordination (Health Information Technology Workgroup, 2010). Observational, comparative-effectiveness research requires robust measures of the severity of illness and the structuring of relevant information to make comparisons that impact care (Bakken, Stone, & Larson, 2008; D'Avolio, Farwell, & Fiore, 2010). Informatics research focuses on information structure, information processes such as data mining algorithms, and technologies that support knowledge generation from practice. Factors such as organizational context, intensity of the informatics intervention, and severity of illness must be considered (Bakken et al., 2008; Bowles et al., 2011). Comparative-effectiveness research needs to consider best practices in project implementation in order to integrate interventions into existing organizational contexts (Bakken & Ruland, 2009; Glasgow, 2010; Glasgow & Chambers, 2012; Kuhn et al., 2008; Weiss, 2007). Thus, population-based informatics can be used to evaluate health outcomes and to support integrated care across the continuum.

Patient-centered information is increasingly available in the form of EHRs. Spurred by government incentives to improve meaningful use of health-information technology (Marcotte et al., 2012), EHRs are utilized in many primary-care offices and hospitals. Meaningful use currently requires the ability to develop registries for chronic disease and report on each quality measure. Risk-standardized care management remains an area for improvement for comprehensive primary care (Shapiro, 2013). However, one area that has not been adequately addressed is the communication of information at the time of a patient’s transfer. Clinical informatics can facilitate complex chronic-care management through improving care between visits, promoting information sharing between clinicians, and identifying aggregate information that could improve care at the provider and system level (Bowles et al., 2011; Naylor et al., 2013; Weiss, 2007). For example, HIT could transmit real-time discharge information to the primary-care practice, incorporate this information in the office-based EHR, reconcile medications, and alert the provider to contact the patient to arrange follow-up care and complete an online survey, such as the Minnesota complexity assessment method (Peek et al., 2009), to screen for other sources of complexity that might require additional services. Aggregate data on population complexity could support program development to meet those needs, and linkage to past administrative claims for the population would support observational comparative-effectiveness research.

Transitions and Informatics

Evidence-based models of transitional care have been successfully translated into population-based interventions using informatics and telephone outreach to reduce readmissions and improve outcomes (Kvedar et al., 2014; Naylor et al., 2013). “Transitional care comprises a range of time-limited services that complement primary care and are designed to ensure healthcare continuity and avoid preventable poor outcomes among at-risk populations as they move from one level of care to another, among multiple providers and across settings” (Naylor & Sochalski, 2010, p. 12). A recent telephone survey (Holland, Mistiaen, & Bowles, 2011) examined adult medical-surgical patients’ unmet needs within the first week of discharge from the hospital. It was found that 73.8 percent had unmet educational needs. Multiple studies have found gaps in medication reconciliation that included duplicative, omitted, and contraindicated medications (McGaw, Conner, Delate, Chester, & Barnes, 2007; McMillan, Trompeter, Havrda, & Fox, 2013). More than half of Medicare fee-for-service patients rehospitalized within 30-days of discharge had no primary-care follow-up (Jencks, Williams, & Coleman, 2009). These gaps in care, including unmet educational needs, inappropriate medications, and lack of follow-up, create opportunities for informatics solutions.

Application of HIT into care management is critical to assure effective information exchange and better communication to improve outcomes during care transition (Craig et al., 2011; Golden, Tewary, Dang, & Roos, 2010; Hewner, in press). Telephone outreach and health monitoring (telehealth) is an innovative and cost-effective method for care coordination, which can target high-cost and high-risk patients with chronic diseases in the primary-care setting (Jack et al., 2009). Baker and colleagues (2011) found that chronically-ill Medicare beneficiaries who receive an integrated telehealth program with care management spent between 7.7-13.3 percent less on health care over two years, compared to controls. Mortality differences between intervention and control groups supports this intervention’s positive impact on healthcare outcomes.

A randomized controlled trial tested the efficacy of a telehomecare monitoring intervention compared to usual skilled home care for patients discharged for heart failure (Bowles et al., 2011). The study found that readmissions were reduced by three percent at 30 days and by six percent at 120 days in the telehomecare group, although the difference was not statistically significant. However, people in the intervention group showed significantly higher satisfaction regarding their care. For telephone outreach to be effective in preventing rehospitalization, it is critical that the outreach be completed by nurses who have detailed knowledge of the hospitalization and a broad knowledge of the individual’s medical and pharmacy history (Jack et al., 2009; McCauley, Bixby, & Naylor, 2006). Telehealth and informatics approaches could improve transitions and increase the spread of transition-care coordination (Kvedar et al., 2014).

Implications for Nursing Practice

To bridge the complexity between individual level and population level, there is a need to develop and test interventions in primary care by using health informatics (Monsen, Westra, Oancea, Yu, & Kerr, 2011). There are a number of simple informatics approaches that can facilitate transition coordination; these fall under the categories of data mining, developing algorithms to identify and flag high-risk cases, creating care alerts, and providing decision support. These approaches are employed at various settings to maximize information at the primary-care level, where the most specific and holistic patient-centered data is both defined and housed. However, even with discharge summaries available through the RHIO, there is a delay of between four and seven days for the information to get to the primary-care office.

Unless the patient informs the office about a hospitalization, there is no action taken until the individual returns for an office visit. Thus, the opportunity for immediate education to correct misconceptions, medication reconciliation, and provision of additional supports is lost. An informatics approach could ensure real-time sharing of information between facilities, creation of a flag to alert the practice to make an outreach call, and development of a tool to assess the level of social complexity to complement information in EHR on medical complexity. In addition, identification of risk for hospitalization is a critical component (Holland, Harris, Leibson, Pankratz, & Krichbaum, 2006). These approaches would allow for both informational and relational continuity as the patient returns home.

Linking Individual and Population Complexity

To reduce and prevent unplanned rehospitalizations, it is critical to predict potential risk factors which may lead to adverse events and to identify special health-care needs during care transition (Greenwald & Jack, 2009; Monsen, Westra, Yu, Ramadoss, & Kerr, 2009; Naylor et al., 2011). Currently, when a primary-care office receives population information about its patients, the information is for a single health plan, perhaps covering 20 percent of its roster. If data mining was centrally completed using all-payer administrative data to determine disease prevalence, medical expense, and trends in utilization in standardized reports, the roster for the primary-care practice could be used to extract the subset of information for the practice. This information could be used to identify the complexity segments, the size of the population with chronic disease, the specific chronic diseases that are associated with high hospital and emergency-department utilization, and changes in these rates of utilization over time. Thus, it would be possible to both predict the need for specific interventions and to evaluate the impact of these interventions, using comparative-effectiveness research.

The COMPLEXedex™ could also be applied at the practice level on the entire roster of patients using the active-problem list. Segmentation of the population based on complexity allows prediction of risk and facilitates development of programs to address the needs of vulnerable subgroups with chronic disease, promoting efficient and equitable resource utilization in health care (Berwick et al., 2008; Lynn et al., 2007; Hewner, Mehrok, Wu, & Deloresco, 2014). When an individual presents at the office or calls in, a flag could be added to indicate the level of complexity. This could assist in triage to the appropriate level of staff to evaluate the problem. If the administrative data described above is available for only a portion of the roster, this complexity flag could be applied to the entire roster, allowing staff to determine the need for specialized services for complex patients. It could help staff determine the population at very high risk for rehospitalization in order to target efforts to get additional interprofessional information to incorporate into the plan of care.

Linking Information across Settings

One of the obvious flaws in the management of transitional care is the fact that the healthcare system relies on the individual patient to convey information between settings at the time of discharge and that most often this occurs in the direction to “make a follow-up appointment with your primary-care provider.” Most hospitals have the ability to generate an electronic record of the patients discharged from their facility in the past 24 hours. A simple way to create a care alert to the primary-care office would be to compare its roster of patients to the hospital’s discharge list and create a message to the primary-care provider with the list of individuals discharged, the type of discharge, and the discharge diagnosis. This list would be available to the practice care coordinator to make outreach calls on the next working day. This approach assumes that the patient has given permission to share health information among providers and that this consent is housed at the RHIO.

Improving Decision Support in Primary Care

This leads to a final informatics approach: development of a decision-support tool to assist in management of the most complex cases. Based on Peek’s work on complexity domains, we know that the presence of chronic disease or even multiple chronic diseases is only one source of complexity. Peek’s (2009) complexity domains could be evaluated during a routine office visit for high-risk patients or as part of a post-discharge outreach call. This information could be stored in the EHR, and the complexity flag on the face sheet could be enhanced with care alerts about other sources of complexity. Additionally, a complexity score could be created to target the cases that need a full interprofessional team approach to proactively deal with their health concerns.

Integrating Individual and Population Knowledge

A nurse care-coordinator in the primary care office would have access to the EHR, timely notification of discharge, awareness of pre-existing complexity, and population knowledge to prioritize outreach. An initial phone call could include assessment of how the patient is managing and what knowledge the patient has about the course of the hospitalization and the ongoing treatment. The nurse could reconcile the patient’s medications with those being taken before the hospital stay, and evaluate the patient’s knowledge about new medications. If there is confusion, the case could be referred to the covering pharmacist. The nurse could assess safety in the home and the need for additional follow-up, and arrange for follow-up in the office. The decision-support interview could be completed, with responses saved directly into the EHR. This information could be conveyed to the entire health team, and very-high-risk cases could be added to the agenda for the next team meeting. The entire phone call could be integrated into the office work flow.


The CSCIM demonstrates how informatics can improve information transfer at the time of care transitions. Care-transitions research continues to expand the types of approaches and the populations addressed. This model helps us see the opportunities for scaling up to large populations using low-cost interventions supported by intensive informatics. It is designed to complement intensive programs focusing advanced practice nurses on individual cases. Integration of population and individual perspective on chronic disease complexity using informatics to support care coordination during transitions could result in a considerable reduction in healthcare costs and improved quality of population health.


Arbaje, A. I., Maron, D. D., Yu, Q., Wendel, V. I., Tanner, E., Boult, C., . . . Durso, S. C. (2010). The geriatric floating interdisciplinary transition team. Journal of the American Geriatrics Society, 58(2), 364-370. doi: 10.1111/j.1532-5415.2009.02682.x

Baker, L. C., Johnson, S. J., Macaulay, D., & Birnbaum, H. (2011). Integrated telehealth and care management program for Medicare beneficiaries with chronic disease linked to savings. Health Affairs, 30, 1689-1697. doi: 10.1377/hlthaff.2011.0216

Bakken, S. (2006). Informatics for patient safety: A nursing research perspective. Annual Review of Nursing Research, 24, 219-254.

Bakken, S., & Ruland, C. M. (2009). Translating clinical informatics interventions into routine clinical care: How can the RE-AIM framework help? Journal of the American Medical Informatics Association, 16(6), 889-897. doi: 10.1197/jamia.M3085

Bakken, S., Stone, P. W., & Larson, E. L. (2008). A nursing informatics research agenda for 2008–18: Contextual influences and key components. Nursing Outlook, 56(5), 206-214.e203. doi: 10.1016/j.outlook.2008.06.007

Berwick, D. M., & Hackbarth, A. D. (2012). Eliminating waste in US health care. The Journal of the American Medical Association, 307(14), 1513-1516. doi: 10.1001/jama.2012.362

Berwick, D. M., Nolan, T. W., & Whittington, J. (2008). The triple aim: Care, health, and cost. Health Affairs, 27(3), 759-769. doi: 10.1377/hlthaff.27.3.759

Bodenheimer, T., Chen, E., & Bennett, H. D. (2009). Confronting the growing burden of chronic disease: Can the US health care workforce do the job? Health Affairs, 28(1), 64-74. doi: 10.1377/hlthaff.28.1.64

Bowles, K. H., Hanlon, A. L., Glick, H. A., Naylor, M. D., Riegel, B., Shih, N. W., & Weiner, M. G. (2011). Clinical effectiveness, access to, and satisfaction with care using a telehomecare substitution intervention: A randomized controlled trial. International Journal of Telemedicine and Applications, 2011, Article ID 540138, 13 pages. doi: 10.1155/2011/540138

Craig, C., Eby, D., & Whittington, J. (2011). Care Coordination Model: Better care at lower cost for people with multipe health and social needs. Cambridge, Massachusetts: Institute for Healthcare Improvement.

D'Avolio, L. W., Farwell, W. R., & Fiore, L. D. (2010). Comparative effectiveness research and medical informatics. The American Journal of Medicine, 123(12, Suppl.), e32-e37. doi: 10.1016/j.amjmed.2010.10.006

DiStanislao, F., Visca, M., Caracci, G., & Moirano, F. (2011). Integrated systems and continuity in health care: A navigation through the concepts and models. Journal of Integrated Care Pathways, 15(3), 82-89. doi: 10.1258/jicp.2011.011008

Felt-Liks, S., & Higgins, T. (2011). Issue brief: Exploring the promise of population health management programs to improve health. Retrieved from

Foote, S. M. (2003). Population-based disease management under fee-for-service Medicare. Health Affairs, Suppl Web Exclusives, W3-342-356. doi: 10.1377/hlthaff.W3.342

Glasgow, R. (2010). HMC research translation: Speculations about makin it real and going to scale. American Journal of Health Behavior, 34(6), 833-840. doi: 10.5993/AJHB.34.6.17

Glasgow, R. E., & Chambers, D. (2012). Developing robust, sustainable, implementation systems using rigorous, rapid and relevant science. Clinical and Translational Science, 5(1), 48-55. doi: 10.1111/j.1752-8062.2011.00383.x

Golden, A. G., Tewary, S., Dang, S., & Roos, B. A. (2010). Care management's challenges and opportunities to reduce the rapid rehospitalization of frail community-dwelling older adults. Gerontologist, 50(4), 451-458. doi: 10.1093/geront/gnq015

Greenwald, J. L., & Jack, B. W. (2009). Preventing the preventable: Reducing rehospitalizations through coordinated, patient-centered discharge processes. Professional Case Management, 14(3), 135-140. doi: 10.1097/NCM.0b013e318198d4e1

Halasyamani, L., Kripalani, S., Coleman, E., Schnipper, J., van Walraven, C., Nagamine, J., . . . Manning, D. (2006). Transition of care for hospitalized elderly patients-Development of a discharge checklist for hospitalists. Journal of Hospital Medicine, 1(6), 354-360. doi: 10.1002/jhm.129

Health Information Technology Workgroup. (2010). Improving transitions of care with health information technology. Washington, DC: National Transitions of Care Coalition.

Hennessey, B., & Suter, P. (2011). The community-based transitions model: One agency's experience. Home Healthcare Nurse, 29(4), 218-232. doi: 10.1097/NHH.0b013e318211986d

Hewner, S. (in press). A population-based care transition model for chronically ill elders. Nursing Economic$.

Hewner, S., Mehrok, N., Wu, Y. W., & Deloresco, F. (2014). Impact of Intervention Intensity on Health Outcomes in Medicaid Adults with Chronic Disease: Comparative Effectiveness Research Using Existing Clinical Electronic Data. Manuscript submitted for publication.

Holland, D. E., Harris, M. R., Leibson, C. L., Pankratz, V. S., & Krichbaum, K. E. (2006). Development and validation of a screen for specialized discharge planning services.

Nursing Research, 55(1), 62-71.

Holland, D. E., Mistiaen, P., & Bowles, K. H. (2011). Problems and unmet needs of patients discharged "Home to Self-Care". Professional Case Management, 16(5), 240-250. doi: 10.1097/NCM.0b013e31822361d8

Homer, J. B., & Hirsch, G. B. (2006). System dynamics modeling for public health: Background and opportunities. American Journal of Public Health, 96(3), 452-458. doi: 10.2105/AJPH.2005.062059

Jack, B. W., Chetty, V. K., Anthony, D., Greenwald, J. L., Sanchez, G. M., Johnson, A. E., . . . Culpepper, L. (2009). A reengineered hospital discharge program to decrease rehospitalization: A randomized trial. Annals of Internal Medicine, 150(3), 178-187.

Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418-1428. doi: 10.1056/NEJMsa0803563

Kuhn, K. A., Knoll, A., Mewes, H. W., Schwaiger, M., Bode, A., Broy, M., . . . Ziegler, S. (2008). Informatics and medicine--From molecules to populations. Methods of Information in Medicine, 47(4), 283-295. doi: 10.3414/ME9117

Kvedar, J., Coye, M., & Everett, W. (2014). Connected health: A review of technologies and strategies to improve patient care with telemedicine and telehealth. Health Affairs, 33(2), 194-199. doi: 10.1377/hltaff.0992

Lynn, J., Straube, B. M., Bell, K. M., Jencks, S. F., & Kambic, R. T. (2007). Using population segmentation to provide better health care for all: The "Bridges to Health" model. Milbank Quarterly, 85(2), 185-208. doi: 10.1111/j.1468-0009.2007.00483.x

Marcotte, L., Kirtane, J., Lynn, J., & McKethan, A. (2014). Integrating health information technology to achieve seamless care transitions. Journal of Patient Safety. Advance online publication. doi: 10.1097/pts.0000000000000077

Marcotte, L., Seidman, J., Trudel, K., Berwick, D. M., Blumenthal, D., Mostashari, F., & Jain, S. H. (2012). Achieving meaningful use of health information technology: A guide for physicians to the EHR incentive programs. Archives of Internal Medicine, 172(9), 731-736. doi: 10.1001/archinternmed.2012.872

Mastrian, K., McGonigle, D., & Pavlekovsky, K. (2007). Information systems and case management practice series: Introduction to information systems and case management information system, part I (of III). Professional Case Management, 12(3), 181-183. doi: 10.1097/01.PCAMA.0000271370.31665.f1

McCauley, K. M., Bixby, M. B., & Naylor, M. D. (2006). Advanced practice nurse strategies to improve outcomes and reduce cost in elders with heart failure. Disease Management, 9(5), 302-310. doi: 10.1089/dis.2006.9.302

McGaw, J., Conner, D., Delate, T., Chester, E., & Barnes, C. (2007). A multidisciplinary approach to transition care: A patient safety innovation study. The Permanente Journal, 11(4), 4-9.

McMillan, A., Trompeter, J., Havrda, D., & Fox, J. (2013). Continuity of care between family poractice physicians and hospitalist services. Journal for Healthcare Quality, 35(1), 41-49. doi: 10.1111/j.1945-1474.2011.00165.x

Monsen, K. A., Westra, B. L., Oancea, S. C., Yu, F., & Kerr, M. J. (2011). Linking home care interventions and hospitalization outcomes for frail and non-frail elderly patients. Research in Nursing & Health, 34(2), 160-168. doi: 10.1002/nur.20426

Monsen, K. A., Westra, B. L., Yu, F., Ramadoss, V. K., & Kerr, M. J. (2009). Data management for intervention effectiveness research: Comparing deductive and inductive approaches. Research in Nursing & Health, 32(6), 647-656. doi: 10.1002/nur.20354

Naylor, M. D. (2012). Advancing high value transitional care: The central role of nursing and its leadership. Nursing Administration Quarterly, 36(2), 115-126. doi: 10.1097/NAQ.0b013e3182a040b

Naylor, M. D., Aiken, L. H., Kurtzman, E. T., Olds, D. M., & Hirschman, K. B. (2011). The importance of transitional care in achieving health reform. Health Affairs, 30(4), 746-754. doi: 10.1377/hltaff.2011.0041

Naylor, M. D., Bowles, K. H., McCauley, K. M., Maccoy, M. C., Maislin, G., Pauly, M. V., & Krakauer, R. (2013). High-value transitional care: Translation of research into practice. Journal of Evaluation in Clinical Practice, 19(5), 727-733. doi: 10.1111/j.1365-2753.2011.01659.x

Naylor, M. D., & Sochalski, J. A. (2010). Scaling up: Bringing the transitional care model into the mainstream. Issue Brief (Commonw Fund), 103, 1-12.

Paez, K. A., Zhao, L., & Hwang, W. (2009). Rising out-of-pocket spending for chronic conditions: A ten-year trend. Health Affairs, 28, 15-25. doi: 10.1377/hlthaff.28.1.15

Parekh, A. K., Goodman, R. A., Gordon, C., & Koh, H. K. (2011). Managing multiple chronic conditions: A strategic framework for improving health outcomes and quality of life. Public Health Reports, 126(4), 460-471.

Parry, C., Min, S., Chugh, A., Chalmers, S., & Coleman, E. A. (2009). Further application of the care transitions intervention: Results of a randomized controlled trial conducted in a fee-for-service setting. Home Health Care Services Quarterly, 28(2-3), 84-99. doi: 10.1080/01621420903155924

Peek, C. J. (2009). Integrating care for persons, not only diseases. Journal of Clinical Psychology in Medical Settings, 16(1), 13-20. doi: 10.1007/s10880-009-9154-y

Peek, C. J., Baird, M. A., & Coleman, E. (2009). Primary care for patient complexity, not only disease. Families, Systems, & Health, 27(4), 287-302. doi: 10.1037/a0018048

Shapiro, R. (2013, June). Fixing primary care: Insights from high-functioning practices in the comprehensive primary care initiative. In R. Berenson (chair), Learning from the best: Fulfilling the potential of primary care. Symposium conducted at the annual research meeting of the Academy Health, Baltimore, MD.

Snee, N. L., & McCormick, K. A. (2004). The case for integrating public health informatics networks. Engineering in Medicine and Biology Magazine, IEEE, 23(1), 81-88. doi: 10.1109/memb.2004.1297178

Thorpe, K. E., & Howard, D. H. (2006). The rise in spending among Medicare beneficiaries: The role of chronic disease prevalence and changes in treatment intensity. Health Affairs, 25(5), w378-w388. doi: 10.1377/hltaff.25.w378

Weiss, K. B. (2007). Managing complexity in chronic care: An overview of the VA state-of-the-art (SOTA) conference. Journal of General Internal Medicine, 22(3), 374-378. doi: 10.1007/s11606-007-0379-x

Author Bios

Sharon Hewner, PhD, RN
Sharon is an Assistant Professor at the University at Buffalo, School of Nursing, State University of New York where she teaches in the doctoral and masters in Nursing Leadership programs. Dr. Hewner has 20 years’ experience in clinical settings which range from homecare to intensive care and 12 years’ experience as a population health analyst employing large databases. Her research focuses on using health information technology to improve care transitions for the population cohort with chronic disease.

Jin Young Seo, MS, WHNP-BC, RN,
Jin Young is a PhD candidate and a research assistant at the Center for Nursing Research in the School of Nursing, University at Buffalo, Buffalo, NY.