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Journal of Nursing Administration:
doi: 10.1097/NNA.0000000000000087

Advancing the Evolution of Healthcare: Information Technology in a Person-Focused Population Health Model

Velianoff, George D. PhD, RN, FACHE, ANEF

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Author Information

Author Affiliation: Health Care Executive, Cerner Corporation, Kansas City, Missouri.

The author declares no conflicts of interest.

Correspondence: Dr Velianoff, 1855 Nashville Ln, Crystal Lake, IL 60014 (

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The current changes introduced into the healthcare delivery system through the Affordable Care Act require more than the isolated, quality/cost process solutions utilized to date. Robust information systems with capabilities to push information and provide valid analytics and decision support utilizing point-of-care data input are required to achieve a complex, person-centered, lifetime-focused model. This article presents a review of the current state of population health, a model identifying components within population health, and an example of information technology integration.

The passage of the Affordable Care Act and related legislation has provided an impetus for healthcare organizations and practices to transform and re-envision the care delivery model in the United States.1 The Centers for Medicare & Medicaid Services (CMS) Innovations Center has spurned a great deal of effort, programs, grants, demonstration projects, and transformation of the current care delivery models both in the acute and ambulatory environments.2 Within the new models, a population health perspective has been constructed to visually describe the ultimate goal of this transformation and includes a focus on primary care, care transitions, care coordination, integration of technology and analytics, and focus on attaining the triple aim: reduce costs, increase satisfaction, and increase accessibility.3 Accountable care organizations, the Patient-Centered Medical Home, transitions of care, value-based payment, and population management are concepts in the forefront.

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Current State

In preparation for the new era of population health, organizations have been putting into place programs, roles, and models to address transformative processes. There currently exists a variety of condition clinics such as heart failure clinics, diabetes clinics, and readmission reduction programs and care coordination efforts and roles. One of the dilemmas with the current work in the field is the noted isolation and silo approach to developing and implementing the various models and programs. Although an interdisciplinary approach is recommended in each program or model, there continues to be a lack of communication, coordination, and transitioning from 1 program, venue, or organization to the next, resulting in confusion, misunderstanding, and subpar outcomes by patients.4

Population health, actualized as the delivery of quality, evidence-based clinical services, requires additional skills and capabilities for the entire clinical team. Incorporating and understanding the provider/clinician perspective in care delivery and touch point technology can offer are necessary for transformation and adoption of new models and ways of delivering healthcare. Furthermore, the change from our current healthcare delivery model to future models requires more than structural integration, working together and cooperation; it requires true collaboration and teamwork. Care coordination and care across a person’s lifespan are 2 key foundational components and necessitate new workforce roles, skills and competencies, evidence based point-of-care and predictive information, and data analytics with the ability to analyze and interpret the information from a single consumer, the entire practice, to the larger community/population.3 In addition, an understanding of the principles of new reimbursement, revenue, and cost models as well as risk and incentive arrangements are needed. Partnering with expert clinicians is critical. Clinicians who understand the impact and need for information technology (IT) can assist in supporting the delivery of evidence-based efficient and effective care across the consumer lifespan and population.

Much work is being done in regard to patient engagement and patient-centered outcomes. Provisions in the Affordable Care Act,1 including creation of the Patient-Centered Outcomes Research Institute, are moving the industry toward greater patient engagement. Patient-centered care is healthcare that establishes a partnership among practitioners, patients, and their families to ensure that decisions respect patients’ wants, needs, and preferences. Patient-centered care ensures that patients have the education and support they need to make decisions and participate in their own care.5

A synthesis of current information available suggests specific areas and workflows of focus for a population health, patient-focused model (Figure 1):

Figure 1
Figure 1
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* defining the patient-centered care model

* identifying roles and responsibilities

* access

* measuring patient engagement

* establishing previsit planning

* implementing care coordination considering

○ complex patients

○ well patients

○ contractual requirements

* implementing care delivery (based on evidence)

* ensuring follow-up

* referrals as appropriate

* ensuring transitions and continuity

In addition, experience has demonstrated that a structured approach to transforming practices/organizations to a population health, patient-centered model is necessary and must involve appropriate representatives from the practice/organization as well as a project/program manager, an IT, and an executive sponsor. Operational considerations and inclusion of technology (health IT) must include the following

1. assessment of current and future state process identification, documentation, and data analytics

2. development of policies, procedures, collateral, and documents and incorporation of evidence

3. reporting/regulatory requirements

4. IT integration, optimization, and push capability

5. workflow changes

6. application of change strategies

7. education

8. outcome identification

9. continuous improvement

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Care Coordination/Managing Care

Several models are cited in the literature and have been utilized by organizations. Three models cited in the literature seem to be the lead models of choice when approaching care management for complex patients. One is the Integrated Case Management Model (ICM),6 the Coleman model,7 and the Transitional Care Model.8 Each model focuses on a holistic approach to patient interactions with the intent of determining key problems, barriers, and issues that need to be addressed specifically for the patient. The key issue in the ICM model6 is to assess the mental, social, and physical conditions of the patient through a relationship-based approach. The Coleman model7 focuses on patient inclusion and involvement around care transitions and education focused on 4 pillars of intervention: medications follow-up, patient-centered record, red flags, and patient contact across all care settings. The Transitional Care MODEL8 addresses the effects associated with breakdowns in care when older adults with complex needs transition from an acute care setting to their home or other care setting, with a focus in preparing patients and family caregivers to effectively manage changes in health.

Although the models present a good framework to assess, plan, and support care transitions and reductions in readmissions and include an interdisciplinary approach, none addresses specifically the entire continuum of care and need for coordination, communication, and oversight. The focus tends to remain at the specific encounter level rather than the overall population health approach. A model of care that spans the lifetime is important to consider. There are individuals who need intense care coordination (those with complex conditions and problems), whereas others need a wellness/prevention strategy. Several models are described in the literature around complex patient care coordination and patient-centered care, while the wellness/prevention aspect is less defined, but the health coach model is generally utilized.

To develop a framework that can encompass the care coordination, population health perspective of the future, we must start with a conceptual model and then identify an operational framework to use. Theories of health, healthcare, health delivery, and holism are found in the literature and can serve as a philosophical starting point in developing a framework for action. A comprehensive lifetime patient-focused model is depicted in Figures 1-4. Figure 1 describes the overall person-focused conceptual framework, whereas Figure 2 identifies the data and technological touch points required within the model. Figures 3 and 4 depict the various entry, exit points, and physical locations a person may interact with the healthcare delivery system and where technology integration is critical.

Figure 2
Figure 2
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Figure 3
Figure 3
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Figure 4
Figure 4
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Roles and Responsibilities

Attention and identification of the roles and responsibilities of the model chosen will need to occur. Decisions about roles need to complement the desired outcomes. Currently, there exists a plethora of roles and responsibilities. Decisions about the role(s) and qualifications need to be identified with the goal of utilizing each individual to their maximum level of practice. In addition, the level of competency around integrating IT, documenting data points, and analyzing data and its meaningful use within the clinical setting needs to be considered. A sampling of the roles and disciplines in patient-centered population health models is delineated in the Table 1. The roles are implemented and defined in various ways across venues and organizations.

Table 1
Table 1
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Some questions that can assist in defining the roles and competencies and where IT may assist include the following:

* Will the role of care coordinator span the acute care, ambulatory, and post–acute care venues?

* What patients will receive care coordinators? Only complex patients? Only those assessed as readmission risks? All patients?

* Will care coordination be divided based on patient complexity? Do those with complex conditions receiving care coordination and those with wellness/prevention need a health coach?

* How will “noncomplex” patient be handled and by what role(s)?

* Are roles practicing to their highest level permitted?

* Are there protocols, evidence-based guidelines, order sets, dashboards, and analytics developed that can assist each role/individual?

* How will we embed a culture of high performance, collaboration within the team, performance improvement, and a view of population and value-based services?

* What clinical and technical IT roles are needed to support the model of person-centric, population management?

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Applying IT

Clear evidence from the emerging population management models highlights the need to have technology implemented to assist in meeting the objectives of the programs and to meet the required reporting and measuring of outcomes included in the models.9,10 A focus on the point-of-care capabilities of technology to assist providers and organizations in their quest to meet the triple aim3 has been varied.

Insurance companies were early adopters in the utilization of technology to move to support the information needs of accountable care.11,12 Insurance companies are facing increased pressure to decrease premiums resulting in the development of new business models to maintain quality and lower cost. In response to this pressure, acquisitions of population health providers and analytics companies have increased. Vendors with success in analyzing and forecasting population data and cost are in demand.7,8 Other noninsurance companies have focused on aggregating claims and other data and using it for various purposes.11,12 Some are retooling to help clients develop and support clinical integration programs and other quality monitoring systems. Expertise in gathering claims data from multiple sources, loading it into a data warehouse, and creating customized reporting is in demand.

The need to document, collect, analyze, and report information about individuals and their healthcare experiences is voluminous and growing: meaningful use criteria, National Committee for Quality Assurance recognition, National Hospital Quality Measures, CMS, state programs to local regulatory bodies, and clinicians. An example is the Healthcare Effectiveness Data and Information Set (HEDIS).13 HEDIS is a tool used by more than 90% of US health plans to measure performance on important dimensions of care and service. Altogether, HEDIS consists of 75 measures across 8 domains of care. Because so many plans collect HEDIS data, and because the measures are so specifically defined, HEDIS makes it possible to compare the performance of health plans on an “apples-to-apples” basis. HEDIS is designed to provide purchasers and consumers with the information they need to reliably compare the performance of healthcare plans.13

As a provider, how can you ensure that data and information are entered at the point of care, that gaps and necessary compliance to quality metrics and services are offered and met, that there is data exchange across the continuum of care and across providers and settings of care, that analytics and outcomes are easily viewed, and that the reporting of data to external entities is accurate and complete? The electronic health record should be the framework to achieve these objectives and the 1st area to begin an assessment of current IT capabilities and gaps.10 Dashboards, analytics, and reports can guide the identification and inclusion of data acquisition points within the EHR.9

Defining evidence of best practices, point-of-care documentation, data entry, and information gathering is the most critical step. The EHR should offer the care providers an easy-to-use and accessible documentation system that incorporates evidence-based data points into the workflow of the team. In addition, push technology whereby the information system analyzes and then displays necessary care tasks, needs, and gaps in care and alerts the team member of needed action should be available. This exchange is commonly referred to as data liquidity. Data liquidity is a necessary component for managing care. Data liquidity has been described as data that are no longer confined to databases or data silos in supply chain management systems, financial systems, and health systems.14,15 Liquidity of data allows for the flow of data where and when needed, bringing information immediately to clinicians, patients, and others when and where they need it.14,15 A good clinical informatics resource with a population management focus, whether internal or contracted, is an invaluable resource to be considered.

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Utilizing diabetes to define the process, data points, and use and liquidity of information and technology could serve as an exemplar. In treating diabetes, the 1st step is to review the relevant evidence and guidelines as well as patient assessment and history. The evidence will help in the identification of management strategies including treatment, interventions, orders, monitoring, discharge/postvisit follow-up, instructions, patient education, and regulatory reporting for the condition. The literature may include care guidelines or pathways that can be used. For those utilizing electronic medical records (EMRs), the EMR may have built in the evidence. After identifying the relevant information for the condition, a process of attributing specific data elements to the condition should be done. For the example of diabetes:

1. What ICD-9/10 (International Statistical Classification of Diseases and Related Health Problems) codes define diabetes and should be included?

2. What SNOMED (Systematized Nomenclature of Medicine) codes are associated with diabetes?

3. What laboratory tests (LOINC [Logical Observation Identifiers Names and Codes]) and other tests are associated with diabetes, for example, HgA1c?

4. What physiologic data are associated with diabetes such as weight and vital signs?

5. What medications are related to diabetes?

6. What are the health expectations or prevention measures associated with diabetes: foot examinations, eye examinations, scheduled laboratory tests, and provider visits?

7. Ensure that your documentation is capturing the necessary data, the necessary fields exist, and the information can flow across the EMR as needed. The ability to push data to the point of care should be established.

8. Do the condition and interventions get linked or documented in the plan of care, patient goals?

9. How does the patient get included in the management of the condition, plan of care, access of the information?

The ability of the information system to analyze and push relevant knowledge and actions to the point of care is required in order to effectively and efficiently manage health.

Next, the development of a plan to incorporate the condition diabetes into a quality improvement/performance improvement program or existing program review will need to be determined, as well as the need to view aggregated data via dashboards and scorecards at the individual and population level including any required information supported by research evidence and regulatory guidelines. The plan should include what will be reviewed, schedule for review, process to analyze and recommend improvements, and how those improvements will be communicated. Policies and processes need to be written and disseminated about the requirements, rationale, and how they will be monitored over time with an educational plan for employees/clinicians and the population or community. During the review, thought should be given to the roles, responsibilities, and competencies needed within your population health model as identified earlier. What data can be entered at the point of care and by whom? What skills and abilities are needed to carry out the care management of a group of patients, interpret the information, and review and take action on aggregated data?

The design and elements of a dashboard should be defined. Specific attention to what kind of data and how they are presented is critical. Design decisions should include which performance metrics as well as specific programmatic measures are to be included in the dashboard views. The goal is be able to view and identify at a glance where interventions are needed and where outcomes are achieved.

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The enactment of the Affordable Care legislation1 and related programs has spurned an evolution of the current healthcare delivery model. The new models require a person-centric, longitudinal (across the lifespan) approach that includes smooth transitions, communication, multidisciplinary collaboration, quality, and cost (value) with a focus on managing populations. Within this evolution, information systems and technology play a significant role in supporting the models. A sample model depicting areas where information systems and technology should be included as well as an example defining process steps and associated technology components should be considered.

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1. The Patient Protection and Affordable Care Act, Public Law 11-148. US Government Printing Office, H.R. 3590. 2010.

2. CMS Innovations Center. Accessed February 2013.

3. Berwick D, Nolan T, Whittington J. The triple aim: health and cost. Health Aff. 2010; 27 (3): 759–769.

4. National Transitions of Care Organization. Accessed February 2013.

5. Patient Centered Outcomes Research Initiative. Accessed January 2013.

6. Guided Care. Baltimore, MD: Johns Hopkins School of Public Health; 2011.

7. Coleman E, Parry C, Chalmers S, Min S. The care transitions intervention. Arch Intern Med. 2006; 166 (17): 1822–1828.

8. Naylor M. Transitional Care Model. Philadelphia, PA: University of Pennsylvania; 2008: 13.

9. Stead WW, Searle JR, Fessler HE, Smith JW, Shortliffe EHStead Academy of Medicine. Biomedical informatics: changing what physicians need to know and how they learn. Acad Med. 2011; 86 (4): 429–434.

10. Accountable Care Council, eHealth Initiatives, Support for Accountable Care: Recommended Health IT Infrastructure. 2012. Accessed June 2012.

11. Aetna. Aetna’s Embedded Nurse Case Management Program. 2013. Accessed March 2014.

12. Blue Cross Blue Shield. Care first. 2013. Accessed January, 2013.

13. NCQA, HEDIS, and Performance Measurement. 2013. Accessed June 2012.

14. Courtney P. Data liquidity in health information systems. Cancer J. 2011; 17 (4): 219.

15. Penfield SL, Anderson KM, Edmunds M, Belanger M. Toward Health Information Liquidity: Realization of Better, More Efficient Care From the Free Flow of Health Information. Rockville, MD: Booz Allen Hamilton, Inc; 2009.

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