1 Introduction

The World Health Organization principles for a core foundation to a healthy population focus on minimizing the inequalities of health care in communities. They focus on promoting healthy living, disease prevention, and access to health services. As access to health services increases an expected outcome is improved wellness, which improves the health and economic welfare of the community.

In order to work toward an increased “healthy” population in any urban community, public health professionals, health care providers, and policy makers must have access to sets of data that can be cognitively and analytically structured to create information. This information can then be used to provide a knowledge base to support community health decisions. Health data sets collected through expanding information technology initiatives such as the increased use of electronic medical records (EMR) and health information exchange (HIE) projects provide new resources for this urban health knowledge base.

Health data within EMRs is patient specific as are HIE projects that extend patient point of care service data to multiple sets of providers. The ability to capture these health service data sets within an urban area support the establishment of a population-based data system on which to build a knowledge management framework. Incorporation of analytical tools designed to support and monitor the impact of urban health care decisions becomes a key objective. These tools include pattern-identification, disease modeling, and statistical and spatial analysis. They can be used to provide information to detect health associations within urban demographic populations, assess risks, establish health care trends, and support epidemiologic disease modeling (Wickramasinghe et al. in press). This knowledge-based environment supports comparative effectiveness research (Federal Coordinating 2009) (CER) which allows the establishment of best practices within diverse urban communities and sub-populations. CER provides knowledge to support health policies and programs directed at increasing access to care, improving service delivery, and setting new targeted standards of practice.

This chapter presents a case study of an existing data management system designed to collect point of care services for children in the District of Columbia urban area. The information system, in operation since 2006, captures data from pediatric well-child visits and leverages the data in a knowledge framework to support urban health care objectives, policy, and health delivery. It demonstrates that when a knowledge-based framework is established to collect, analyze, and monitor health data, urban area health strategic objectives for multi-disciplined health professionals are supported.

2 Background

Prior to 2000 in the District of Columbia, there was little or no key health data to support the community coordination of care for children. There was little data to effectively evaluate the impact of health promotions, policies, or delivery of health care to this population. Health outcomes were assessed through anecdotal experience, surveys, or through analysis of billing claims data. Health care specialists relied on standard public health reporting workflows that resulted in under reporting and data that was not timely, accurate, or representative of the urban area. Information was not available to adequately support and monitor outcomes of urban area health care policy decisions.

The District of Columbia has over 75% of its population of 110,000 children under the age of 21 enrolled in the government Medicaid program. These children receive their health care from providers who are reimbursed for services. Without sufficient data to determine the effectiveness of the Medicaid program, the District was engaged in a legal action, Salazar vs. the District of Columbia (United States District 2001). This court case originated to challenge the District’s ability to ensure all Medicaid eligible children had access to care through the Early Periodic Screening, Diagnosis, and Treatment (EPSDT) program.

One of the principal court orders mandated the monitoring and reporting of patient access to providers for the eligible population to ensure they received the full benefit of services available. Two key outcomes resulted from this court order: (1) the creation of a Standard Medical Record Form (SMRF) which was used for collecting age appropriate point of care well-child visit data, and (2) the development of an information system that generates service coverage statistics and pay-for-performance provider data. The District established an initial target goal to ensure that 80% of the eligible children were age appropriately enrolled in the program and were receiving services with an ever increasing coverage rate over the course of a number of years.

A series of seven, age appropriate SMRFs (Beers et al. 2007) were established for documenting the well-child encounter visit, see Fig. 4.1. The data to be recorded complied with federal regulations 42 U.S.C 1396d(r), standards of care outlined in the Centers of Medicaid and Medicare Services (CMS) Medicaid Manual, and the DC Health Check program periodicity schedule. This schedule is based on the American Academy of Pediatrics recommendations for preventative pediatric health care.

Fig. 4.1
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Two of DC’s standardized medical record forms, (1) Children 0–1 months of age (left), and (2) Children 11–21 years of age (right)

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The SMRFs were used by physicians to build a paper medical record that followed each child through their age appropriate visits. An information system was developed to capture data electronically, with a patient centered system design. A point of care data system, in and of itself, does not ensure an effective information environment to support more informed health decisions. Therefore, a knowledge management approach was taken to create the framework to expand service delivery data to population data and a resulting urban area information environment.

3 The Child Health Information System Workflow

Figure 4.2 illustrates the work flow of the health information system that was created.

Fig. 4.2
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Work flow of age-appropriate well-child checks in DC

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The system recognizes electronically if a child and the child’s provider were Medicaid enrolled at the time of service and thus, the provider was eligible for reimbursement. Currently, the application relies on collecting copies of the forms, accomplishing data entry, and scanning the forms as an attached electronic record to the patient record. In the near future as providers move toward EMRs the data entry component of the work flow will gradually be enhanced with electronic capture of records.

Individual patient data collected through this system is illustrated in Table 4.1.

Table 4.1 Data collected during the well-child visits
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4 The DC EPSDT Knowledge Management System

To cast this system in a knowledge management framework (Liebowitz 2001), Fig. 4.3 is presented which consists of the five levels:

Fig. 4.3
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The framework of EPSDT knowledge management system

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The five levels are standard to the knowledge management approach, with layers 1, 2 and 5 specifically tailored to the District pediatric system.

  1. 1.

    Data Sources – Currently this pediatric data system relies on data collected through standard medical record forms for EPSDT visits as illustrated in Table 4.1. Eligibility data is electronically exported from the District’s Medicaid claims system for both providers and patients. Immunization records are available in a District registry, and the design supports electronic capture from EMRs.

  2. 2.

    Knowledge Acquisition – Patient information is generated from the visit data as a child ages to 21 and receives services. Physical exams, risk screening, family histories, development assessment, and preventive care measures such as immunizations are established and tracked for each individual patient.

  3. 3.

    Knowledge Construction – As data is gathered and patient information retained, the third level of the knowledge management framework applies rules and processes to the data allowing knowledge to be constructed from the results.

    For example, District regulations for lead toxicity establish that screening through blood lead tests should occur for children age 12 months and 24 months. The pediatric care protocol requires providers to request a blood test for lead at the age appropriate patient visits.

    When the result of the blood test is reported, the knowledge management system applies rules to assess the patient risk to lead poisoning. Specifically, if the test result is equal to or greater than 10 μg/dl, a patient is flagged as a “high lead risk,” thus knowledge is gained as a result.

  4. 4.

    Knowledge Organization – As the data application is web-based, it provides an effective method for knowledge transfer, creation, and distribution to support the needs of management, health care, and public health stakeholders. Knowledge organization includes the tools for information retention and retrieval from ­simple data queries to complex searches across various patient demographics. In addition, this organizational component includes the ability to link data, such as spatial links. This allows population health knowledge to be created by linking individual patient care to common sub-populations.

    For example, take the geographical representation of children lead tests from one District sub-population as illustrated in Fig. 4.4. By spatially representing this data, knowledge of potential clusters of high lead risk children are established through a geographical “heat map.”Footnote 1 By linking additional data, such as the age of housing in the District, it is possible to identify high risk areas to target mitigation efforts. A high risk knowledge resource is created and can be replicated to support health policy, implementation, and assessment of the outcome.

    Fig. 4.4
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    Example of spatial cluster analysis of linked data

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    In this example, as the mitigation programs are implemented over time, the knowledge gained by continually monitoring and displaying graphically the population risks to lead supports the effectiveness of the health decisions being made today.

  5. 5.

    Knowledge Business Process – The end users of the pediatric knowledge management system are represented in this layer. Public health practice, service delivery, policy, and management support benefit from the data collected as it rolls up through the system. Figure 4.5 illustrates how the knowledge management system supports the end user community, specifically for the lead example. As patients are appropriately screened during their well-child visits, data captured, and rules applied, information is gathered based on patient centric and case-driven processes. This information provides the knowledge to the user community to support their specific needs.

    Fig. 4.5
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    Illustration of lead screening process and case knowledge-based approach

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5 Summary

The principles of knowledge management drive decisions through improved use of information derived from increasing data captured by disparate systems. Health information technology initiatives continue to create opportunities to support expanded stakeholders through the use of this information.

The District of Columbia EPSDT program, as it evolves from paper forms and data entry to a solution with electronic data capture and integrated tools for mining of data, will increase the decision support and evaluation capabilities for users. Future extensions of the system that integrate visualization tools and orchestrated displays to allow the sharing of data through an urban area health dashboard (Scientific Technologies 2009) will take advantage of this knowledge framework, as illustrated in Fig. 4.6.

Fig. 4.6
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Facilitation of knowledge transfer through EPSDT specific information displays

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Although it was the U.S. court system that became the driver initiating the creation of a standardized reporting and data collection system for a specific sub-population of the urban area, it is the architecture and design of the system that allows for true population health knowledge to be created from point of care services. The vision of the District to not only address the immediate needs but create a system to support evolving health reform through a knowledge-based framework will serve the urban community for years to come. Its value will be further demonstrated as utilization supports the World Health Organization principles for a healthy population day-in and day-out provision of data, information, and knowledge to measure and monitor:

  1. (a)

    Access to care.

  2. (b)

    Ensuring age appropriate risk screening.

  3. (c)

    Monitoring disease prevention care, i.e., immunizations.

  4. (d)

    Ensuring care and appropriate referrals for high risk children.

  5. (e)

    Assessing “wellness” of urban area population.

  6. (f)

    Monitoring “outcomes” to ensure best practices and management of resources.

Knowledge management is a science. It is the science of organizing the knowledge that exists when people, processes, and technology come together. Applying knowledge management to improve health outcomes in a population is an art. It is the art of orchestrating the data and information from these same people, processes, and technology.