Archive for the ‘healthcare data aggregation’ Category

Technical Foundations for Accountable Care – Part 1

This posting is part one of an article about the IT infrastructure requirements for ACOs; I explain how data collection works today in traditional HIEs using XML CCD documents (i.e. HITSP C32), and why this is insufficient for care coordination purposes required for accountable care. The full Whitepaper is published here.

In the last few years, Healthcare information technology has evolved quite dramatically.  In 2000, merely 12 years ago, Electronic Medical Record (EMR) adoption across the U.S. physician population was reported to be around 18%, while numbers from 2011 suggest more than 55% adoption (Jamoom et al., 2012).  Now that more data are collected electronically, they can be analyzed, aggregated, and shared, giving rise to many new clinical and financial opportunities, especially in the support of coordinated care.  However, current health information exchange infrastructure using Continuity of Care Documents (CCD) are insufficient for event based, episodic healthcare to evolve towards a more holistic and accountable system that emphasizes prevention and quality improvement.  This white paper explains the limitations of current infrastructure, and proposes a registry platform approach to support next generation of Healthcare.

A performance based Accountable Care Organization (ACO) model requires an extensible, interoperable, and sophisticated IT infrastructure to track patients, providers, and their relationships to promote coordinated care and measure quality improvements.  The proliferation of EMR systems and the widespread adoption of integration technology and standards is helpful, but they alone are not sufficient to aggregate, coordinate, and contextualize the abundant, disparate data exchanged across the ACO community.

The ACO environment is comprised of multiple constituents, some of which are new to the real-time exchange of data.  The landscape represents a range of technical capabilities, distinct data sets, and varying levels of data quality.  Connecting the information systems among such varied participants can be a tedious undertaking, but the task is certainly achievable, especially if common messaging formats and interoperability profiles are utilized.

However, transferring data from one application to another is now merely a first step in the transformation towards accountable care.  The next step is to achieve semantic interoperability to ensure data are exchanged in regards to the same “entity”, such as a patient, provider, or code set.  And to realize full ACO benefits, the technology infrastructure must be able to identify, organize, and correlate data into clinically useful analytics for decision support, care coordination activities, and intelligent referral management.  In the absence of a single system to orchestrate this data aggregation, a platform of healthcare registries can create the framework to deliver a comprehensive and coherent view of information and enable the data management goals of accountable care.

Quality of Care in the United States

The Institute of Medicine (IOM) indicates that quality of care issues in the United States healthcare system cause a high number of avoidable deaths, complications, and associated direct- and indirect costs (Kohn, Corrigan, & Donaldson, 2000).  As a result, the United States performs poorly among a panel of developed nations with regard to clinical outcomes and mortality rates, while at the same time producing the largest increases in cost (Peterson, 2007).

In the same report, the IOM (Institute of Medicine, 2001) recommends “coordination of care across patient conditions, services, and settings over time” which includes:

  • Continuous healing relationships
  • Customization [of care plans] based on patient’s needs
  • Patient as source of control
  • Shared knowledge and free flow of information
  • Evidence based decision making
  • Anticipation of needs
  • Continuous decrease of waste
  • Cooperation among clinicians

These findings about cost and quality motivated unprecedented government programs to invest in the modernization of healthcare IT.  For example, the ARRA/HITECH act offers financial incentives for providers who invest in EMR/EHR infrastructure that complies with certain requirements for interoperability, privacy, and security (Stark, 2010).

Traditional EMR systems, however, are considered “information silos”, that is, the data they maintain are coded in a proprietary format understood only by that particular EMR vendor’s systems.  In an effort to expand the enterprise value from these EMRs, policies like Meaningful Use (MU) were enacted, but ultimately, the policies only require that information is made available, not meaningfully organized.  There is no inherent need or incentive (Porter and Teisberg, (2004) for providers in a Fee-For-Service world to spend time sifting through vast amounts of largely redundant data to discover nuggets of useful information.

The Accountable Care Organization


The concept of an Accountable Care Organization (ACO) is to improve quality through coordination of care across patient conditions, services, settings, and time, and to align financial incentives for participating providers and payers.  Organizations agree that spending more energy on disease prevention and the prudent management of continuous diseases will eventually reduce the need for costly emergency care and hospitalization.  And they also accept that coordinating the various components of care can eliminate waste and redundancy.

Early results from various ACOs formed between private payers and regional ambulatory and acute care providers reported significant cost savings and quality improvements.  But the results also showed that many organizations are not ready to participate in ACOs (Higgins, 2011) because they lack the infrastructure to support the information requirements of risk assessment, care transition management, and care plan development for prevention and early detection.

ACOs need IT infrastructure to do more than just share vast amounts of redundant information. They need IT to uniquely identify content, ensure accurate patient matching, and aggregate data so that participating providers can actually use the information to make evidence based clinical decisions.

Fee-For-Service Information Processing


In a Fee-For-Service (FFS) model, information systems are used to document and reference episodes of care.  The primary information system used in the FFS model is the EMR, and there are hundreds of EMR vendors offering their unique application.   Patients who visit a primary care physician can have their data recorded in a local EMR system, as long as they consent to the electronic storage and sharing of protected health information (PHI).  Typically, that EMR system uses an internal code set to record demographics (D), known problems (P), medications (M), allergies (A), and diagnoses (P). If and when the same patient returns to the same Physician, the EMR is used to access the patient’s historic data as well as any data added in the interval.

However, if the patient visits a different Physician at a different facility, the EMR system with the patient’s medical data may not be available.  But if the Providers participate in a Health Information Exchange (HIE), it may be possible to access the patient’s information in the local EMR.  In an HIE environment, applications and systems and are connected through an information exchange that most likely uses Integrating the Healthcare Enterprise (IHE) standards for cross community document sharing, abbreviated as XDS (Integrating-the-Health-Enterprise, 2007).

To maintain consistency across systems, the well-defined Continuity of Care Document (CCD) [1] has been chosen as the mechanism to share patient data.  For each episode of care, whether it is an ambulatory consultation or an acute care hospitalization, a CCD is created that contains a summary of the event, such as the patient’s current demographics, blood pressure, lipid panels, prescribed medication, etc.  Every CCD is registered in the HIE XDS Registry, an XML repository with web-service access in a so-called ‘affinity domain’ (IHE, 2007).  Reviewed together, multiple CCDs can recount a “history” and illustrate the progression of an illness and its treatment.

Continuity of Care Document Contents


Given an episode of care for a new patient, the published CCD summary record could be described as   C1 = [D1, P1, A1, M1], where:

  • D1 – patient demographics like name, address, local MRN and other identifiers
  • P1 – a description of a diagnosis coded in a clinical Terminology such as ICD10 or SNOMED
  • A1 – a reported allergy in free text
  • M1 – a prescription coded in RxNorm

If the patient visits the same provider and another diagnosis P2 and prescription M2 are added, the CCD for that visit would contain:

C2 = [D2, P1, P2, A1, M1, M2]

If these CCDs are published to an IHE XDS.b compliant registry in the HIE, they are registered under a unique patient identifier, commonly referred to as an EUID.  The EUID is assigned by an Enterprise Master Patient Index (EMPI) service when patient demographics are registered for the first time.  To assign the EUID, the EMPI evaluates the demographic components of the CCD against known patient identities within the registry.  A sophisticated EMPI can incorporate many different demographics data fields into the evaluation.  So when C1 is registered, D1 is evaluated, identified as a new record, and a new EUID is issued and cross-referenced with the local MRN contained in D1.  When C2 is registered, the EMPI can use the cross-referenced MRN contained in D2 to identify the patient’s already existing EUID.

Other systems connected to the HIE, such as EMR, Radiology, and other specialty systems, can also use the information contained in the EMPI.  For example, during a typical patient registration, the registration system could search the EMPI first, instead of automatically creating another local MRN which increases the probability of fragmented patient information.  The EMPI evaluates the search criteria against known patients, and responds to the registrar with a list of likely matches with their associated EUIDs.  This query / response web services transaction, known as “Active Integration”, is described in the IHE XDS.b framework as Patient Demographics Query (PDQ) (Integrating-the-Health-Enterprise, 2007).  An external system could also send the local MRN only to the EMPI, and if the MRN already exists, the EMPI would respond with the cross-referenced EUID.  This query / response web services transaction is known in IHE XDS.b as Patient Identifier Cross Referencing (PIX) service.

CCD Workflow Issues

Let’s assume that when C1 was registered, a new EUID 47111996 was issued by the EMPI.  When C2 is published, it is registered under the same, now existing, EUID.  If D2 of C2 contains additional or different information compared to D1 of C1, such as a new address or additional phone number, the EMPI will update the demographics information for the patient.  A sophisticated EMPI keeps a record of all changes to increase the likelihood of successful PDQ queries, even when they are based on outdated data.

If the physician ordered a lab test during the second consultation, the lab could publish results directly to the HIE, noted here as L1, resulting in:

C3 = [D3, L1]

When the physician imports these lab results into a local EMR, using them to identify a new problem, the EMR would publish:

C4 = [D4, P1, P2, P3, A1, M1, M2, L1]

If the lab results suggest that the initial diagnosis expressed in P2 is wrong, the EMR would publish:

C4’ = [D4, P1, P3, A1, M1, M2, L1]

When the patient goes to see a new provider, a specialist for example, that provider could query the HIE and would see the following content:

C1 = [D1, P1, A1, M1]

C2 = [D2, P1, P2, A1, M1, M2]

C3 = [D3, L1]

C4 = [D4, P1, P2, P3, A1, M1, M2, L1] or C4’ = [D4, P1, P3, A1, M1, M2, L1].


The provider has a few options to review clinical data.  Content can be retrieved and viewed in a portal solution, or data can be imported into a local EMR if the provider is interested in doing so and the HIE offers out-bound data integration.  In the case where the Provider imports C4’ into a local EMR, but chooses not to import the lab result, and then identifies a new problem for the patient and prescribes new medication, then C5 = [D5, P1, P3, P4, A1, M1, M2, M3,] will be published.

The content of the HIE for EUID = 47111996 will now reflect:

C1 = [D1, P1, A1, M1]

C2 = [D2, P1, P2, A1, M1, M2]

C3 = [D3, L1]

C4 = [D4, P1, P2, P3, A1, M1, M2, L1] or C4’ = [D4, P1, P3, A1, M1, M2, L1]

C5 = [D5, P1, P3, P4, A1, M1, M2, M3,]

In the context of Accountable Care, this information model has several problems.  As one can see, CCDs contain a lot of redundant information.  The difference between C1 and C2 is only P2 and M2, but in the lengthy document, it might take a while to identify those differences. By the time C4 is published, M1 might not be current anymore.  If a Physician sees both C2, C4’ and C5, can they safely assume that P2 is obsolete? Should they only import the most recent CCD, in this case C5?  But if they do so, they would lose the L1 information contained in C3, and P2 information contained inC2.

When the goal is to analyze patient information for gaps in care so that preventive care actions can be developed, the redundant, incomplete, and unclear information contained in CCDs presents a significant challenge.

Laboratory Results and Medication Mapping

Some HIEs will store laboratory results as discrete data, preserving the original terminology.  Some EMRs can import such original data to be utilized by built-in decision support tools.  But not all HIEs are capable of discrete data export.  Even if data are stored discreetly, and could be imported into a local EMR, it is still necessary for the local EMR to parse the data.  If L1 is coded in a proprietary format not known to the local EMR, the data are rendered useless for the EMR.  It would be more useful to map proprietary data to LOINC®[2], and store the equivalent LOINC terms, so that all systems retrieving data could utilize the content.

The same problem applies to medication data.  Since different EMRs use different terminologies to describe drugs, CCD entries need to be mapped to RxNorm[3].  In this way, local EMRs importing data from the HIE could identify potential drug-drug effects and issue alerts, as would be the case if prescription M3 conflicts with M1 or M2.

[1] CCD, Continuity of Care Document is a HL7 CDA document format; In ARRA/HITECH legislation it is referred to as HITSP/C32 and HITSP/C83

[2] LOINC: Logical Observation Identifiers Names and Codes,