Essential IT Capabilities Of An Accountable Care Organization (ACO)

The Certification Commission for Health Information Technology (CCHIT) recently published a document entitled A Health IT Framework for Accountable Care. The document identifies the following key processes and functions necessary to meet the objectives of an ACO:

• Care Coordination
• Cohort Management
• Patient and Caregiver Relationship Management
• Clinician Engagement
• Financial Management
• Reporting
• Knowledge Management.

The key to success is a shift to a data-driven healthcare delivery. The following is my assessment of the most critical IT capabilities for ACO success:

• Comprehensive and standardized care documentation in the form of electronic health records including as a minimum: patients' signs and symptoms, diagnostic tests, diagnoses, allergies, social and familiy history, medications, lab results, care plans, interventions, and actual outcomes. Disease-specific Documentation Templates can support the effective use of automated Clinical Decision Support (CDS). Comprehensive electronic documentation is the foundation of accountability and quality improvement.


• Care coordination through the secure electronic exchange and the collaborative authoring of the patient's medical record and care plan (this is referred to as clinical information reconciliation in the CCHIT Framework). This also requires health IT interoperability standards that are easy to use and designed following rigorous and well-defined software engineering practices. Unfortunately, this has not always been the case, resulting in standards that are actually obstacles to interoperability as opposed to enablers of interoperability. Case Management tools used by Medical Homes (a concept popularized by the Patient-Centered Medical Home model) can greatly facilitate collaboration and Care Coordination.


• Patients' access to and ownership of their electronic health records including the ability to edit, correct, and update their records. Patient portals can be used to increase patients' health literacy with health education resources. Decision aids comparing the benefits and harms of various interventions (Comparative Effectiveness Research) should be available to patients. Patients' health behavior change remains one of the greatest challenges in Healthcare Transformation. mHealth tools have demonstrated their ability to support Patient Activation.


• Secure communication between patients and their providers. Patients should have the ability to specify with whom, for what purpose, and the kind of medical information they want to share. Patients should have access to an audit trail of all access events to their medical records just as consumers of financial services can obtain their credit record and determine who has inquired about their credit score.


• Clinical Decision Support (CDS) as well as other forms of cognitive aids such as Electronic Checklists, Data Visualization, Order Sets, Infobuttons, and more advanced Clinical Question Answering (CQA) capabilities (see my previous post entitled Automated Clinical Question Answering: The Next Frontier in Healthcare Informatics). The unaided mind (as Dr. Lawrence Weed, the father of the Problem-Oriented Medical Record calls it) is no longer able to cope with the large amounts of data and knowledge required in clinical decision making today. CDS should be used to implement clinical practice guidelines (CPGs) and other forms of Evidence-Based Medicine (EBM).
  
  However, the delivery of care should also take into account the unique clinical characteristics of individual patients (e.g., co-morbidities and social history) as well as their preferences, wishes, and values. Standardized Clinical Assessment And Management Plans (SCAMPs) promote care standardization while taking into account patient preferences and the professional judgment of the clinician. CDS should be well integrated with clinical workflows (see my my previous post entitled Addressing Challenges to the Adoption of Clinical Decision Support (CDS) Systems).


• Predictive risk modeling to identity at-risk populations and provide them with pro-active care including early screening and prevention. For example, predictive risk modeling can help identify patients at risk of hospital re-admission, an important ACO quality measure.


• Outcomes measurement with an emphasis on patient outcomes in addition to existing process measures. Examples of patient outcome measures include: mortality, functional status, and time to recovery.


• Clinical Knowledge Management (CKM) to disseminate knowledge throughout the system in order to support a learning health system. The Institute of Medicine (IOM) released a report titled  Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. The report describes the learning health system as:
  
  

  "delivery of best practice guidance at the point of choice, continuous learning and feedback in both health and health care, and seamless, ongoing communication among participants, all facilitated through the application of IT."



• Applications of Human Factors research to enable the effective use of technology in clinical settings. Examples include: implementation of usability guidelines to reduce Alert Fatigue in Clinical Decision Support (CDS), Checklists, and Visual Analytics. There are many lessons to be learned from other mission-critical industries that have adopted automation. Following several incidents and accidents related to the introduction of the Glass Cockpit about 25 years ago, Human Factors training known as Cockpit Resource Management (CRM) is now standard practice in the aviation industry.

Toward An Enterprise Reference Architecture for the Meaningful Use Era

Meaningful Use is not just about financial incentives. In fact, the financial incentives provided under the HITECH Act will only cover a portion of the total costs associated with migrating the healthcare industry to health information technologies. Consider the following Meaningful Use criteria:

• Submit electronic data to immunization registries
  
• Incorporate clinical lab-test results into certified EHR technology as structured data
  
• Submit electronic syndromic surveillance data to public health agencies
  
• Generate and transmit permissible prescriptions electronically (eRx)
  
• Provide patients with an electronic copy of their health information
  
• Exchange key clinical information among providers of care and patient authorized entities electronically.
  

These criteria are clearly designed to bridge information silos within and across organizational boundaries in the healthcare industry. This integration is necessary to improve the coordination and quality of care, eliminate the costs associated with redundancies and other inefficiencies within the healthcare system, empower patients with their electronic health records, and enable effective public health surveillance. This integration requires a new health information network such as those provided by state and regional Health Information Exchanges (HIEs) and the Nationwide Health Information Network (NHIN) initiative. HIEs and NHIN Exchange are based on a decentralized, service-oriented architecture (SOA) whereby authorized health enterprises securely exchange health information.

Health CIOs who see Meaningful Use as part of a larger transformational effort will drive their organizations toward success. Creating a coherent and consistent Enterprise Architecture for tackling these new challenges should be a top priority. Not having a coherent Enterprise Architecture will lead to a chaotic environment with increased costs and complexity. The following are some steps that can be taken to create an Enterprise Reference Architecture that is aligned with with the organization's business context, goals, and drivers such as Meaningful Use:

1. Adopt a proven architecture development methodology such as TOGAF.
   
   
2. Create an inventory of existing systems such as pharmacy, laboratory, radiology, patient administration, electronic medical records (EMRs), order entry, clinical decision support, etc. This exercise is necessary to gain an understanding of current functions, redundancies, and gaps.
   
   
3. Create a target enterprise service inventory to eliminate functional redundancies and maximize service reuse and composability. These services can be layered into utility, entity, and task service layers. For example, Meaningful Use criteria such as "Reconcile Medication Lists", "Exchange Patient Summary Record", or "Submit Electronic Data to Immunization Registries" can be decomposed into two or more fine-grained services.
   
   
4. Task services based on the composition of other reusable services can support the orchestration of clinical workflows. The latter previously required clinicians to log into multiple systems and re-enter data, or relied on point-to-point integration via the use of interface engines. These task services can also invoke services across organizational boundaries from other providers participating in a HIE in order to compose a longitudinal electronic health record (EHR) of the patient. Other services such as clinical decision support services or terminology services can be shared by multiple healthcare organizations to reduce costs.
   
   
5. Consider wrapper services to encapsulate existing legacy applications.
   
   
6. The Enterprise Reference Architecture should support standards that have been mandated in the Meaningful Use Final Rule, but also those required by NHIN Exchange, NHIN Direct, and the HIE in which the organization participates. These standards are related to data exchange (HITSP C32, CCR, HL7 2.x), messaging (SOAP, WSDL, REST, SMTP, WS-I Basic), security and privacy (WS-I Security, SAML, XACML), IHE Profiles (XDS, PIX, and PDQ), and various NHIN Exchange and NHIN Direct Specifications. While standards are not always perfect, healthcare interoperability is simply not possible without them.
   
   
7. Select a SOA infrastructure (such as an Enterprise Service Bus or ESB) that supports the standards listed above. Consider both open source and commercial offerings.
   
   
8. Consider non-functional requirements such as performance, scalability, and availability.
   
   
9. The Enterprise Reference Architecture should also incorporate industry best practices in the areas of SOA, Security, Privacy, Data Modeling, and Usability. These best practices are captured in various specifications such as the HL7 EHR System Functional Model, the HL7 Service Aware Interoperability Framework (SAIF), and the HL7 Decision Support Service (DSS) specification.
   
   
10. Finally, create a Governance Framework to establish and enforce enterprise-wide technology standards, design patterns, Naming and Design Rules (NDRs), policies, service metadata and their versioning, quality assurance, and Service Level Agreements (SLAs) such as the NHIN Data Use and Reciprocal Support Agreement (DURSA).