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Top 10 Health IT and Analytics Considerations for Effective and Efficient Care Management

The demand for meaningful and actionable data in healthcare has never been as prevalent as it is today. While most healthcare organizations are utilizing some form of health information technology (“health IT”) platforms to capture clinical documentation, system integration and broad spectrum analytic capabilities offered by these platforms are very underdeveloped. As pay-for-value becomes the standard for healthcare service reimbursement, organizations are beginning to realize the importance of establishing more efficient and effective care management processes that are supported by robust health IT systems and analytics frameworks. Below are 10 key health IT and analytic considerations to enable an effective care management program.

  1. Data Governance. Data is the crux of healthcare improvement. It is critical to establish a data governance council consisting of an interdisciplinary team that is responsible for developing a set of processes that serve as a quality control mechanism for handling information. These mechanisms define lines of responsibility and establish methods to foster the accessibility, completeness, and integrity of data. Strong data governance that can validate the accuracy of the data is critical to instill trust among clinicians. The absence of a data governance structure exposes the risk of clinicians contesting the accuracy and usefulness of the performance information produced by the data, thereby reducing its value to continuous improvement initiatives.
  2. Health IT Strategy. An organization must develop a health IT and analytics strategy to determine the types of health IT necessary to support the clinical and operational processes of the organization. The most essential component in the development of a successful health IT strategy is to gain consensus from all affected stakeholders. Attempting to implement a strategy without the buy-in from the affected stakeholders will be extremely difficult, if not impossible, because clinicians will not support the efforts and may challenge the types of health IT implemented.
  3. Systems and Technology Framework. The systems and technology framework of healthcare organizations is very complex. Organizations must inventory the various systems and develop a blueprint to guide system selection, technical and functional builds, consistent standards, and information output. All of these aspects affect clinical operations, and the implications must be considered and risks mitigated. Neglecting this activity will have serious implications on system integration and data aggregation efforts.
  4. System Integration and Solution Architecture. Healthcare organizations have various technology systems and platforms that were typically implemented at a point in time to serve a specific business need without extensive thought or planning given to system integration. The result of this approach is that these systems are not designed to “speak to each other”. Organizations must build an enterprise architectural approach that allows platform integration of multiple disparate systems, supports system interoperability, and establishes a centralized master data management structure.
  5. Solutions Search and Selection. Commonly, a systems and technology inventory reveals gaps in the framework, and the organization must embark on a search and selection process to identify a system to fulfill the need(s). It is important to establish a systematic approach to IT system selection so that the evaluation of the systems is comparative. More important, the organization must enlist the help of an interdisciplinary team to evaluate and score the systems based on a pre-defined list of functionality and standards. The process must be executed very methodically, starting with defining the system requirements, developing the evaluation criteria, training the evaluation team, conducting the system demonstrations, reviewing test cases, and selecting the system of choice. In addition, the organization must establish an enterprise approach to system search and selection, which can help minimize ad hoc purchases that can conflict with the overall integration strategy.
  6. Workflow Redesign. Clinical workflow redesign is integral with any health IT solution implementation. Before even embarking on system selection, an organization must first understand the clinical workflow. This will provide insight to the existing processes and protocols and enable the organization to select a technology solution that will best support clinical operations by incorporating solution sets that promote workflow redesign around functional roles, care model redesign, clinical decision support, and clinical protocols. Failure to consider the clinical implications when selecting a system will lead to an underutilized system and decrease efficiencies.
  7. Clinical Health IT Optimization. Clinical care optimization is critical to maximize the value of the health IT system. Health IT systems are rarely used to their fullest potential. This underutilization can have negative impacts on many facets of a healthcare organization, including patient safety, quality of care, clinical performance, staff satisfaction, and revenue capture. The organization can improve the functional use of systems and applications through utilization evaluation, reeducation of clinical documentation requirements, and system functionality retraining for clinical staff. Failure to optimize clinical health IT systems leaves the organization vulnerable and at risk for poor coordination of care, fragmented communication, performance penalties, staff turnover, and decreased revenue.
  8. Integrated Analytics and Reporting Strategy. An integrated analytics strategy is imperative to establish a streamlined approach to developing, managing, updating, and reporting performance measures. Many organizations are faced with various regulatory, accreditation, and quality program reporting requirements. Typically, report writers will build ad hoc reports according to the specifications requested, but report reviews reveal that many of these reports include the same metrics. Organizations must catalogue the performance measures contained in all reports. This accounting should include the details of the measure (e.g., numerator and denominator), discreet data needed to calculate the measure, the source system for the data, the purpose of the measure, the report measure owner (both requester and developer), the user(s) of the information, and the user  status (internal or external). This will help the organization to delineate the various reports, identify duplications, and establish consistency across the clinical analytics and reporting requirements.
  9. Clinical Informatics and Analytics. In line with the analytics and reporting strategy, an organization must define an approach and process to ensure that solution capabilities enable the collection of discreet clinical data that supports the development of reliable, action-oriented reports. An important component to developing a sound clinical informatics and analytics process is having an interdisciplinary team composed of IT, clinical, and report analyst representatives. This team composition will ensure that the information needed from the clinical team is addressed, the technical team can build it, and that it is structured so that analysts can easily generate the necessary reports.
  10. Actionable Data Analytics. Once the type of information that is needed for analytical reports is determined and the process by which this information will be captured is defined, an organization needs to determine the most effective way to present the information, to whom the information should be provided, and how frequently in a timely manner. Most important is designing reports that present data in a usable, action-oriented, and meaningful way. The way in which data is presented can make the difference between impactful care management and quality improvement results and futile care coordination efforts.

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