tracking with an embedded rules engine to support follow-up tasks and reminders; and creation and sharing of care plans that include longitudinal care views of goals and progress. Chronic illness registry tools typically have been devel- oped for single diseases and have produced lists of patients that need follow up or have “care gaps,” but do not include case management tools or health coaching functionality to manage and/or document the work in coordinating care and assisting patients with their illnesses. These tools help facilitate identifi cation and can report the results, but they do not manage the workfl ow across multiple diseases or support case management/health coaching. New popula- tion health and care management systems are now available that are multi-disease and can help care teams with role- based task management, care coordination, prescription drug adherence, patient letters and reminders, lifestyle tracking to goals and comprehensive clinical and fi nancial performance reporting. They are designed to be fl exible and accommodate different workfl ows across the care teams and also allow for ongoing changes in measures, defi nitions and guidelines as required.
Population surveillance rules engine
Staff should be able to monitor care processes and out- comes using evidence-based guidelines, with links to both a population and care management system and the EHR. Most EHRs will facilitate reminders that “pop up” dur- ing a patient encounter to fl ag the need for routine preven- tive screenings, immunizations, lab tests and care gaps, but they are not very fl exible and do not connect to a follow-up tracking system that facilitates role-based workfl ow for the care team. Since EHRs are visit based, they gener- ally don’t trigger actions between encounters, don’t allow fl exible workfl ows for follow up across the care teams and don’t document interventions or communication attempts. Evidence-based rules engines that exist outside of the EHR can support population management by the care teams for actions that are triggered, often avoiding the expense of a face-to-face visit with the practitioner. New population health and care management systems will incorporate evidence-based rules engines for population surveillance and support care teams in closing the care gaps.
Clinical integration of systems
Integrating population health IT with EHR functionality and workfl ow is a must.
The complete set of information about each patient must still be stored in the EHR to support optimal patient care. This requires that new information generated in a population and care management system be fed back to the EHR, so it is available at the point of care for decision making and follow up. The workfl ow between the EHR and the population and care management system must be optimally integrated to help assure effi ciency and access
to the data. Over time, some of the population health functionality that isn’t available now may be incorporated in the EHR itself. However, EHRs are usually structured around encounters rather than populations, care teams, or non-encounter-based workfl ows. This may ultimately limit the capacity of most current EHRs to incorporate popula- tion health IT functionality. Certifi cation Commission for Healthcare Information Technology (CCHIT) certifi cation ensures that the EHRs are positioned to exchange patient information bi-directionally. Little attention has been given to developing functional integration of workfl ows across systems, access to computer physician order entry (CPOE) for population management or making this integration commonplace..
The complete set of information about each patient must still be stored in the EHR to support optimal patient care. This requires that new information generated in a population and care management system be fed back to the EHR,
so it is available at the point of care for decision making and follow up.
Analytics tools should focus on predictive modeling, epi- sode grouping, severity and case mix adjustments. Predictive modeling tools support proactive identifi - cation and stratifi cation of the highest-risk patients for potential referral to complex case management. A parallel methodology is also needed to measure cost and utiliza- tion with case mix adjustment, typically through episode groupers.
Remote monitoring technologies
Home monitoring must interface with care management and EHRs.
High-risk patients with certain chronic illnesses such as congestive heart failure, diabetes, hypertension and chronic obstructive pulmonary disease may benefi t from utilizing home-monitoring devices that allow them to track their own illnesses and work interactively with a case manager and/or health coach who can also follow and track their outcomes in real time. This information can be sent back to both the population and care management systems and the EHRs.
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