Chronic Conditions - Appendix E

APPENDIX E:

Guidance on Technological Interventions


 

EHRs were primarily designed to manage individual patients rather than groups of patients. However, over time, EHRs have increasingly added functionality for population-level quality reporting and management, and for some degree of care planning and care coordination, especially to support value-based care tracking and reporting. Practices should evaluate your EHR capabilities against specifically designed population management applications. While these applications require interface with the EHR, they generally offer additional functionality. While EHR solutions are integrated with EHR data, they still usually require import of data from outside sources to be optimally useful. Managed care organizations may provide care coordination and population management applications, usually only for their own enrolled patients. EHR-based solutions may also pose challenges where groups of practices using different EHR solutions are collaborating in value-based care contracts.

In value-based care arrangements, practices are responsible for attributed patients who may have never been seen. Since these patients do not have records in the EHR, practices need to consider how they can manage these patients to engage them into care at the practice in the absence, at least initially, of the patients having records within the EHR. If your practice is using freestanding applications for this, it needs the capacity to handle these attributed patients who have not been registered as patients.

Figure 25 below includes the technical functionalities required to support population management for adults with chronic conditions. These requirements can guide the evaluation of existing solutions or guide the development of requirements in evaluating potential new applications. The figure also indicates the data sources required to enable the functionality.

 

FIGURE 25: CORE POPULATION HEALTH MANAGEMENT FUNCTIONALITY REQUIREMENT


Functionality

Population Health Management Requirement Description

Data Acquisition Dependency

Care guidelines

Identify care gaps for all adults with chronic conditions against care protocol.

Care guidelines may be presentable to the clinical provider/support team at the point of care through the EHR, in the visit workflow as pre-visit prep/team huddle, through registries as above, and aspirationally as prompts to patients/caregivers.

Commercial EHR-embedded guidelines provided by vendor or customized by practice.

External source guidelines (clinical guidelines). 

Reference sites made available electronically. 


Registries
   

Identify care gaps for all adults with chronic conditions against care protocol.

Care guidelines may be presentable to the clinical provider/support team at the point of care through the EHR, in the visit workflow as pre-visit prep/team huddle, through registries as above, and aspirationally as prompts to patients/caregivers.

Ability to produce registries (list/cohort of patients) organized to facilitate population management:

Adults in age ranges relevant to measures and/or clinical standards.

Adults sharing designated high-risk criteria (medical, behavioral, social needs) impacting their ability to achieve guidelines.

These registries should consider the inclusion of functionality to trigger automated, predefined action(s) and/or human-initiated action(s) for all or a defined subset of patients comprising the registry.

Suggested HIT assets that can be leveraged to achieve this function include: 

EHR – generates a list of patients who meet the criteria for inclusion in the population of focus. 

Track using an external database. Consider merging patients from an external data source, such as a payor, to have a complete roster. 

Population health management tool – specialized chronic disease management applications (some of which include patient-facing components).

EHR:

  • Clinical data.
  • Scheduling data: appointments (e.g., adult wellness and sick visits).
  • Preventive screening history.
  • Screening data (see below).

 

External data sources, such as: reference labs, specialty care, immunization registries, social service clinicians’ data. 

Data from home devices such a glucometers and home blood pressure monitoring devices.

 


Clinical
decision support (CDS)

Care gaps should be displayed based on what is due, with insight into previous results, to support clinicians’ ability to make decisions at the point of care (POC) for the provider and care team members supporting non-POC management.

Care guidelines may be presentable as clinical decision support to the clinical provider/support team at the point of care, in the visit workflow as pre-visit prep/team huddle, through registries as above, and aspirationally as prompts to patients/caregivers. While EHR-based prompts are usually thought of as ideal, team-based care presents an opportunity for clinical decision support to be presented to other members of the care team through other channels. 

The Five Rights Framework Clinical Decision Support: More Than Just ‘Alerts’ Tipsheet is a useful guidance to help health centers to support decision-making across a wider range of the care delivery life cycle, broader teams and technology other than the EHR to look beyond office visits and providers. This is especially important to avoid “alert fatigue” and burnout.


Internal EHR data.
External source clinical data.
Claims data (clinical lag should be noted).
Electronic guideline specifications.
Patient-contributed data.

Care dashboards and reports

Adults with chronic conditions dashboard: population view by eligible study with sorting/filtering capability based on characteristics to be defined by the practice, with ability for care team/case managers to document the actions completed; ability to see care gaps at a patient level and population level according to health center-prioritized care guidelines. Note that to automate these reports, it is necessary to apply standardized data collection strategies against electronically specified protocols. 

Same as above (EHR data and external data sources. Data from other sources of care).

Claims data.


Quality
reports

Same as above by quality measures, as opposed to care guidelines; ability to track HEDIS as well as customized measures and UDS.

Quality measure specifications.

Same as above (EHR data and external data sources).

Data from other sources of care.

Claims data.


Risk stratification

Ability to categorize risk for patients and develop lists according to risk classification (tie to registry).

Can be imported as externally generated risk score or calculated internally according to proprietary or customized risk algorithm.

Data acquisition platform ingestion: already curated high-risk list ingested and utilized downstream in the journey and/or additional internal and external data sources to populate defined risk model.


Outreach
and engagement

Allow for outreach to support previsit planning or post-visit care needs, such as assessments.

Technology channels include population registry outputs; patient-facing applications, such as patient portals; freestanding text messaging; and self-assessment/self-management applications.

 

Same as above (clinical/EHR/etc.)

Claims.

 


Care management

Allow for management of specific and unique care needs for high-risk patients. Care management requires the ability for multiple members of the care team to contribute to and rack elements of the plan. Challenges with freestanding care management applications include access to data from other sources of care, including the ability to track referrals, and workflow burden of staff utilizing multiple applications. 

Ability of the care management application to draw from and “write back” to the EHR is desirable but difficult to achieve.

 

Care management protocols. 

Appointment data: internal/external.

Clinical data from external service providers.

 

 

FIGURE 26: USE OF TECHNOLOGY FOR RECOMMENDED SCREENING FOR FOUNDATIONAL KEY ACTIVITIES

This figure identifies strategies for using digital tools to complete appropriate screeners as recommended by clinical guidelines. Using technology to facilitate screening may streamline the workflow and preserve patient confidentiality where necessary.


ID

Focus Area

Completion of Digital Screeners

Data Acquisition Dependency

1

Depression screening

In-office tablet-based screening and/ or remote patient-facing application-based self-completed screening.

  • A workflow for identifying emergent behavioral health risk should be codified.
  • A workflow for preserving patient confidentiality should be codified.

Population health and EHR integration of screener responses or, at minimum, scores.


2

Anxiety screening

In-office tablet-based screening and/ or remote patient-facing application-based self-completed screening.

  • A workflow for identifying emergent behavioral health risk should be codified.
  • A workflow for preserving patient confidentiality should be codified.

Population health and EHR integration of screener responses or, at minimum, scores.


3

Unhealthy substance use screening

In-office tablet-based screening and/ or remote patient-facing application-based self-completed screening.

  • A workflow for identifying emergent behavioral health risk should be codified.
  • A workflow for preserving patient confidentiality should be codified.

Population health and EHR integration of screener responses or, at minimum, scores.


4

Social needs screening

In-office tablet-based screening and/ or remote patient-facing application-based self-completed screening.

  • A workflow for identifying emergent behavioral health risk should be codified.
  • A workflow for preserving patient confidentiality should be codified.

Population health and EHR integration of screener responses or, at minimum, scores.

 

FIGURE 27: USE OF TECHNOLOGY FOR PATIENT OUTREACH AND PVP FOR FOUNDATIONAL KEY ACTIVITIES

This figure outlines the use of technology to facilitate specific activities and potential technology solutions that can optimize the uptake and efficiency of in-office visits.


ID

Technology Focus

Patient Outreach and Pre-Visit Planning

Data Acquisition Dependency

1

Portal-based communication

  • Appointment reminders.
  • Medication reconciliation.
  • Consents.

EHR interface and integration.


2

AI-enabled chatbots

  • Appointment reminders.
  • Pre-visit education regarding routine screening, health maintenance and anticipatory guidance.
  • Pre-visit planning and screening (e.g., social needs, development).

Identifying issues that need to be addressed before an office visit can be converted to telehealth visits.

Population health and EHR incorporation of screening scores and responses.


3

Text messaging

Appointment reminders.

EHR interface and integration.

 

FIGURE 28: USE OF TECHNOLOGY FOR ENHANCED PATIENT ENGAGEMENT AND VIRTUAL CARE FOR GOING DEEPER ACTIVITIES

The figure identifies technology solutions to engage patients asynchronously from office visits for a variety of use cases to enhance care and patient experience.


ID

Focus Area

Patient Engagement and Mobile Technology

Data Acquisition Dependency

1

AI-enabled chatbots

  • Triage protocols for acute care needs.
  • Conversion from triage dialogues to telehealth visit for use cases that require synchronous communication with a member of a care team.
  • Multimedia content sharing for patient education or diagnostic purposes (e.g., images of rashes, audio files, video files).
  • Sharing care plans based on patient-generated inquiries regarding health questions and conditions.

EHR integration.

2

  • Remote diagnostic technology for otoscopic, oropharyngeal, and cardiopulmonary examination.
  • Remote blood pressure monitoring.
  • Remote blood glucose monitoring.
  • Continuous glucose monitoring.
  • Remote spirometry.

EHR and population health integration.

 

FIGURE 29: USE OF TECHNOLOGY FOR INNOVATIONS IN CARE DELIVERY FOR ON THE HORIZON ACTIVITIES

The figure describes technology strategies that can enhance care delivery by using artificial intelligence and advanced technology tools.


ID

Focus Area

Artificial Intelligence and Innovation

Data Acquisition Dependency

1

Predictive analytics

  • Risk prediction (e.g., behavioral health risk, chronic disease risk, ED utilization risk).
  • AI-enabled care plans with patient-specific instructions.

EHR integration, population health, and patient engagement application integration.

2

Artificial intelligence (AI)- enabled diagnostics

  • Advanced diagnostic tools that can use imaging, audio files, and EHR data to suggest diagnoses and care management plans.

EHR integration and population health integration.