By Kapila Monga, MBA, and Murali Gandhirajan, BE, PMP

A previous Journal of AHIMA article, Care for Caregivers: Assimilating the Caregiver Needs in the Health Data Ecosystem, explained that for the healthcare system to view caregivers as an integral part of the ecosystem, caregiver information needs to be systematically captured, stored, and managed.

As detailed in the previous article, caregiver information typically falls under six data domains: 1) Demographics; 2) Values and Preferences; 3) Caregiver Health; 4) Caregiver’s Perception of Care Recipient’s Health and Well-Being; 5) Goals, Activities, and Aspirations; and 6) Caregiving Support Resources.

There are two entities in the healthcare ecosystem that are best positioned to store, manage, and subsequently utilize the caregiver data for caring for caregivers: electronic health record (EHR) vendors (as part of the EHR data model) and long-term care organizations (as part of their IT system’s back-end model). Both of these entities can enable managing caregiver health and life better.

The caregiver data management process can be viewed as a three-step process:

  1. Caregiver Data Collection – This involves identifying the right points in the patient journey to gather caregiver information and having mechanisms and systems in place to capture that information.
  2. Caregiver Data Storage – This involves the proper way to organize and store caregiver information in an easy-to-use to data model.
  3. Caregiver Data Publication – This involves making the stored data available for integration with other patient data in the health data ecosystem.

In this article, we’ll delve deeper into how the six data domains can be knitted together in a data model that can enable effective data storage and subsequently effective data management for assessing caregiver needs.

Caregiver Data Storage: The Data Model

The data model defines how the collected caregiver data is organized and stored within information technology systems. It is structured using entities, attributes, and defined relationships between those entities.

Entities and Attributes: Entities refer to the concepts/areas to which the data model is supposed to cater. In this case, this could mean caregiver demographics, caregiver values, preferences, and caregiver health. The six data domains provide a directional indication on what those entities should be. Attributes refer to information pieces within those entities that are of value. For example, within the demographics entity, name, age, gender, etc., are attributes. The table below outlines the different entities and attributes for capturing caregivers’ needs.

In order to keep the entities and attributes at a level easily understood by nontechnical audiences, some liberties have been taken to omit specificities with regards to implementing data models (e.g., First Name, Middle Name, and Last Name have been combined and represented as Name). Mentioning data types has been skipped; instead, a description has been provided to make the model easily comprehensible.

# Name Name Description
1 Caregiver Demographics Name Caregiver name
Phone Number Caregiver phone number
Date of Birth Caregiver date of birth
Address Caregiver address
Email Caregiver email
Income Level Family income level range: $0 to $50,000; $50,001 to $100,000; $100,000 to $200,000; $200,000+
Caregiver ID System-assigned unique identifier
2 Caregiver- Care Recipient Crosswalk Caregiver ID Caregiver ID
Care Recipient ID Care recipient ID for which caregiver is providing care
Caregiver – Care Recipient Relation Relationship between caregiver and care recipient:

spouse, parent, friend, family, other

3 Typical Caregiving Journey Phase ID Identifier of a phase
Step ID Identifier of a step in the caregiving journey phase
Phase Name Name of phase
Step Name Name of step
Resource ID List of support resources available for the phase
4 Support Resource Resource ID ID field
Resource Name Name of resource
Resource Weblink URL associated with resource
Resource Type Type of resource: caregiver needs, care recipient needs, financial, medical, emotional support
Contact Person Primary contact person for the resource
Contact Person Phone Phone number of primary contact person for the resource
Contact Person Email Email of primary contact person for the resource
Active If resource is still active
5 Caregiver Goals Goal ID Auto-generated goal ID
Caregiver ID Caregiver ID
Active If the goal is still valid from the caregiver’s perspective
Set Date Date goal was set
Perceived Achievable If goal is perceived achievable by the caregiver considering their caregiving responsibility
Recalibration Support Available Support resources available to help recalibrate the goal in light of current caregiving responsibility
6 Caregiver Daily Routine Caregiver ID System-generated unique identifier
Work Status Yes or no
Work Type Full time, part time
Work Days Days of week
Work Timing Work timing
Other Activities Other personal/social activities
Kids Special Needs Yes or no
Kids Care Detail Text to capture special needs of kids
Kids School Duty Timing for school pickup/drop-off
7 Caregiver Health Caregiver ID System-assigned unique identifier
Existing Conditions Text to capture details; values available in standard health form
Average Exercise Duration Exercise time in a week
Current Medication Text to capture details
Mental Health Treatment Indicator Yes or no
Mental Health Treatment Description Details to capture any mental health treatment in the past or current
Medicare Medicaid Indicator Indicator to capture Medicare or Medicaid benefits
8 Caregiver Preferences Caregiver ID System-assigned unique identifier
Contact Preference Email, phone, text, other
Place of Support Location preference for caregiving activities
Type of Support Text to capture type of support caregiver willing to provide
Need for additional Support Yes or no
Request for Additional Support Details Describes if caregiver needs additional support to provide complete care support for care recipient
9 Caregiver Values Caregiver ID System-assigned unique identifier
Caregiving Motivation Describes what motivates caregiver for providing care
Cultural Belief Toward Caregiving Describes cultural beliefs toward caregiving
Perceived Challenges Describes challenges that the caregiver faces
Perceived Priority for Caregiving Caregiver’s priorities for caregiving in 1, 2 or 3 categories
10 Care Recipient Needs – Caregiver Perception Care Recipient ID Care Recipient ID
Caregiver ID Caregiver ID
Type of Need Medical, social, behavioral, emotional, physiological
Frequency Ongoing, hourly, daily, monthly
Time Required Time required to address the need
Need addressed by Need addressed by
Need fulfillment Satisfaction Level Satisfaction level with how need is being addressed
Additional Support Needed Additional support needed
11 Caregiver Caregiving Journey Phase ID Identifier of a phase
Step ID Identifier of a step in the caregiving journey phase
Caregiver ID Caregiver ID
Care Recipient ID Care recipient ID
Date Entered Date entered
Date Completed Date completed
Transition Comments – Caregiver Transition experience of the caregiver
Transition Comments – Other Entities Transition experience of the care recipient and other supporting entities
12 Caregiver-Support Resource crosswalk Caregiver ID Caregiver ID
Care Recipient ID Care recipient ID
Resource ID ID of support resource being used
Used On Last used date
Frequency of Usage Daily, weekly, monthly, biweekly, ad-hoc
Feedback Feedback on ease of usage and the effectiveness of resource


Entity Relationship: The entity relationship diagram defines how the data entities representing each of the domain areas such as demographics, value and preferences, etc., are tied together and helps bring all facets of caregiver data together with proper data integrity and consistency. The entity relationship also enables easy integration of caregiver data with other critical patient data that is captured in any electronic health record (EHR) data model. The graphic below illustrates the entity relationship diagram for the entities relating to caregivers and caregiving.



We have postulated that EHR vendors can extend their data model to include the elements of caregiving, but recognizing the other systemic changes this warrants regarding regulations, reimbursements, incentives, there are two alternative routes, as well:

  1. Long-term care organizations and hospice centers can evolve their data model to include all the above aspects. They can then also play a pivotal role in generating actionable insights for caring for caregivers, as it aligns fairly well with the purpose of these organizations.
  2. Health information management professionals can continue documenting insights based on the above data model as part of an EHR system (even if in an unstructured way), and artificial intelligence technologies can be used to get insights from that data.

Once the existing data model for healthcare systems—whether for EHR vendors or long-term care organizations—has been extended to incorporate the caregiver data model as outlined above, the data corresponding to the caregiver and their needs can start to flow in as the data collection modalities become operational.

The next critical step is to define how this data can be analyzed and made available for consumption to downstream systems so that it can provide actionable insights to understand caregiver needs, support the design of interventions for care for caregivers, enable measurement of the efficacy of those interventions, and set a foundation for a learning system that can continuously evolve along with caregiver needs.

There’s a long road ahead to fully addressing the needs of caregivers, but everyone in a position to either influence the decision-making process or make decisions themselves owes it to the caregivers who selflessly care for their ailing friends, family members, or relatives while also saving the healthcare system time and money.


The views expressed in this article are the authors’ own and not of the authors’ employers or of any other entities they are associated with.

Kapila Monga ( is a director in Cognizant’s Digital Business AI and Analytics Practice for Healthcare and LifeSciences.

Muralidharan Narayanasam Gandhirajan ( is senior director in Cognizant’s Digital Business AI & Analytics Practice for Healthcare.


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