By Kapila Monga, MBA

Family caregivers are people who fill the role of caregiver for an ailing family member, relative, friend, or acquaintance. According to the Family Caregiver Alliance and several other sources, 80 percent of people who need long-term care (LTC) supportive services live at home or in community settings. And more than 78 percent of adults who receive LTC at home get all their care from unpaid family and friends. Family caregivers are saving the healthcare system money by bridging the gap between health and LTC.1 According to a report by AgingInPlace, in the past five years, over 40 million family caregivers provided care worth $470 billion to loved ones.

Much has been written and documented around the impact of caregiving on the physical, emotional, social, and spiritual health of the caregiver. Most of the time, the caregiver also ends up becoming a liaison between the patient and patient’s healthcare providers, which can be overwhelming for the caregiver. Having multiple provider types treating the patient, limited to no integration across the provider specialties, limited electronic health record (EHR) adoption, and lack of healthcare access are just some of the factors that contribute to the caregiver’s agony.

Fortunately, interoperability mandates, increased EHR adoption, digitization of the healthcare ecosystem, and efforts in direction of improving healthcare access will help ease the burden for caregivers. However, a lot still needs to happen to care for the caregiver. There is a consensus across all stakeholders on the need to address the care needs of caregivers so that we can not only minimize the chances of today’s caregivers becoming tomorrow’s care recipients but can also empower this segment to continue caregiving without it having a detrimental effect on their own lives.

For the healthcare system to integrally view caregivers as the part of the ecosystem, it is critical that the data pertaining to caregivers is captured as a part of health data. While there are surveys through which the caregiver’s needs do get documented, that data needs to be part of the healthcare industry data model for a systemic change to happen.

Family Caregiver Life Cycle: The Foundation for Assessing the Health Data Needs for Care for Caregivers

A good way to comprehend the data needs for the endeavor of care for caregivers is to start by understanding the family caregiver life cycle. The life cycle has some variations based on medical conditions; however, there are some common themes across underlying facets. For the purposes of understanding the data domains and the data model, a simplified version of a family caregiver life cycle can be considered as follows:

Phase 1: Initiation Phase – A medical crisis pushes a family member to adopt the caregiver role.

Phase 2: Assimilation Phase – The caregiver understands the immediate needs of care recipient and takes up the duties.

Phase 3: Recalibration Phase – Due to the care recipients’ responsibilities, the caregiver has to reprioritize some of their goals and activities.

Phase 4: Reach for Help Phase – Realizing that doing it all on their own isn’t possible, the caregiver seeks resources for help.

Phase 5: Stability Phase – A new balance has been achieved between the caregiver’s personal aspirations and their caregiver responsibilities.

Phase 6: Now What Phase – After the care recipient’s death, the caregiver wonders “Now what?”

The caregiver life cycle is unpredictable because the if, when, and how of the transition from one phase to another is unknown and, in many cases, cannot be assured.

  • Except from Phase 1 to Phase 2, none of the transitions happen for all caregivers. While most caregivers are pushed into Phase 3 by necessity, it takes time for them to embrace Phase 3 and recalibrate their aspirations.
  • Transition from Phase 2 to Phase 3 is critical and one of the most challenging transitions because of the intertwined behavioral, psychological, sociological, and physical aspects associated with it.
  • Not all societies have access to all resources. This is not a new fact, but it proves to be a big deterrent of transition from Phase 3 to phases 4 and 5.
  • Phase 6, the Now What Phase, is inevitable for all; however, most caregivers are unable to plan for the same and wonder “Now what?” when the inevitable death of their family member does happen.

It is worth pausing and reflecting here, for herein also lie the keys for making care for caregivers a part of health data ecosystem and for forming strategies for caring for the caregiver.

Data Domains for Capturing Data for Care for Caregivers

A review of various stages of the caregiver life cycle and of caregiver needs assessment surveys used today leads us to the following 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. The following table lists the illustrative data elements in each of the data domains.


Data Domain Illustrative elements
Demographics ●      Standard demographic attributes like age, gender, education level, address, etc.

●      Information on household (number of members, income, employment status)

●      Relationship to care recipient

●      Number of years as caregiver

Values and Preferences ●      Motivation behind accepting caregiving role

●      Cultural beliefs around caregiving

●      Perceived value in caregiving

●      Perceived challenges in caregiving

●      Specific preferences toward time, place, and channel for scheduling caregiving related activities

●      Quality of relationship to the care recipient

Caregiver Health ●      Preexisting medical conditions

●      Family medical history

●      Current medical prescriptions

●      Behavioral health assessments

●      Attitude toward preventive health measures

●      Perceived/reported medication adherence

●      Attitude towards self-care related activities

Caregiver’s Perception of Care Recipient’s Health and Well-Being ●      Caregiver’s level of understanding of care recipient’s health

●      Support needed by care recipient in day-to-day activities

●      Emotional needs of care recipient

●      Other support needs of the care recipient, such as managing finances, household chores etc.

●      Quality of relationship between the caregiver and recipient

Goals, Activities and Aspirations ●      Short-, medium-, and long-term aspirations and goals of caregiver

●      Daily activities of caregiver for themselves and their other family members: spouse/kids/siblings/parents

●      Daily activities of caregiver supporting care recipient

Caregiving Support Resources ●      Support resources caregiver has access to across all facets of caregiving including family support and institutional support

●      Usages statistics of the resources


In an ideal world, EHR systems can extend their data models to incorporate the above data domains. However, there are hurdles to cross before that becomes a reality. Health information professionals can help in the interim by capturing the above information about the caregivers in the notes sections of EHR data. Artificial intelligence technologies can then enable the mining of any such data to get a holistic picture of current health and support the needs of the caregivers. This can then help in both gathering data to drive systemic policy changes and helping caregivers with the right resources at the right time.

Care for caregivers is as mighty an endeavor as it can get in the healthcare industry; the lives of two individuals (care recipient and the caregiver) are intertwined on so many different levels, and both are unable to change their respective conditions by themselves. This endeavor needs a systemwide focus and continuous efforts by government agencies, private players, and society.



Kapila Monga ( is an artificial intelligence and machine learning professional with over 15 years of experience working with healthcare and life sciences clients across North America designing AI/ML solutions. She currently works as a director in Cognizant’s Digital Business AI and Analytics Practice.


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