By Kapila Monga, MBA
A previous Journal of AHIMA article, Care for Caregivers: Laying the Foundation for the Data Currency, presented a data model that enables effective data storage and data management for assessing caregiver needs. That article built on an initial installment in this series, Care for Caregivers: Assimilating the Caregiver Needs in the Health Data Ecosystem.
It is now worth defining the key questions that the data stored in the caregiver data model will help answer, which will help in the mighty cause of care for caregivers. These questions also play a pivotal role in defining a 360-degree view of the caregiver and the subsequent analytics and artificial intelligence (AI)/machine learning (ML) that can be performed on the data to derive actionable and meaningful insights.
Key Questions the Caregiver Data Model Needs to Address
The table below details a list of questions that need to be answered to enable meaningful steps in the direction of caring for caregivers. Once these questions are answered for a sizable population of caregivers, the answers can be analyzed by policymakers or long-term care organizations in order to devise strategies and design programs and resources to support caregivers. This is particularly pertinent for family caregivers, who are generally operating with limited institutional support and are thus in need of focused efforts from the system to maintain a healthy balance between their caregiving responsibilities and their life outside of it.
Does the caregiver need support to help them ease into the caregiving role? How best can the same be provisioned?
Does the caregiver have access to the resources needed for caregiving? Can something be done to improve the access?
How useful have those resources proved to be so far in a caregiver’s caregiving journey?
Does the caregiver see a gap in available resources?
Has there been a tangible improvement in caregiver and the care recipient’s life because of those resources? What does the improvement look like?
How is the caregiver’s own physical, psychological, and emotional health and well-being?
What impact is caregiving having on the caregiver’s health?
What resources can we provide to the caregiver to help them manage their own physical, psychological, and emotional health?
Caregiver Goals and Aspirations
What profession is the caregiver currently in?
What impact does addition of caregiving responsibilities have on the caregiver’s current career plan and their ability to their job well?
Does the caregiver need to start working to support the care recipient’s needs financially?
What effect does caregiving have on the goals and aspirations of the caregiver (i.e., in comparison to when they didn’t have the caregiving responsibility)?
Has the caregiver recalibrated their goals in light of their (relatively) new caregiving responsibility?
Does the caregiver need support recalibrating their goals and calibrations?
What resources are available to the caregiver to help achieve the recalibrated goals?
How is the caregiver tracking against their goals and aspirations?
Caregiver’s Personal Life
What daily activities of the caregiver can’t be performed as before because of caregiving responsibilities? How can their schedule be rearranged? Do they have support to do so?
What impact do caregiving responsibilities have on caregiver’s family life? How can any adverse effect be minimized?
Life Adjustment Support
In the event of a care recipient’s death, does the caregiver have emotional resources to help them cope through the same?
In the event of a care recipient’s death, what will the caregiver’s life look like?
The next critical step is to examine how each of these questions will be addressed through the recommended data model. An analysis plan for each of the questions above which includes defining key KPIs to be used to measure, process of measurement of those KPIs, frequency of measurement, surrounding analytics or machine learning techniques is hence warranted. Subsequent articles will address each of the question tenets (and questions) above.
Customer 360°, member 360°, provider 360°, and patient 360° aren’t new terms for the industry. Mammoth initiatives have been undertaken to develop a comprehensive view of all these entities in the healthcare ecosystem. Given the pertinent need to address care for caregivers, it is befitting to define a comprehensive caregiver 360° view. The ability to create a caregiver 360° view will be pivotal to help the caregiver in their respective caregiving journey, as it will help answer key questions specific to the caregiver. The graphic below illustrates a view for caregiver 360°.
Caregiver 360° can be viewed as having five key dimensions: Demographics; Caregiving Responsibility; Health and Well-Being; Goals, Aspirations, and Values; and Day-to-Day Activities.
Dimensions of Caregiver 360°
– Demographic attributes specific to the caregiver
– Motivation behind caregiving
– Caregiving activities
– Care recipient’s health
– Resources available, or lack of them
– Usability of the resources
– Any other caregiving support available
Health and Well-being of Caregiver
– Past and current health and well-being of the caregiver: physical, emotional, social, spiritual
– Resources for caregiver health and well-being: available/not available
– Usability of available resources
– Attitude toward preventive health
Goals, Aspirations and Values
– Current goals and aspirations
– Need for recalibration
– Recalibrated goals and aspirations
– Additional provisioned support(need of) for achieving goals
– Daily routine of caregiver, with and without caregiving
– Perceived impact of caregiving on daily activities and family life
– Additional support provisioning plan
Answering the key questions outlined above will enable determination and design of the overall plan and strategies for improving the life of caregivers. The ability to create the caregiver 360° view will enable the decision of what strategy to use to help which caregiver and at what point. Both combined will fuel the journey of making the caregiver’s life better and will create a self-improving cycle. The cycle starts with overall determination of plan and strategies (answers to key questions), progresses with those strategies being applied to individual caregivers (caregiver 360°), and will continue to evolve as we measure the improvement in life of caregiver (caregiver data model analytics) because of those strategies, and perform necessary course correction (revisiting answers to key questions).
This will kick-start a cycle that will, in time, make tangible positive and measurable difference in the life of the caregiver. In subsequent articles, we will explore how to get answers to those key questions and how to define the self-improving engine.
Disclaimer: The views expressed in this article are authors’ own only, and not of the author’s employers nor of any other entities author is associated with.
Kapila Monga (email@example.com) is an artificial intelligence (AI) and machine learning (ML) 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 for Healthcare and LifeSciences.