Finding Meaning in Family History
Rebecca Green, Ph.D. and Seth Claybrook
Cerner Corporation 2800 Rockcreek Pkwy Kansas City, MO 64117
Terminology for Unknown versus Unavailable Family Health History
Methods (con’t)
Introduction
The purpose of this study is to review the role of the family history in ambulatory care, identify some of the current barriers to family
history documentation in Electronic Health Records (EHRs), and outline lessons learned during development of Family History
modules in an EHR. Two iterations for defining the visualization of family history in an EHR, in the form of both condition review and
pedigree review are explored. Research identified the value of pedigree views and the limitations imposed by complex family
histories during collection and reviewing patient family history. The constraints pertaining to accessing non-direct lineage family
members from a point correlated to their relatedness are addressed. The representation of patient conditions within the family history
application is explored. The research also explores the terminology associated with reporting family history that is unknown,
unavailable, or negative family history information. Finally, we outline potential characteristics of a new family history for EHR
applications.
Metrics and Data Scoring
We observed the extent to which the participants could perform the tasks, recorded the navigational paths that the participants took
for each task, and collected their comments. Effectiveness was measured as participant success rates as well as number of errors
and calculated using the Adjusted Wald.
Visualization of Family Health History
The first two studies explored potential visualizations of genetic conditions within the family health history, a condition table and a
graphical depiction. The graphical depiction was designed based on two potential visualization metaphors, pedigree fan chart and/or
the traditional genogram.
Background
Recent regulations (77 Fed. Reg. 171, 4 September, 2012) have proposed that Patient family health history is recorded for more
than 20% of unique patients (170.314(a)(13)). These regulations suggest the adoption of the definition of first degree relative used
by the National Human Genome Research Institute of the National Institutes of Health for use in EHRs. Additionally, for patients who
are asked about their family health history, but do not know their family history, it is acceptable for the provider to record the patient’ s
family history as “unknown.“
However, a study by the U.S. Centers for Disease Control and Prevention (2004) showed: Only 29.8% of patient respondents had actively collected family history information for their health records.
They were twice as likely to be female and were more likely to report maternal relatives’ health status than paternal relatives.
Furthermore, previous research identified limitations on how family history is interpreted from the patient perspective when reporting
their family history (Green et al., 2006). These results suggested that primary consideration would be given to immediate family or
first-degree relatives, but there is a clear drop off with greater degrees of genetic separation. They also tended to consider older
generation relatives more than younger generation relatives (i.e., less likely to consider (genetically close) relatives of future
generations such as a daughter, son, granddaughter, or grandson). Respondents also indicated difficulty when considering an uncle
or aunt as part of family history due to indirect relatedness.
From the physician perspective, the purpose of a family history is to understand the genetic risk of a family, but entering family history
information is very different from viewing the family relationships. Entry of family history information centers around each family
member individually (e.g., medical history of the mother or father). Family history information is viewed on a condition basis (e.g.,
which family members have a hypertension condition). Therefore, the main goal of this research was to determine potential structures
for acquiring versus viewing a family health history.
As a starting point, according to Rich et al. (2004) the characteristics of the ideal family history tool include:
Patient-completed (e.g., paper, desktop, telephone, or web input)
Adapted to patient age, gender, ethnicity, common conditions
Elicits specific patient concerns
Brief, understandable, easy to use
Compatible with multiple clinical applications (paper, EHR, PDA)
Contains clinical decision support
Branches and prioritizes based on clinical significance
Methods
A fan chart, similar to mind mapping is a visualization technique for outlining information in diagrams (see Figure 1). It starts with a
single word or text in the middle and branches out to associated ideas, words and concepts. The radiating categories use lines to
connect the words, ideas, or tasks and are part of the meaning of the relationship (Buzan, 2000). Mind mapping allows the family
tree to present the condition only within the context of the branches that provide clinical significance and reduce information noise.
Traditional genealogical representations, such as a family tree, pedigree chart, or genograms in which family relationships are
represented in a tree structure diagram (see Figure 2). Family trees are often presented with the oldest generations at the top and
the newer generations at the bottom. Traditional genealogical representations provide relational significance and can increase the
distinction of blood vs. non-blood relations.
Clinicians were found to favor organizing family history information graphically in a pedigree or genogram format (86%, n = 14),
similar to Figure 2, as being the more helpful than a condition format (see Figure 3) for determining genetic risk information. From the
clinical perspective, “The family history information that is pertinent to my patient is pretty basic; a single line. Father, deceased,
diabetes, age of death, that’ s all I’ m looking for...”.
Figure 1. A fan chart metaphor for visualizing family health history.
Figure 2. A pedigree chart metaphor for visualizing family health history.
Recruited participants had a mix of backgrounds and demographic characteristics conforming to the solution and role tested. Table 1
describes the participant’ s specialties, professional experience, and experience with EHRs. A total of 34 participants were recruited,
of which 32 were physicians and 2 were registered nurses. One participant in study 2 was not analyzed due to incomplete data.
7 physicians evaluated condition visualization (Study 1)
15 physicians evaluated condition vs. pedigree add/change/access designs (Study 2)
7 physicians evaluated terminology usage for unknown/unavailable FH (Study 3)
3 physicians, 2 registered nurses evaluated desktop implementations (Study 4)
Figure 3. A family health history displayed according to number of family
members with a condition.
Table 1. Clinical speciality, practice experience (years), and EHR experience (years) by Study.
Study 1
Specialties
Avg. Practic Exp. (yrs.)
Avg. EHR Exp. (yrs.)
Study 2
Med., Pediatrics, Fam.
Pediatrics, Fam. Med., Int.
Gen. Surgery, Critical
ED, and Gen. Surgery Med.,
Care, and OBGYN
16.86
20.64
12.93
7.00
Study 3
Study 4
Pediatrics, Fam. Med.,
and Urology
Pediatrics, Fam. Med.,
and Emergency RN
22.00
14.50
14.20
7.43
Test Environment
Remote desktop testing was conducted on a testing laptop using a remote screen sharing application and mobile applications were
downloaded and viewed on the clinician’ s device. In-person testing was conducted in the physicians office environment or a quiet
testing room, set up much like an office environment. All of the studies were conducted using hi-fidelity prototypes that were available
to the participants on a password protected server. Participants worked at a desk or table with a testing computer running the tested
solution connected through a wireless internet connection. The participant’ s interaction with the EHR was captured and recorded
digitally with screen capture software running on the test machine.
Conclusions
While the simplest visualization is to develop a perfect binary tree of the patient, and first-degree relatives, extending the
generational lines with degrees of relatedness, cultural relationships, and the patient’ s own medical history complicates the use of a
straightforward metaphor. Developing visualization for affinity kinship within the family history structure in electronic health records
presents a specific challenge in electronic health records. The challenges introduced from viewing the family health history in the
electronic medium, suggests that removing the extra noise introduced by a traditional pedigree could improve physician
understanding of the genetic condition information. This research found that grouping of affinity kinship outside of the descent group
within the visualization metaphor should be indicated from a point correlated to their relatedness. However, providing indications of
relatedness as interpreted by various cultural forms that recognize kinship beyond biological relationships is a challenge that will still
need to be approached in future studies.
Patient interpretations of “family history“ suggests that determining the family health history information might require additional
specificity or elaboration or perhaps different terminology substituted to better communicate the intended concept. Additionally, our findings suggest that the use of alternative labeling for ‘ negative’ versus ‘ unknown’ history (i.e., “No known problems”
and “No documented medical history for this patient.”) can improve the communication of condition information. Unfortunately, the accuracy of patient-provided information is still limited by the patient’ s understanding, memory, and willingness to
disclose health issues of family members.
We found that when the family health information is consolidated into a single condition view (see Figure 4), 57% of the physicians
who evaluated this view of family history found that the combination of family health history overall and family kinship health history
was confusing to understand and navigate (n = 7). They found the combination of condition information for a single family member
while displaying the combination of family members presented an organizational disparity.
Demographics
It is important to know that family health history definitions discriminate between individuals who currently have and do not have the
condition of interest. These terms are referred to as positive predictive value (PPV) and negative predictive value (NPV). PPV refers
to those individuals who will actually develop or currently have the disease. NPV indicates the individuals who will remain disease
free or do not currently have the disease. From the clinical perspective, while the term ‘ no known allergies’ is commonly used in
electronic health records, a consistent similiar term is not used for family health history.
In study 3, we evaluated various terms including ‘ negative family health history’ to represent family relations that do not have a family
health history. When presented with family health history records displaying the terms ‘ Negative history...’ (57%) and ‘ Unknown
history...’ (71%) they were commonly misunderstood (n = 17.8, p = .61).
“Is this negative as in bad, or negative as in he has no history? Personally primary care doctors aren’ t going to mark
positive or negative. It’ s moot if it’ s I don’ t know or they answered no problem.”
Furthermore, since patients more accurately report the absence of disease than the presence of disease in family members (Berg et
al., 2009), the importance of reporting a known negative is critical. When presented with a family health history module that provided
a consistent approach to collecting known negatives, they preferred the approach that included defining a condition as positive or
negative (60%, n = 5).
Figure 4. Family health history for a condition.
Additional constraints we encountered when developing visualization for affinity kinship within the family history structure in electronic
health records included:
Grouping of affinity kinship outside of the descent group within the visualization metaphor.
Generational birth order.
Recognizing relatedness as interpreted by various cultural forms that recognize kinship beyond biological relationships.
Study 2 found that clinicians would not successfully identify affliate family members when grouped outside of the generational
referent (n = 14, p = .5). Furthermore, when reviewing genetic condition information for a patient’ s decedents in a generational
kinship, clinicians expect to view the family members arranged in birth order from left to right (64%, n = 14). In fact, clinicians
expected to directly identify family decendents by name, even when the family member is an 2nd or 3rd order generational relation
(100%, n = 14). Therefore, when reviewing condition information for a specific affiliate family member, the clinician and patient
communication requires describing the complexities surrounding the generational information, maternal/paternal affiliation, and sex
of the family member.
The lessons learned from exploring potential structures for acquiring versus viewing a family health history include several constraints
imposed by electronic health records: Increase in complexity due to number of generational relatives
Terminology associated with age at onset versus age at death
Indications of lineage and generational descent (marriage vs. siblings)
Abstraction of medical genetic standards of (maternal vs. paternal relatives)
Furthermore, the collection of family history by the primary care giver can and should be improved by the use of electronic health
records in which the family health information is connected to the patient by the identification of relations and internal connections are
applied by the system.
“I would like to be able to connect the tree to other charts…that is how an EMR should work.” --Physician
As an organization, we will continue to review evidence about where and how primary care physicians collect family history and how
best to use this information to improve the tools being developed as well as how they may affect the standardized collection of family
history.
References
Berg, A. O., Baird, M. A., Botkin, J. R., Driscoll, D. A., Fishman, P. A., Guarino, P. D., Hiatt, R. A., Jarvik, G. P., Millon-Underwood, S., Morgan, T. M., Mulvihill, J. J., Pollin, T. I., Schimmel, S. R., Stefanek, M. E. E., Vollmer, W. M., and Williams, J. K. (2009). National institutes of health State-of-the-Science conference statement: Family history and improving health. Annals of internal medicine, 151(12):872-877.
Buzan, Tony. (2000). The Mind Map Book, Penguin Books, 1996.
Centers for Disease Control and Prevention (2004). Awareness of family health history as a risk factor for disease: United States, 2004. Morbidity and Mortality Weekly Report, 53, 1044-7.
Green, R. S., Cowley, J. A., & Wogalter, M. S. (2006). Acquiring health information: Interpreting terminology and reported search sources. Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, 50, 984-988.
Health Information Technology: Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology, 2014 Edition; Revisions to the Permanent Certification Program for Health Information Technology. Federal Register 77:171 (4 September 2012) p. 54163.
Lindenmeyer, A., Griffiths, F., and Hodson, J. (2011). ’ The family is part of the treatment really’ : A qualitative exploration of collective health narratives in families. Health, 15(4):401–415.
Rich, E. C., Burke, W., Heaton, C. J., Haga, S., Pinsky, L., Short, M. P., and Acheson, L. (2004). Reconsidering the family history in primary care. Journal of general internal medicine, 1
(3):273-280.
Acknowledgments
Thanks go to the clinicians across the country who volunteered their time to provide feedback during the development of family history modules exploring alternative visualization methods.