Visualizations to SupportPatient-Clinician Communication ofCare Plans

Adam PererIBM T.J. Watson Research CenterYorktown Heights, NY 10598United Statesadam.perer@us.ibm.com

David GotzIBM T.J. Watson Research CenterYorktown Heights, NY 10598United Statesdgotz@us.ibm.com

Copyright is held by the author/owner(s).CHI PCC Workshop’13, April 28, 2013, Paris, France.ACM 978-1-XXXX-XXXX-X/XX/XX.

AbstractCareFlow is a novel system designed to help doctors andpatients communicate about possible care plans and theiroutcomes. Using observed outcomes from clinically similarpatients, CareFlow allows doctors and patients to under-stand which treatments have historically been most effec-tive.

IntroductionWhen a patient is diagnosed with a disease, their doctor willoften devise a care plan, a sequence of medical treatmentsto help manage their disease or condition. A successful out-come often relies on patients understanding and adheringto the care plan, and our work aims to help doctors com-municate with patients about the impact of a care plan ontheir health.

Our work leverages the rich longitudinal data found in Elec-tronic Medical Records (EMRs) to empower clinicians witha new data-driven resource. This paper describes CareFlow,a system designed to visualize the effectiveness of differenttreatment sequences among similar patients. Using the rel-evant clinical data of a specific target patient, CareFlowmines EMR data to find clinically similar patients using ourpatient similarity analytics. CareFlow then visualizes all ofthe different care plans that these similar patients have un-dergone, while providing context on which care plans were

successful and which were not. The resulting visualizationsupports the identification of the most desirable and mostproblematic care plans, which can serve as evidence for pa-tients to understand and adhere to their prescribed courseof treatment.

Mining and Visualizing Care Plans from DataCareFlow is designed to show doctors and patients a per-sonalized view of the various care plans and associated out-comes that are most relevant to the patient and their med-ical condition. As the interface is personalized, CareFlowbegins by analyzing the medical history of the patient. Us-ing historical medications, symptoms, diagnoses and lab re-sults of the patient, CareFlow applies our patient similarityanalytics [1] to find a population of clinically similar pa-tients. The care plans and outcomes of this similar patientpopulation will be made visible by CareFlow.

While patients may be able to make sense out of their owncare plan, doing so for all care plans observed within a pop-ulation of similar patients is much more challenging. Al-ternative care plans may include a large variety of differenttypes of treatments, and the sequence of these treatmentsoften varies as well. CareFlow was designed to summarizethe temporal sequence of treatments in a comprehensivevisual interface using an enhancement of the Outflow visu-alization technique [2]. As described in Figure 1, treatmentsare represented as nodes and positioned along the horizon-tal axis, which represents the sequence of treatments overtime. The diagnosis of a disease occurs on the far left ofthe visualization, and treatments in the care plan extendto the right. For instance, in this illustrative example, pa-tients diagnosed with a specific disease were then giveneither Diuretics or Cardiotonics. The height of each nodeis proportional to the number of patients that took a giventreatment. So, in this example, twice as many patients

[ ]

Node

TimeEdge

LinkEdge

Lege

nd

Future

[t1 ,t

3 ]

Diagnosis

Poor Good

ColorOutcome of Health

[Diu

retics]

[Diu

retics,B

eta B

lockers]

[Ca

rdio

nto

nics]

[Diu

reticds,

An

tian

gin

al]

[Ca

rdio

nto

nics,

An

tihyp

ertensive]

Figure 1: The visual encoding of care plans in CareFlow. The heightof nodes represents the number of patients. The width of time edgesrepresents the average duration of treatments. Color represents theaverage patient outcome.

were prescribed Diuretics than treatment Cardiotonics.

Each treatment node is also augmented with a time edge,whose width encodes the duration of a how long it tookpatients to transition to this treatment. In this example,patients were typically prescribed Cardiotonics more quicklythan Diuretics after diagnosis. Link edges are also presentto connect nodes from their previous and future nodes inthe care plan.

The visual elements are colored according to the averageoutcome of all patients represented by the node or edge.

Elements that are colored green represent parts of the careplan where patients remained healthy, whereas elementsthat are colored red indicate that the corresponding patientsended up in poor health. For example, for patients whoinitially took Diuretics, the next treatment that leads tothe best outcome for patients is Beta Blockers, whereasthe patients treated with Antianginal Agents ended up withworse outcomes.

Patient-Clinician Communication of Care Plansfor Congestive Heart FailureThis scenario involves a doctor who has recently diagnoseda patient with congestive heart failure and wishes to useCareFlow to communicate the effectiveness of possible treat-ments with their patient. CareFlow connects to a longi-tudinal EMR database of over 50,000 patients with heartconditions spanning over 8 years.

On the left-hand side of Figure 2, a summary of the pa-tient’s relevant medical history is shown, including recentmedications, symptoms, and diagnoses. In the center panelof Figure 2, a visualization of the care plans of the 300 mostsimilar patients is shown. The left-most node representsthese similar patients at their point of diagnosis with heartfailure. As the visualization extends to the right, the varioustreatment sequences of similar patients are shown. The careplans are colored according to a continuous color scale, asindicated by the legend in the upper-right. By default, plansthat are colored red implies most patients within that nodeended up being hospitalized, whereas green plans meansmost patients managed to stay out the hospital. However,CareFlow empowers doctors to interactively customize theoutcome measure. For example, it is possible for the doc-tor to use CareFlow to focus on care plans that lead topatient deaths, to better understand treatments associatedwith mortality.

Patients may be curious how the similar patients were cho-sen, and they can find details in the Similarity panel (notpictured). Here, the dominant features are presented thatwere used to derive this patient population, including medi-cations, diagnoses, lab tests, and symptoms. This informa-tion may be useful in convincing the patient that the careplan outcomes presented in CareFlow are relevant to them.

CareFlow provides doctors and patients with the ability toget more information about the patients who undertook aparticular care plan. By selecting a Treatment node, doc-tors an view a precise count of the number of patientsthe node represents, as well as the average outcome forthese patients. In addition, the right panel of the interfacedisplays summary information about a set of patients bydisplaying factors common to this cohort, as well as fac-tors rare in this group. For instance, the selected patientsin Figure 2 typically exhibit symptoms of Rales and havemore diagnoses of Chronic Kidney disease. On the otherhand, these patients exhibit Acute Pulmonary Edema andPleural Effusion less frequently than the rest of the simi-lar patients. By presenting this information, patients canunderstand what they have in common with the similar pa-tient population.

ConclusionTo conclude, CareFlow is a novel system designed to helpdoctors and patients understand the outcomes associatedwith possible treatment plans. CareFlow provides an overviewof the care plans used to treat a cohort of similar pa-tients which may help promote understanding and adher-ence among patients.

About the AuthorsAdam Perer and David Gotz are research scientists in theHealthcare Analytics Research Group at IBM T.J. Wat-

son Research Center. Our research group is composed ofphysicians, medical informatics researchers, and computerscientists to design, develop, and deploy novel healthcareanalytics and visualization technologies. Adam Perer plansto attend the workshop.

AcknowledgmentsWe thank Jianying Hu, Robert Sorrentino, Harry Stravopolous,and Jimeng Sun for their assistance and feedback in design-ing the CareFlow system.

Figure 2: CareFlow’s visual interface. The left panel displays a summary of the patient’srelevant medical history. The center panel displays a visualization of the care plans of the 300most similar patients. The right panel displays the factors associated with a selected subset ofpatients.

References[1] Wang, F., Sun, J., and Ebadollahi, S. Integrating dis-

tance metrics learned from multiple experts and its ap-plication in inter-patient similarity assessment. In SDM,SIAM / Omnipress (2011), 59–70.

[2] Wongsuphasawat, K., and Gotz, D. Exploring flow, fac-tors, and outcomes of temporal event sequences withthe outflow visualization. IEEE Transactions on Visu-alization and Computer Graphics 18, 12 (Dec. 2012),2659 –2668.