pith. sign in

arxiv: 2605.20207 · v1 · pith:YYWSYLVYnew · submitted 2026-04-09 · 💻 cs.HC

HealthTale: A Patient-Centric Health Story Visualization Tool

Ryan Smith , Kyle D. Chin , Tamara Munzner This is my paper

Pith reviewed 2026-05-21 09:55 UTC · model grok-4.3

classification 💻 cs.HC
keywords patient narrativeshealth visualizationtimeline representationclinical communicationnarrative elicitationuser-centered designhealth informatics
0
0 comments X

The pith

HealthTale converts patients' freeform health narratives into structured timelines to improve communication during clinical visits.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Patients often struggle to share complete accounts of their health histories, mixing clinical events with personal experiences, in time-limited doctor appointments. The paper presents HealthTale as a visualization system that lets patients enter stories in their own words and automatically organizes them into timeline views grouped by health concerns. This design draws from patterns identified through expert discussions, online stories, interviews, and collected narratives. Testing with patients and clinicians shows the tool helps patients recall details, organize their thoughts, and advocate for themselves while allowing clinicians to interpret the information quickly and reach shared understanding.

Core claim

HealthTale transforms freeform narratives into structured timeline representations, grounded in a data abstraction that models health stories as events grouped by health concern and time, capturing both clinical and contextual information with flexibility to handle temporally imprecise data and non-linear distributions of events across time. Evaluation with patients and clinicians finds that this supports recall, organization, and self-advocacy for patients while enabling clinicians to rapidly interpret patient-generated narratives and establish a shared understanding.

What carries the argument

The data abstraction model that represents health stories as events grouped by health concern and time, with support for imprecise timing and non-linear event sequences.

If this is right

  • Patients gain improved ability to recall and structure their health experiences for clinical discussions.
  • Clinicians can interpret patient stories more rapidly and reach shared understanding in initial encounters.
  • The model accommodates real variations in event timing and sequence that occur in actual health histories.
  • Patients experience greater support for self-advocacy during medical conversations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar timeline tools could extend to ongoing management of long-term conditions by allowing patients to update stories between visits.
  • The approach might transfer to other domains where individuals must convey complex personal histories under time pressure.
  • Larger studies with diverse patient groups could test whether the identified story patterns hold across different backgrounds and cultures.

Load-bearing premise

The recurring patterns in how people construct health stories from the studied interviews and narratives are representative enough to support a general data model.

What would settle it

A controlled comparison where patients using HealthTale show no gains in recall, organization, or clinician understanding compared to standard verbal accounts would indicate the approach does not deliver the claimed benefits.

Figures

Figures reproduced from arXiv: 2605.20207 by Kyle D. Chin, Ryan Smith, Tamara Munzner.

Figure 1
Figure 1. Figure 1: Example health stories created during the evaluation of the patient-centric HealthTale system. These visualizations illustrate the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Health story data abstraction. Events are atomic items with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: In HealthTale, after the patient writes free-form text in the Elicitation Panel on the left (A), it is automatically transformed into structured data [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pseudocode for the grouping and layout algorithm. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Layout comparison for participant E22 from the elicitation ses [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Patients often struggle to communicate coherent accounts of their health histories during time-constrained clinical encounters. These accounts, which we refer to as health stories, include both clinical events and lived experiences. Existing systems prioritize structured, clinician-centered data and provide limited support for eliciting and communicating patient-generated narratives. We present HealthTale, a patient-centric visualization system designed to elicit health stories from patients and structure them to facilitate communication during initial clinical conversations. Its design arises from a multi-stage qualitative investigation across domain expert discussions, online narratives (n=20), patient (n=11) and clinician (n=6) interviews, and elicited health stories (n=22), identifying recurring patterns in how individuals construct and communicate their health stories. HealthTale transforms freeform narratives into structured timeline representations, grounded in a data abstraction that models health stories as events that are grouped by health concern and time, capturing both clinical and contextual information, with the flexibility to handle temporally imprecise data and non-linear distributions of events across time. Through evaluation with patients (n=34) and clinicians (n=3), we find that HealthTale supports recall, organization, and self-advocacy, while enabling clinicians to rapidly interpret patient-generated narratives and establish a shared understanding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. HealthTale is a patient-centric visualization system that elicits freeform health stories from patients and transforms them into structured timeline representations grouped by health concern and time. The design is grounded in a multi-stage qualitative investigation involving domain expert discussions, online narratives (n=20), patient (n=11) and clinician (n=6) interviews, and elicited stories (n=22) to identify recurring construction patterns, with flexibility for temporally imprecise and non-linear events. Evaluation with patients (n=34) and clinicians (n=3) reports that the tool supports recall, organization, and self-advocacy for patients while enabling clinicians to rapidly interpret narratives and establish shared understanding.

Significance. If the core claims hold, the work could advance patient-clinician communication tools in HCI by offering a flexible data abstraction that integrates clinical events with lived experiences, addressing gaps in existing clinician-centered EHR systems. The multi-stage qualitative grounding from diverse sources (online narratives, interviews, and elicited stories) provides a credible foundation for the abstraction model and represents a methodological strength.

major comments (1)
  1. Evaluation section: the claim that HealthTale enables clinicians to 'rapidly interpret patient-generated narratives and establish a shared understanding' rests on a sample of n=3 clinicians with no reported baseline (e.g., unstructured notes or standard EHR review), controls, or quantitative measures. This sample size is load-bearing for the communication-facilitation component of the central claim and does not allow outcomes to be distinguished from selection effects, novelty, or small-sample bias.
minor comments (1)
  1. Abstract: lacks any description of analysis methods, statistical or qualitative coding approaches, or outcome measures used in the patient (n=34) and clinician (n=3) evaluations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which identifies a key limitation in how we present the clinician evaluation results. We respond to the major comment below and commit to revisions that better contextualize our findings.

read point-by-point responses

  1. Referee: Evaluation section: the claim that HealthTale enables clinicians to 'rapidly interpret patient-generated narratives and establish a shared understanding' rests on a sample of n=3 clinicians with no reported baseline (e.g., unstructured notes or standard EHR review), controls, or quantitative measures. This sample size is load-bearing for the communication-facilitation component of the central claim and does not allow outcomes to be distinguished from selection effects, novelty, or small-sample bias.

    Authors: We agree that the clinician evaluation (n=3) is small and lacks a baseline comparison, controls, or quantitative metrics, which constrains the strength of claims about rapid interpretation and shared understanding. The study was exploratory and qualitative in nature, intended to surface initial insights from clinician interviews rather than to demonstrate comparative efficacy. In the revised manuscript we will moderate the relevant claims in the abstract and evaluation section to emphasize their preliminary character, add an explicit limitations subsection that details the small sample, absence of controls, and risks of selection or novelty effects, and expand the description of the interview protocol and thematic analysis for greater transparency. These adjustments will align the reported conclusions more closely with the evidence obtained. revision: yes

Circularity Check

0 steps flagged

No significant circularity; design and evaluation remain independent

full rationale

The paper's derivation chain consists of a multi-stage qualitative investigation (domain expert discussions, online narratives n=20, patient/clinician interviews n=11+6, elicited stories n=22) that informs the data abstraction and system design, followed by a separate evaluation phase with distinct participants (patients n=34, clinicians n=3). No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The central claims about support for recall, organization, and clinician interpretation are presented as outcomes of the evaluation rather than reducing by construction to the input data or prior self-references. This is a standard HCI iterative design process with independent validation steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on qualitative patterns observed in a modest set of interviews and narratives rather than on fitted parameters or new postulated entities; the data abstraction is presented as derived from those observations.

axioms (1)
  • domain assumption Health stories consist of events that can be grouped by health concern and time while capturing both clinical and contextual information.
    Stated as the basis for the data abstraction in the abstract.

pith-pipeline@v0.9.0 · 5752 in / 1186 out tokens · 34293 ms · 2026-05-21T09:55:29.808951+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    HealthTale transforms freeform narratives into structured timeline representations, grounded in a data abstraction that models health stories as events that are grouped by health concern and time, capturing both clinical and contextual information, with the flexibility to handle temporally imprecise data and non-linear distributions of events across time.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    The grouping and layout algorithm is designed to support health story representations for initial clinical encounters... using DBSCAN... first-fit packing strategy.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

65 extracted references · 65 canonical work pages

  1. [1]

    Claude sonnet 4.6

    Anthropic. Claude sonnet 4.6. https://www.anthropic.com/claude,

    work page

  2. [2]

    Large language model. 6, 7

    work page

  3. [3]

    R. Bade, S. Schlechtweg, and S. Miksch. Connecting time-oriented data and information to a coherent interactive visualization. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 105–112. ACM, 2004. doi: 10.1145/985692.985706 2

    work page doi:10.1145/985692.985706 2004

  4. [4]

    Bhattacharyya, K

    O. Bhattacharyya, K. Mossman, L. Gustafsson, and E. C. Schneider. Using human-centered design to build a digital health advisor for patients with complex needs: Persona and prototype development.Journal of Medical Internet Research, 21(5):e10318, 2019. doi: 10.2196/10318 2

    work page doi:10.2196/10318 2019

  5. [5]

    Campbell, A

    R. Campbell, A. Ju, M. T. King, and C. Rutherford. Perceived benefits and limitations of using patient-reported outcome measures in clinical practice with individual patients: a systematic review of qualitative studies. Quality of Life Research, 31(6):1597–1620, 2022. doi: 10.1007/s11136 -021-03003-z 2

    work page doi:10.1007/s11136 2022

  6. [6]

    Canali, V

    S. Canali, V . Schiaffonati, and A. Aliverti. Challenges and recommenda- tions for wearable devices in digital health: Data quality, interoperability, health equity, fairness.PLOS digital health, 1(10):e0000104, 2022. doi: 10.1371/journal.pdig.0000104 2

    work page doi:10.1371/journal.pdig.0000104 2022

  7. [7]

    Carpendale, A

    S. Carpendale, A. Thudt, C. Perin, and W. Willett. Subjectivity in personal storytelling with visualization.Information Design Journal, 23(1):48–64, Jan 2017. doi: 10.1075/idj.23.1.07thu 2

    work page doi:10.1075/idj.23.1.07thu 2017

  8. [8]

    R. Charon. Narrative medicine: A model for empathy, reflection, profes- sion, and trust.Annals of Internal Medicine, 135(10):930–934, 2001. doi: 10.7326/0003-4819-135-10-200111200-00022 1

    work page doi:10.7326/0003-4819-135-10-200111200-00022 2001

  9. [9]

    E. K. Choe, N. B. Lee, B. Lee, W. Pratt, and J. A. Kientz. Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1143–1152. Association for Computing Machinery, New York, NY , 2014. doi: 10.1145/2556288.2557372 2

    work page doi:10.1145/2556288.2557372 2014

  10. [10]

    Detmar, N

    S. Detmar, N. K. Aaronson, L. Wever, M. Muller, and J. Schornagel. How are you feeling? who wants to know? patients’ and oncologists’ preferences for discussing health-related quality-of-life issues.Journal of Clinical Oncology, 18(18):3295–3301, 2000. doi: 10.1200/JCO.2000.18. 18.3295 2

    work page doi:10.1200/jco.2000.18 2000

  11. [11]

    Dodson, A

    P. Dodson, A. M. Haase, M. Jeffreys, and C. Hales. Capturing patient experiences of care with digital technology to improve service delivery and quality of care: A scoping review.Digital Health, 10:20552076241282900,

    work page

  12. [12]

    doi: 10.1001/jamanetworkopen.2023.21746 2

    work page doi:10.1001/jamanetworkopen.2023.21746 2023

  13. [13]

    Esmail, K

    K. Esmail, K. Nanji, and S. Ziai. Evaluation of the effectiveness of ophthalmology emergency clinic intake forms.University of Ottawa Journal of Medicine, 10(2):42–46, 2021. doi: 10.18192/uojm.v10i2.4851 2

    work page doi:10.18192/uojm.v10i2.4851 2021

  14. [14]

    Ester, H.-P

    M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. InProceed- ings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231, 1996. 6

    work page 1996

  15. [15]

    Faisal, A

    S. Faisal, A. Blandford, and H. W. Potts. Making sense of personal health information: challenges for information visualization.Health Informatics Journal, 19(3):198–217, 2013. doi: 10.1177/1460458212465213 1, 2

    work page doi:10.1177/1460458212465213 2013

  16. [16]

    Figueiras

    A. Figueiras. Narrative visualization: A case study of how to incorporate narrative elements in existing visualizations. In2014 18th International Conference on Information Visualisation, pp. 46–52. IEEE, Paris, France,

    work page

  17. [17]

    doi: 10.1109/IV.2014.79 2

    work page doi:10.1109/iv.2014.79 2014

  18. [18]

    Fulda, M

    J. Fulda, M. Brehmer, and T. Munzner. TimeLineCurator: Interactive authoring of visual timelines from unstructured text.IEEE Transactions on Visualization and Computer Graphics, 22(1):300–309, 2016. doi: 10. 1109/TVCG.2015.2467531 3, 8

    work page arXiv 2016

  19. [19]

    K. J. Gilmore, I. Corazza, L. Coletta, and S. Allin. The uses of patient reported experience measures in health systems: A systematic narrative review.Health Policy, 128:1–10, 2023. doi: 10.1016/j.healthpol.2022.07. 008 2

    work page doi:10.1016/j.healthpol.2022.07 2023

  20. [20]

    J. M. Hall and J. Powell. Understanding the person through narrative. Nursing research and practice, 2011(1):293837, 2011. doi: 10.1155/2011/ 293837 1

    work page doi:10.1155/2011/ 2011

  21. [21]

    Herrmann

    D. Herrmann. Reporting current, past, and changed health status: what we know about distortion.Medical Care, 33(4):AS89–AS94, 1995. 2

    work page 1995

  22. [22]

    Hickmann, P

    E. Hickmann, P. Richter, and H. Schlieter. All together now–patient engagement, patient empowerment, and associated terms in personal healthcare.BMC Health Services Research, 22(1):1116, 2022. doi: 10. 1186/s12913-022-08501-5 1, 2

    work page 2022

  23. [23]

    Higgins, E

    T. Higgins, E. Larson, and R. Schnall. Unraveling the meaning of patient engagement: a concept analysis.Patient Education and Counseling, 100(1):30–36, 2017. doi: 10.1016/j.pec.2016.09.002 2

    work page doi:10.1016/j.pec.2016.09.002 2017

  24. [24]

    J. S. Hirsch, J. S. Tanenbaum, S. L. Gorman, C. Liu, E. Schmitz, D. Hashorva et al. Harvest: A longitudinal patient record summarizer. Journal of the American Medical Informatics Association, 22(2):263–274,

    work page

  25. [25]

    doi: 10.1136/amiajnl-2014-002945 2

    work page doi:10.1136/amiajnl-2014-002945 2014

  26. [26]

    Huang, M

    D. Huang, M. Tory, B. A. Aseniero, L. Bartram, S. Bateman, S. Carpen- dale et al. Personal visualization and personal visual analytics.IEEE Transactions on Visualization and Computer Graphics, 21(3):420–433,

    work page

  27. [27]

    doi: 10.1109/TVCG.2014.2359887 3

    work page doi:10.1109/tvcg.2014.2359887 2014

  28. [28]

    J. S. Hudson et al. Outcomes of using an electronic anesthesia-focused electronic intake form.Journal of PeriAnesthesia Nursing, 40(4):e62–e63,

    work page

  29. [29]

    doi: 10.1016/j.jopan.2025.05.078 2

    work page doi:10.1016/j.jopan.2025.05.078 2025

  30. [30]

    J. A. Kientz, R. I. Arriaga, and G. D. Abowd. Baby steps: evaluation of a system to support record-keeping for parents of young children. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1713–1722, 2009. doi: 10.1145/1518701.1518965 2

    work page doi:10.1145/1518701.1518965 2009

  31. [31]

    N. W. Kim, B. Bach, N. Kerracher, S. Drucker, D. Wigdor, and S. Carpen- dale. Datatoon: Drawing dynamic network comics with pen + touch interaction. InProceedings of the SIGCHI Conference on Human Fac- tors in Computing Systems (CHI), pp. 1–12, 2019. doi: 10.1145/3290605. 3300657 3

    work page doi:10.1145/3290605 2019

  32. [32]

    Y .-S. Kim, B. Lee, S. Drucker, and N. Elmqvist. Inking your insights: Investigating digital externalization behaviors during data analysis. In Proceedings of the ACM International Conference on Interactive Surfaces and Spaces (ISS), pp. 255–267, 2019. doi: 10.1145/3343055.3359706 3

    work page doi:10.1145/3343055.3359706 2019

  33. [33]

    Y .-S. Kim, K. Reinecke, and J. Hullman. Explaining the gap: Visualizing one’s predictions improves recall and comprehension of data. InProceed- ings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1375–1386. Association for Computing Machinery, 2017. doi: 10.1145/3025453.3025592 2

    work page doi:10.1145/3025453.3025592 2017

  34. [34]

    Kong and K

    H.-K. Kong and K. Karahalios. Addressing cognitive and emotional bar- riers in parent-clinician communication through behavioral visualization webtools. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1–12. ACM, 2020. doi: 10.1145/3313831. 3376687 2

    work page doi:10.1145/3313831 2020

  35. [35]

    Kozinets.Netnography: The essential guide to qualitative social media research

    R. Kozinets.Netnography: The essential guide to qualitative social media research. SAGE, 3rd ed., 2020. 4

    work page 2020

  36. [36]

    Lagan, A

    S. Lagan, A. Ramakrishnan, E. Lamont, et al. Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder.International Journal of Bipolar Disorders, 8(39), 2020. doi: 10. 1186/s40345-020-00202-4 2

    work page 2020

  37. [37]

    K. O. Leslie and J. Rosai. Standardization of the surgical pathology report: formats, templates, and synoptic reports.Seminars in Diagnostic Pathology, 11(4):253–257, 1994. 2

    work page 1994

  38. [38]

    I. Li, A. K. Dey, and J. Forlizzi. Understanding my data, myself: Support- ing self-reflection with ubicomp technologies. InProceedings of the 13th International Conference on Ubiquitous Computing, pp. 405–414. Associ- ation for Computing Machinery, 2011. doi: 10.1145/2030112.2030166 2, 3

    work page doi:10.1145/2030112.2030166 2011

  39. [39]

    Liang, B

    Z. Liang, B. Ploderer, W. Liu, and T. Nagel. Sleepexplorer: A visualiza- tion tool to make sense of correlations between personal sleep data and contextual factors.Personal and Ubiquitous Computing, 20(6):985–1000,

    work page

  40. [40]

    doi: 10.1007/s00779-016-0966-9 3

    work page doi:10.1007/s00779-016-0966-9

  41. [41]

    Manalili, M

    K. Manalili, M. J. Santana, and I. P. in Clinical Practice Implementation Science Work Group. Using implementation science to inform the inte- gration of electronic patient-reported experience measures (eprems) into healthcare quality improvement: description of a theory-based application in primary care.Quality of Life Research, 30(11):3073–3084, 2021. d...

    work page doi:10.1007/s11136-020-02588-1 2021

  42. [42]

    Norouzinia, M

    R. Norouzinia, M. Aghabarari, M. Shiri, M. Karimi, and E. Samami. Communication barriers perceived by nurses and patients.Global Journal of Health Science, 8(6):65, 2015. doi: 10.5539/gjhs.v8n6p65 2

    work page doi:10.5539/gjhs.v8n6p65 2015

  43. [43]

    Offenwanger, M

    A. Offenwanger, M. Brehmer, F. Chevalier, and T. Tsandilas. Timesplines: Sketch-based authoring of flexible and idiosyncratic timelines.IEEE Transactions on Visualization and Computer Graphics, 30(1):34–44, 2024. doi: 10.1109/TVCG.2023.3326933 3

    work page doi:10.1109/tvcg.2023.3326933 2024

  44. [44]

    Pirolli, S

    P. Pirolli, S. K. Card, and M. M. Van Der Wege. The effect of information scent on searching information: visualizations of large tree structures. In Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 161–172. Association for Computing Machinery, New York, NY , USA,

    work page

  45. [45]

    doi: 10.1145/345513.345304 8

    work page doi:10.1145/345513.345304

  46. [46]

    Plaisant, B

    C. Plaisant, B. Milash, A. Rose, S. Widoff, and B. Shneiderman. LifeLines: visualizing personal histories. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 221–227. Association for Computing Machinery, New York, NY , 1996. 2

    work page 1996

  47. [47]

    Rajabiyazdi, C

    F. Rajabiyazdi, C. Perin, L. Oehlberg, and S. Carpendale. Communicating patient health data: A wicked problem.IEEE Computer Graphics and Applications, 41(6):179–186, 2021. doi: 10.1109/MCG.2021.3112845 1, 2

    work page doi:10.1109/mcg.2021.3112845 2021

  48. [48]

    Rajabiyazdi, C

    F. Rajabiyazdi, C. Perin, J. Vermeulen, H. MacLeod, D. Gromala, and S. Carpendale. Differences that matter: In-clinic communication chal- lenges. InProceedings of the 11th EAI International Conference on Per- vasive Computing Technologies for Healthcare, pp. 251–260. Association for Computing Machinery, New York, NY , 2017. doi: 10.1145/3154862. 3154885 2

    work page doi:10.1145/3154862 2017

  49. [49]

    Rapp and F

    A. Rapp and F. Cena. Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data.Inter- national Journal of Human-Computer Studies, 94:1–17, 2016. doi: 10. 1016/j.ijhcs.2016.05.006 2

    work page 2016

  50. [50]

    Rostamzadeh, S

    N. Rostamzadeh, S. S. Abdullah, and K. Sedig. Data-driven activities involving electronic health records: an activity and task analysis frame- work for interactive visualization tools.Multimodal Technologies and Interaction, 4(1):7, 2020. doi: 10.3390/mti4010007 2

    work page doi:10.3390/mti4010007 2020

  51. [51]

    Ericson, David Weintrop, and Tovi Grossman

    H. Ryu et al. "you can see the connections": Facilitating visualization of care priorities in people living with multiple chronic health conditions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1–17. ACM, 2023. doi: 10.1145/3544548.3580916 2

    work page doi:10.1145/3544548.3580916 2023

  52. [52]

    Schaad, C

    B. Schaad, C. Bourquin, F. Bornet, T. Currat, M. Saraga, F. Panese et al. Dissatisfaction of hospital patients, their relatives, and friends: anal- ysis of accounts collected in a complaints center.Patient education and counseling, 98(6):771–776, 2015. doi: 10.1016/j.pec.2015.02.019 2

    work page doi:10.1016/j.pec.2015.02.019 2015

  53. [53]

    Segel and J

    E. Segel and J. Heer. Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6):1139– 1148, Nov-Dec 2010. doi: 10.1109/TVCG.2010.179 2

    work page doi:10.1109/tvcg.2010.179 2010

  54. [54]

    Sepehri, L

    K. Sepehri, L. Holsti, S. Niasati, V . Chan, and K. E. Maclean. Beyond the bulging binder: Family-centered design of a digital health informa- tion management system for caregivers of children living with health complexity. InProceedings of the SIGCHI Conference on Human Fac- tors in Computing Systems (CHI), pp. 1–19. Association for Computing Machinery, ...

    work page doi:10.1145/3544548.3581459 2023

  55. [55]

    B. K. Smith, J. Frost, M. Albayrak, and R. Sudhakar. Integrating glu- cometers and digital photography as experience capture tools to enhance patient understanding and communication of diabetes self-management practices.Personal and Ubiquitous Computing, 11(4):273–286, 2007. doi: 10.1007/s00779-006-0087-2 2

    work page doi:10.1007/s00779-006-0087-2 2007

  56. [56]

    Smuck, C

    M. Smuck, C. A. Odonkor, J. K. Wilt, et al. The emerging clinical role of wearables: factors for successful implementation in healthcare.npj Digital Medicine, 4(45), 2021. doi: 10.1038/s41746-021-00418-3 2

    work page doi:10.1038/s41746-021-00418-3 2021

  57. [57]

    M. A. Stewart. Effective physician-patient communication and health outcomes: a review.CMAJ: Canadian Medical Association Journal, 152(9):1423, 1995. 2

    work page 1995

  58. [58]

    R. L. Street Jr, G. Makoul, N. K. Arora, and R. M. Epstein. How does communication heal? pathways linking clinician–patient communication to health outcomes.Patient education and counseling, 74(3):295–301,

    work page

  59. [59]

    doi: 10.1016/j.pec.2008.11.015 2

    work page doi:10.1016/j.pec.2008.11.015 2008

  60. [60]

    Sultanum, F

    N. Sultanum, F. Naeem, M. Brudno, and F. Chevalier. Chartwalk: Navigat- ing large collections of text notes in electronic health records for clinical chart review.IEEE Transactions on Visualization and Computer Graphics, 29(1):1244–1254, 2022. doi: 10.1109/TVCG.2022.3209444 2

    work page doi:10.1109/tvcg.2022.3209444 2022

  61. [61]

    P. N. Valenstein. Formatting pathology reports: applying four design principles to improve communication and patient safety.Archives of Pathology & Laboratory Medicine, 132(1):84–94, 2008. doi: 10.5858/ 2008-132-84-FPRAFD 2

    work page 2008

  62. [62]

    Wongsuphasawat, J

    K. Wongsuphasawat, J. A. Guerra Gómez, C. Plaisant, T. D. Wang, M. Taieb-Maimon, and B. Shneiderman. Lifeflow: Visualizing an overview of event sequences. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1747–1756. ACM, 2011. doi: 10.1145/1978942.1979196 2

    work page doi:10.1145/1978942.1979196 2011

  63. [63]

    Zachariae, C

    R. Zachariae, C. G. Pedersen, A. B. Jensen, E. Ehrnrooth, P. B. Rossen, and H. von der Maase. Association of perceived physician communication style with patient satisfaction, distress, cancer-related self-efficacy, and perceived control over the disease.British Journal of Cancer, 88(5):658– 665, 2003. doi: 10.1038/sj.bjc.6600798 2

    work page doi:10.1038/sj.bjc.6600798 2003

  64. [64]

    H. Zhu, J. Colgan, M. Reddy, and E. K. Choe. Sharing patient-generated data in clinical practices: An interview study. InAMIA Annual Symposium Proceedings, vol. 2016, p. 1303, 2017. 2

    work page 2016

  65. [65]

    K. B. H. Zolnierek and M. R. DiMatteo. Physician communication and patient adherence to treatment: a meta-analysis.Medical care, 47(8):826– 834, 2009. doi: 10.1097/MLR.0b013e31819a5acc 2

    work page doi:10.1097/mlr.0b013e31819a5acc 2009