Narrative Health Analytics: Integrating Empathy, Data, and Ethics in Patient-Centered Healthcare
DOI:
https://doi.org/10.65166/yxgx8e59Keywords:
narrative medicine, patient narratives, patient experience, clinical discourse analysis, empathy measurement, natural language processing, sentiment analysis, data ethics, healthcare quality improvementAbstract
Data-driven healthcare has expanded the capacity to measure performance, predict risk, and optimize clinical workflows; however, many analytic systems continue to under-represent the meaning-laden dimensions of care captured in patient narratives. This paper develops the Narrative Health Analytics (NHA) Framework, a patient-centered model that integrates narrative interpretation, computational text analytics, and ethical governance to support organizational learning and quality improvement. A qualitative meta-synthesis was conducted using peer-reviewed literature published from 2015–2025, identified through structured searches of Consensus.app, PubMed, Scopus, and Web of Science across five relevant domains: narrative medicine/health humanities, clinical discourse and linguistics, natural language processing and text mining, data ethics and datafication studies, and patient-centered care research. The synthesis yields three integrative outputs: (1) a staged pathway linking narrative inputs to linguistic–emotional feature identification and NLP-based processing, (2) an explicit ethical–cultural mediation layer to reduce bias and interpretive flattening, and (3) a feedback loop that positions narrative-derived indicators as complementary inputs to existing dashboards rather than replacements for established quantitative measures. The framework emphasizes that interpretability and cultural context are necessary conditions for responsible analytics when patient experience is operationalized as data. The paper recommends future empirical testing of the framework across settings and languages, with attention to privacy, fairness, and implementation feasibility.
References
Al-Amer, R., Ramjan, L., Glew, P., Darwish, M., & Salamonson, Y. (2015). Translation of interviews from a source language to a target language: Examining issues in cross-cultural health-care research. Journal of Clinical Nursing, 24(9–10), 1151–1162. https://doi.org/10.1111/jocn.12681
Alkhnbashi, O., Mohammad, R., & Hammoudeh, M. (2024). Aspect-based sentiment analysis of patient feedback using large language models. Big Data and Cognitive Computing, 8(12), 167. https://doi.org/10.3390/bdcc8120167
AlNasser, S., & Almuhaideb, S. (2024). Listening to patients: Advanced Arabic aspect-based sentiment analysis using transformer models towards better healthcare. Big Data and Cognitive Computing, 8(11), 156. https://doi.org/10.3390/bdcc8110156
Bao, D., & Su, W. (2025). Optimizing deep learning-based natural language processing for sentiment analysis. International Journal of High Speed Electronics and Systems. https://doi.org/10.1142/S0129156425403043
Barker, M., Leach, K., & Levett-Jones, T. (2023). Patient’s views of empathic and compassionate healthcare interactions: A scoping review. Nurse Education Today, 131, 105957. https://doi.org/10.1016/j.nedt.2023.105957
Bauer, M., Glenn, T., Monteith, S., Bauer, R., Whybrow, P., & Geddes, J. (2017). Ethical perspectives on recommending digital technology for patients with mental illness. International Journal of Bipolar Disorders, 5(1), 6. https://doi.org/10.1186/s40345-017-0073-9
Berger, S., Saut, A., & Berssaneti, F. (2020). Using patient feedback to drive quality improvement in hospitals: A qualitative study. BMJ Open, 10(3), e037641. https://doi.org/10.1136/bmjopen-2020-037641
Boylan, A., Turk, A., van Velthoven, M., & Powell, J. (2020). Online patient feedback as a measure of quality in primary care: A multimethod study using correlation and qualitative analysis. BMJ Open, 10(2), e031820. https://doi.org/10.1136/bmjopen-2019-031820
Boylan, A., Williams, V., & Powell, J. (2019). Online patient feedback: A scoping review and stakeholder consultation to guide health policy. Journal of Health Services Research & Policy, 25(2), 122–129. https://doi.org/10.1177/1355819619870837
Brookes, G., McEnery, T., McGlashan, M., & Smith, G. (2021). Narrative evaluation in patient feedback. Narrative Inquiry, 31(1), 45–65. https://doi.org/10.1075/ni.20098.bro
Charan, G., Kaur, A., & Kaur, K. (2023). Is digital innovation killing empathy in health care? The realities of patient care in a tech-driven world. Current Medicine Research and Practice, 13(5), 251–252. https://doi.org/10.4103/cmrp.cmrp_72_23
Cipta, D., Andoko, D., Theja, A., Utama, A., Hendrik, H., William, D., … Handoko, M. (2024). Culturally sensitive patient-centered healthcare: A focus on health behavior modification in low and middle-income nations—Insights from Indonesia. Frontiers in Medicine, 11, 1353037. https://doi.org/10.3389/fmed.2024.1353037
Coats, H., Shive, N., Bennett, P., Boyd, M., Doorenbos, A., & Schmiege, S. (2023). Use of a person-centered narrative intervention in an outpatient palliative-care setting: A feasibility study. Journal of Patient Experience, 10, 1–9. https://doi.org/10.1177/23743735231202729
Coats, H., Shive, N., Doorenbos, A., & Schmiege, S. (2020). Integration of person-centered narratives into the electronic health record. Nursing Research, 69(6), 483–489. https://doi.org/10.1097/NNR.0000000000000463
Cordeiro, J. V. (2021). Digital technologies and data science as health enablers: An outline of appealing promises and compelling ethical, legal, and social challenges. Frontiers in Medicine, 8, 647897. https://doi.org/10.3389/fmed.2021.647897
Crawford, T., & Leask, J. (2021). A discourse analysis of health-provider interactions with parents who are reluctant to vaccinate. Patient Education and Counseling. https://doi.org/10.1016/j.pec.2021.08.035
Crawford, T., Roger, P., & Candlin, S. (2018). The consequences of diverse empathic responses in nurse-patient interactions: A discourse analysis. Journal of Communication in Healthcare, 11(2), 87–94. https://doi.org/10.1080/17538068.2018.1453435
Daryazadeh, S., Adibi, P., Yamani, N., & Mollabashi, R. (2020). Impact of a narrative-medicine program on reflective capacity and empathy of medical students in Iran. Journal of Educational Evaluation for Health Professions, 17, 3. https://doi.org/10.3352/jeehp.2020.17.3
De León Languré, A., & Zareei, M. (2024). Evaluating the effect of emotion models on the generalizability of text emotion detection systems. IEEE Access, 12, 70489–70500. https://doi.org/10.1109/ACCESS.2024.3401203
De Rosis, S., Cerasuolo, D., & Nuti, S. (2020). Using patient-reported measures to drive change in healthcare: The experience of the digital, continuous and systematic PREMs observatory in Italy. BMC Health Services Research, 20, 315. https://doi.org/10.1186/s12913-020-05099-4
Degrie, L., Gastmans, C., Mahieu, L., De Casterlé, B. D., & Denier, Y. (2017). How do ethnic-minority patients experience the intercultural care encounter in hospitals? A systematic review of qualitative research. BMC Medical Ethics, 18, 2. https://doi.org/10.1186/s12910-016-0163-8
Denecke, K., & Reichenpfader, D. (2023). Sentiment analysis of clinical narratives: A scoping review. Journal of Biomedical Informatics, 144, 104336. https://doi.org/10.1016/j.jbi.2023.104336
Deshmukh, S., & Pacharaney, U. (2025). Leveraging patient-flow data to reduce hospital readmissions: A study using medical dashboards. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 1662–1665). https://doi.org/10.1109/ICSADL65848.2025.10933252
Dohan, D., Garrett, S. B., Rendle, K. A., Halley, M., & Abramson, C. (2016). The importance of integrating narrative into healthcare decision making. Health Affairs, 35(4), 720–725. https://doi.org/10.1377/hlthaff.2015.1373
Dowding, D., Randell, R., Gardner, P., Fitzpatrick, G., Dykes, P., Favela, J., … Currie, L. (2015). Dashboards for improving patient care: Review of the literature. International Journal of Medical Informatics, 84(2), 87–100. https://doi.org/10.1016/j.ijmedinf.2014.10.001
Drewniak, D., Glässel, A., Hodel, M., & Biller-Andorno, N. (2020). Risks and benefits of web-based patient narratives: Systematic review. Journal of Medical Internet Research, 22(2), e15772. https://doi.org/10.2196/15772
Dunsch, F., Evans, D., Macis, M., & Wang, Q. (2018). Bias in patient satisfaction surveys: A threat to measuring healthcare quality. BMJ Global Health, 3(3), e000694. https://doi.org/10.1136/bmjgh-2017-000694
Eberhardt, S., Schaffrath, J., Moggia, D., Schwartz, B., Jaehde, M., Rubel, J., Baur, T., André, E., & Lutz, W. (2024). Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy. Psychotherapy Research, 35, 174–189. https://doi.org/10.1080/10503307.2024.2322522
Efthymiou, E. (2025). Integrating digital and narrative medicine in modern healthcare: A systematic review. Medical Education Online, 30(1), 2475979. https://doi.org/10.1080/10872981.2025.2475979
Finset, A., & Ørnes, K. (2017). Empathy in the clinician–patient relationship. Journal of Patient Experience, 4(1), 64–68. https://doi.org/10.1177/2374373517699271
Flickinger, T. E., Saha, S., Roter, D., Korthuis, P. T., Sharp, V., Cohn, J., Eggly, S., Moore, R., & Beach, M. C. (2016). Clinician empathy is associated with differences in patient–clinician communication behaviors and higher medication self-efficacy in HIV care. Patient Education and Counseling, 99(2), 220–226. https://doi.org/10.1016/j.pec.2015.09.001
Gaurav, A., Gupta, B., & Chui, K. (2024). BERT-based model for robust mental health analysis in clinical informatics. 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), 153–160. https://doi.org/10.1109/JCSSE61278.2024.10613729
Geyer, C. (2021). Empathy and language in the clinician–patient relationship: Improving the translation of evidence to practice. American Journal of Health Promotion, 35(4), 590–592. https://doi.org/10.1177/08901171211002328b
Gremyr, A., Holmberg, C., Thor, J., Malm, U., Gäre, B. A., & Andersson, A. C. (2022). How a point-of-care dashboard facilitates co-production of health and care for individuals with psychotic disorders: A mixed-methods case study. BMC Health Services Research, 22, 1045. https://doi.org/10.1186/s12913-022-08992-2
Grob, R., Schlesinger, M., Barre, L. R., Bardach, N., Lagu, T., Shaller, D., … Palimaru, A. (2019). What words convey: The potential for patient narratives to inform quality improvement. The Milbank Quarterly, 97(1), 176–227. https://doi.org/10.1111/1468-0009.12374
Guo, J. (2022). Deep learning approach to text analysis for human emotion detection from big data. Journal of Intelligent Systems, 31(1), 113–126. https://doi.org/10.1515/jisys-2022-0001
Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2022). More than a feeling: Accuracy and application of sentiment analysis. International Journal of Research in Marketing, 39(3), 697–715. https://doi.org/10.1016/j.ijresmar.2022.05.005
Hassett, M., Cronin, C., Cronin, A., Uno, H., Paudel, R., Osarogiagbon, R., Wong, S., Dizon, D., Bian, J., Hazard-Jenkins, H., Brooks, G., & Schrag, D. (2024). The relationship between patient-reported experience and patient-reported outcomes in patients with cancer. JCO Oncology Practice, 20(10 Suppl), 248. https://doi.org/10.1200/op.2024.20.10_suppl.248
Healy, M., Richard, A., & Kidia, K. (2022). How to reduce stigma and bias in clinical communication: A narrative review. Journal of General Internal Medicine, 37(10), 2533–2540. https://doi.org/10.1007/s11606-022-07609-y
Holmes, S. M. (2023). Learning language, un/learning empathy in medical school. Culture, Medicine and Psychiatry, 49(1), 40–64. https://doi.org/10.1007/s11013-023-09830-8
Jacennik, B., Zawadzka-Gosk, E., Moreira, J., & Glinkowski, W. (2022). Evaluating patients’ experiences with healthcare services: Extracting domain- and language-specific information from free-text narratives. International Journal of Environmental Research and Public Health, 19(16), 10182. https://doi.org/10.3390/ijerph191610182
James, T., Calderon, E., & Cook, D. (2017). Exploring patient perceptions of healthcare service quality through analysis of unstructured feedback. Expert Systems with Applications, 71, 479–492. https://doi.org/10.1016/j.eswa.2016.11.004
Janerka, C., Leslie, G., & Gill, F. (2023). Development of patient-centred care in acute hospital settings: A meta-narrative review. International Journal of Nursing Studies, 140, 104465. https://doi.org/10.1016/j.ijnurstu.2023.104465
Kemp, J., Zhang, T., Inglis, F., Wiljer, D., Sockalingam, S., Crawford, A., Lo, B., Charow, R., Munnery, M., Takhar, S., & Strudwick, G. (2020). Delivery of compassionate mental health care in a digital technology-driven age: Scoping review. Journal of Medical Internet Research, 22(3), e16263. https://doi.org/10.2196/16263
Kerasidou, A. (2020). Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bulletin of the World Health Organization, 98(4), 245–250. https://doi.org/10.2471/BLT.19.237198
Kokab, S., Asghar, S., & Naz, S. (2022). Transformer-based deep learning models for the sentiment analysis of social media data. Array, 14, 100157. https://doi.org/10.1016/j.array.2022.100157
Kumar, M. (2024). Emotion recognition in natural language processing: Understanding how AI interprets the emotional tone of text. Journal of Artificial Intelligence & Cloud Computing, 6(3). https://doi.org/10.47363/jaicc/2024(3)e238
Kwame, A. (2024). Achieving universal healthcare coverage in a multilingual care setting: Linguistic diversity and language use barriers as social determinants of care in Ghana. Qualitative Health Research. https://doi.org/10.1177/10497323241298886
Lamprell, K., Pulido, D., Arnolda, G., Easpaig, B., Tran, Y., & Braithwaite, J. (2024). From stories to solutions: A research-cycle framework for enhancing trustworthiness in studies of online patient narratives. Journal of Medical Internet Research, 27, e58310. https://doi.org/10.2196/58310
Lerzynski, G. (2021). Ethical implications of digitalization in healthcare. In Responsible Innovation in Digital Health (pp. 163–170). Springer. https://doi.org/10.1007/978-3-030-65896-0_14
Li, P., & Miao, X. (2025). Exploring the role of language choice: A catalyst for transforming research dynamics and relationships. Qualitative Health Research, 35(3), 462–475. https://doi.org/10.1177/10497323251315444
Locock, L., Kirkpatrick, S., Brading, L., Sturmey, G., Cornwell, J., Churchill, N., & Robert, G. (2019). Involving service users in the qualitative analysis of patient narratives to support healthcare quality improvement. Research Involvement and Engagement, 5, 1–12. https://doi.org/10.1186/s40900-018-0133-z
Lorié, Á., Reinero, D., Phillips, M., Zhang, L., & Riess, H. (2017). Culture and nonverbal expressions of empathy in clinical settings: A systematic review. Patient Education and Counseling, 100(3), 411–424. https://doi.org/10.1016/j.pec.2016.09.018
Luo, M., Warren, C., Cheng, L., Abdul-Muhsin, H., & Banerjee, I. (2024). Assessing empathy in large language models with real-world physician–patient interactions. 2024 IEEE International Conference on Big Data (BigData) (pp. 6510–6519). https://doi.org/10.1109/BigData62323.2024.10825307
Maeckelberghe, E., Zdunek, K., Marceglia, S., Farsides, B., & Rigby, M. (2023). The ethical challenges of personalized digital health. Frontiers in Medicine, 10, 1123863. https://doi.org/10.3389/fmed.2023.1123863
Maher, N. A., Senders, J. T., Hulsbergen, A. F. C., Lamba, N., Parker, M., Onnela, J.-P., Bredenoord, A. L., Smith, T. R., & Broekman, M. L. D. (2019). Passive data collection and use in healthcare: A systematic review of ethical issues. International Journal of Medical Informatics, 129, 242–247. https://doi.org/10.1016/j.ijmedinf.2019.06.015
Marshall, L., Vargas, L., Eltantawy, N., & Cruz, G. (2023). Do you feel safe at home? A critical discourse analysis of tools used in medical settings to screen for intimate partner violence. Journal of Family Violence. https://doi.org/10.1007/s10896-023-00527-5
Martínez-Angulo, P., Rich-Ruiz, M., Ventura-Puertos, P., & López-Quero, S. (2023). Analysing power relations among older Norwegian patients and Spanish migrant nurses in home-nursing care: A critical discourse-analysis approach from a transcultural perspective. Healthcare, 11(9), 1282. https://doi.org/10.3390/healthcare11091282
Méndez, L., Bitetti, D., & Perry, J. (2023). A cross-cultural perspective of narrative retells in kindergarten children. Bilingual Research Journal, 46(3), 275–289. https://doi.org/10.1080/15235882.2023.2258838
Navarro, S., Ochoa, C., Chan, E., Du, S., & Farias, A. (2021). Will improvements in patient experience with care impact clinical and quality-of-care outcomes? Medical Care, 59(9), 843–856. https://doi.org/10.1097/MLR.0000000000001598
Nawab, K., Ramsey, G., & Schreiber, R. (2020). Natural language processing to extract meaningful information from patient experience feedback. Applied Clinical Informatics, 11(2), 242–252. https://doi.org/10.1055/s-0040-1708049
Nebeker, C., Torous, J., & Ellis, R. J. (2019). Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Medicine, 17(1), 137. https://doi.org/10.1186/s12916-019-1377-7
Nembhard, I., Matta, S., Shaller, D., Grob, R., & Schlesinger, M. (2023). Learning from patients: The impact of using patient narratives on patient-experience scores. Academy of Management Proceedings, 2023(1), 225bp. https://doi.org/10.5465/amproc.2023.225bp
Nguyen, A., Trinh, X., Wang, S., & Wu, A. (2020). Determination of patient sentiment and emotion in ophthalmology: Infoveillance tutorial on web-based health-forum discussions. Journal of Medical Internet Research, 23(5), e20803. https://doi.org/10.2196/20803
Núñez, A., Del Carmen Santiago Díaz, M., Vázquez, A., Marcial, J., & Linares, G. (2024). Emotion detection using natural language processing. International Journal of Combinatorial Optimization Problems and Informatics, 15(5), 108–114. https://doi.org/10.61467/2007.1558.2024.v15i5.564
Olszewski, A., Bogetz, J., Mercer, A., Bradford, M., Scott, M., Fields, B., Williams, K., Rosenberg, A., & Trowbridge, A. (2023). Empathy expression in interpreted and non-interpreted care conferences of seriously ill children. Pediatrics, 151(2), e2022059447. https://doi.org/10.1542/peds.2022-059447
Osváth, M., Yang, Z., & Kósa, K. (2023). Analyzing narratives of patient experiences: A BERT topic-modeling approach. Acta Polytechnica Hungarica, 20(7), 155–174. https://doi.org/10.12700/APH.20.7.2023.7.9
Oudin, A., Maatoug, R., Bourla, A., Ferreri, F., Bonnot, O., Millet, B., Schoeller, F., Mouchabac, S., & Adrien, V. (2022). Digital phenotyping: Data-driven psychiatry to redefine mental health. Journal of Medical Internet Research, 25, e44502. https://doi.org/10.2196/44502
Park, M., Giap, T.-T.-T., Lee, M., Jeong, H., Jeong, M., & Go, Y. (2018). Patient- and family-centred care interventions for improving the quality of healthcare: A review of systematic reviews. International Journal of Nursing Studies, 87, 69–83. https://doi.org/10.1016/j.ijnurstu.2018.07.006
Peeters, B., & Marini, M. (2018). Narrative medicine across languages and cultures: Using Minimal English for increased comparability of patients’ narratives. In [Edited volume title unavailable] (pp. 259–286). https://doi.org/10.1007/978-3-319-62512-6_11
Popa, E. O., Van Hilten, M., Oosterkamp, E., & Bogaardt, M.-J. (2021). The use of digital twins in healthcare: Socio-ethical benefits and risks. Life Sciences, Society and Policy, 17(1), 6. https://doi.org/10.1186/s40504-021-00113-x
Price, W. N., II, & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43. https://doi.org/10.1038/s41591-018-0272-7
Roberts, B., Puri, N., Trzeciak, C., Mazzarelli, A., & Trzeciak, S. (2020). Socioeconomic, racial, and ethnic differences in patient experience of clinician empathy: Results of a systematic review and meta-analysis. PLoS ONE, 16(2), e0247259. https://doi.org/10.1371/journal.pone.0247259
Rolland-Lozachmeur, G. (2022). La relation patient/soignant: Sur le discours des « individus encagés ». Annals of the University of Craiova, Series Philology Linguistics, 44(1-2), 73. https://doi.org/10.52846/aucssflingv.v44i1-2.73
Rubeis, G. (2022). iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interventions, 28, 100518. https://doi.org/10.1016/j.invent.2022.100518
Salman, A., Kim, A., Mercado, A., Ring, D., Doornberg, J., Fatehi, A., & Crijns, T. (2021). Are patient linguistic tones associated with mental health and perceived clinician empathy? The Journal of Bone and Joint Surgery, 103(24), 2181–2189. https://doi.org/10.2106/JBJS.21.00124
Shah, A., Yan, X., Shah, S., & Mamirkulova, G. (2019). Mining patient opinion to evaluate service quality in healthcare: A deep-learning approach. Journal of Ambient Intelligence and Humanized Computing, 11(7), 2925–2942. https://doi.org/10.1007/s12652-019-01434-8
Singh, D., Barve, S., & Dwivedi, A. (2025). OptiASAR: Optimized aspect-based sentiment analysis of reviews with BiLSTM-GRU and NER-BERT in healthcare decision-making. IEEE Access, 13, 47459–47474. https://doi.org/10.1109/ACCESS.2025.3549303
Squires, A., Gerchow, L., C., C., Liang, E., Trachtenberg, M., & Miner, S. (2023). A multi-language qualitative study of limited English proficiency patient experiences in the United States. PEC Innovation, 2, 100177. https://doi.org/10.1016/j.pecinn.2023.100177
Stokes, D., Kishton, R., McCalpin, H., Pelullo, A., Meisel, Z., Beidas, R., & Merchant, R. (2021). Online reviews of mental-health treatment facilities: Narrative themes associated with positive and negative ratings. Psychiatric Services, 72(11), 1199–1206. https://doi.org/10.1176/appi.ps.202000267
Sugiyama, A., Utsunomiya, K., Fujimoto, K., & Bando, H. (2025). Clinical utility of integrated multidisciplinary patient-centered information in breast-cancer care: A mixed-methods study. Journal of Multidisciplinary Healthcare, 18, 1875–1893. https://doi.org/10.2147/JMDH.S506292
Sze, K. Y., Lim, Y. W., Fong, Q. H., De Roza, J., Lee, P. S., Lee, E. Y., & Tan, S. B. (2025). Digital health’s impact on the patient–doctor relationship in a primary healthcare setting: A qualitative study. Annals of the Academy of Medicine, Singapore, 54(2), 129–131. https://doi.org/10.47102/annals-acadmedsg.2024341
Tan, K., Lee, C., Anbananthen, K., & Lim, K. (2022). RoBERTa-LSTM: A hybrid model for sentiment analysis with transformer and recurrent neural network. IEEE Access, 10, 21517–21525. https://doi.org/10.1109/ACCESS.2022.3152828
Tanana, M., Soma, C., Kuo, P., Bertagnolli, N., Dembe, A., Pace, B., Srikumar, V., Atkins, D., & Imel, Z. (2021). How do you feel? Using natural language processing to automatically rate emotion in psychotherapy. Behavior Research Methods, 53(5), 2069–2082. https://doi.org/10.3758/s13428-020-01531-z
Tapuria, A., Porat, T., Kalra, D., Dsouza, G., Sun, X., & Curcin, V. (2021). Impact of patient access to their electronic health record: Systematic review. Informatics for Health and Social Care, 46(3), 194–206. https://doi.org/10.1080/17538157.2021.1879810
Terry, C., & Cain, J. (2016). The emerging issue of digital empathy. American Journal of Pharmaceutical Education, 80(4), 58. https://doi.org/10.5688/ajpe80458
Van Baar, R. (2022). Sentiment analysis for Dutch medical texts (Undergraduate thesis, University of Amsterdam).
Van De Baan, F., Lambregts, S., Bergman, E., Most, J., & Westra, D. (2023). Involving health professionals in the development of quality and safety dashboards: Qualitative study. Journal of Medical Internet Research, 25, e42649. https://doi.org/10.2196/42649
Vestgarden, L., Dahlborg, E., Strunck, J., & Aasen, E. (2023). Nurses’ discursive construction of older-adult immigrant patients in hospitals. BMC Health Services Research, 23, 59590. https://doi.org/10.1186/s12913-023-09590-6
Vignot, M., Jung, C., Bathaei, S., Lazzati, A., Gateau, V., Angeli, F., & Delorenzo, C. (2024). Patient experience in bariatric surgery: Protocol of a French narrative inquiry and qualitative analysis. BMJ Open, 14(2), e082528. https://doi.org/10.1136/bmjopen-2023-082528
Wang, Y., Kung, L., Gupta, S., & Ozdemir, S. (2019). Leveraging big-data analytics to improve quality of care in healthcare organizations: A configurational perspective. British Journal of Management, 30(2), 362–388. https://doi.org/10.1111/1467-8551.12332
Weggelaar-Jansen, A. M. J. W. M., Broekharst, D. S., & de Bruijne, M. C. (2018). Developing a hospital-wide quality and safety dashboard: A qualitative research study. BMJ Quality & Safety, 27(12), 1000–1007. https://doi.org/10.1136/bmjqs-2018-007784
Wolff, A. C., Dresselhuis, A., Hejazi, S., Dixon, D., Gibson, D., Howard, A., … Edwards, L. (2021). Healthcare provider characteristics that influence implementation of PROM/PREM data: Protocol for a mixed-methods review with narrative synthesis. Systematic Reviews, 10, 156. https://doi.org/10.1186/s13643-021-01725-2
Wong, E. L. Y., Mavondo, F., & Fisher, J. (2020). Patient feedback to improve quality of patient-centred care in public hospitals: A systematic review. BMC Health Services Research, 20, 530. https://doi.org/10.1186/s12913-020-05383-3
Wu, Y. (2021). Empathy in nurse–patient interaction: A conversation analysis. BMC Nursing, 20(1), 145. https://doi.org/10.1186/s12912-021-00535-0
Zakkar, M., & Lizotte, D. (2021). Analyzing patient stories on social media using text analytics. Journal of Healthcare Informatics Research, 5(4), 382–400. https://doi.org/10.1007/s41666-021-00097-5
Downloads
Published
Versions
- 2025-12-28 (3)
- 2025-12-28 (2)
- 2025-12-25 (1)
Issue
Section
License
Copyright (c) 2025 International Journal of Health & Business Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors retain copyright of their articles but grant the International Journal of Health & Business Analytics (IJHBA Press) the right of first publication.
