The Role of Business Intelligence in Cancer Data Management: A Scoping Review

Document Type : Review

Authors

1 Student Research Committee, Faculty of Para-medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran

2 2. Assistant Professor of Medical InformaticsDepartment of Health Information Technology, School of Allied Medical SciencesHormozgan University of Medical Sciences

3 3. Associate Professor of Pediatric Hematology and OncologyDepartment of Pediatrics,School of Medicine Hormozgan University of Medical Sciences

4 Associate Professor of Health Information Management Department of Health Information Technology, School of Allied Medical Sciences Hormozgan University of Medical Sciences

10.30476/jhmi.2024.103708.1236

Abstract

Introduction: Business intelligence is the electronic display of summarized key data in dashboards. Due to the importance of business intelligence in the collection and integration of treatment data of cancer patients and its management, knowledge of business intelligence tools and their characteristics in the field of cancer is most important for managers and health professionals. Therefore, this study aims to explore the application of business intelligence in cancers.
Material and Method: A comprehensive search of major bibliographic databases, including PubMed, Scopus, Web of Science, and Embase, was conducted without applying time restrictions. A total of 24 studies were ultimately selected, and data extraction was performed using an enhanced version of the Arksey and O'Malley framework.
Results: Research has demonstrated that business intelligence plays a crucial role in the management of cancer data. Additionally, the result indicated enhancing patient safety, improving the performance of medical staff in patient monitoring, and supporting decision-makers and administrators in the adoption of optimal resource allocation policies. Furthermore, business intelligence has been applied to monitor the performance of health service departments for cancer patients, ensure effective resource management, and optimize workflow processes within these departments.
Conclusion: Business intelligence is among the most effective and efficient approaches for optimizing the use of large volumes of data, enabling healthcare providers and other decision-makers to access valuable information for making timely decisions in the field of cancer.

Keywords

Main Subjects

References

  1. Janssen A, Donnelly C, Kay J, Thiem P, Saavedra A, Pathmanathan N, et al. Developing an Intranet-Based Lymphedema Dashboard for Breast Cancer Multidisciplinary Teams: Design Research Study. Journal of Medical Internet Research. 2020;22(4): e13188. doi: 10.2196/13188.
  2. Basile LJ, Carbonara N, Pellegrino R, Panniello U. Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making. Technovation. 2023;120(1). doi:org/10.1016/j.technovation.
  3. Pestana M, Pereira R, Moro S. Improving health care management in hospitals through a productivity dashboard. Journal of Medical Systems. 2020;44(4):1-19.
  4. Davy A, Borycki EM. Business Intelligence Dashboards for Patient Safety and Quality: A Narrative Literature Review. Stud Health Technol Inform. 2022;290:438-441.
  5. Karami M, Langarizadeh M, Fatehi M. Evaluation of effective dashboards: key concepts and criteria. The open medical informatics journal. 2017;11(1):52-57.
  6. Khalifa M, Khalid P. Developing strategic health care key performance indicators: a case study on a tertiary care hospital. Procedia Computer Science. 2015;63:459-466.
  7. Almasi S, Rabiei R, Moghaddasi H, Vahidi-Asl M. Emergency department quality dashboard; a systematic review of performance indicators, functionalities, and challenges. Archives of Academic Emergency Medicine. 2021;9(1): e47. doi: 10.22037/aaem.v9i1.1230.
  8. Jebraeily M, Hasanloei MAV, Rahimi B. Design of a management dashboard for the intensive care unit: Determining key performance indicators and their required capabilities. Applied Medical Informatics. 2019;41(3):111-121.
  9. Fischer MJ, Kourany WM, Sovern K, Forrester K, Griffin C, Lightner N, et al. Development, implementation and user experience of the Veterans Health Administration (VHA) dialysis dashboard. BMC nephrology. 2020;21(1):1-12.
  10. Henkel M, Horn T, Leboutte F, Trotsenko P, Dugas SG, Sutter SU, et al. Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening. PLoS One. 2022;17(7): e0271183. doi: 10.1371/journal.pone.0271183.
  11. Walsh AM, Hess J, Rees M, Wetmore C, Vadiya V. Creation of a chemotherapy-induced nausea/vomiting dashboard to improve outcomes for pediatric cancer patients. Support Care Cancer. 2021;29(3):1549-1555.
  12. Soerjomataram I, Bray F. Planning for tomorrow: global cancer incidence and the role of prevention 2020-2070. Nat Rev Clin Oncol. 2021;18(10):663-672.
  13. Hardwick S, Hariparsad S, Kain N, Malata CM. Importance of long-term monitoring of patients with breast reconstructions: a case of 10-year cancer recurrence. Case Reports Plast Surg Hand Surg. 2022;9(1):1-6.
  14. Arksey H, O'malley L. Scoping studies: towards a methodological framework. International journal of social research methodology. 2005;8(1):19-32.
  15. Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. doi.org/10.1186/1748-5908-5-69.
  16. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467-473.
  17. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377-384.
  18. Korakakis V, Whiteley R, Tzavara A, Malliaropoulos N. The effectiveness of extracorporeal shockwave therapy in common lower limb conditions: a systematic review including quantification of patient-rated pain reduction. Br J Sports Med. 2018;52(6):387-407.
  19. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263.
  20. Zarowitz BJ. Emerging pharmacotherapy and health care needs of patients in the age of artificial intelligence and digitalization. Annals of Pharmacotherapy. 2020;54(10):1038-1046.
  21. Perry LM, Morken V, Peipert JD, Yanez B, Garcia SF, Barnard C, et al. Patient-Reported Outcome Dashboards Within the Electronic Health Record to Support Shared Decision-making: Protocol for Co-design and Clinical Evaluation With Patients With Advanced Cancer and Chronic Kidney Disease. JMIR Res Protoc. 2022;11(9): e38461. doi: 10.2196/38461.
  22. Khodaveisi T, Dehdarirad H, Bouraghi H, Mohammadpour A, Sajadi F, Hosseiniravandi M. Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping review. Z Gesundh Wiss. 2023:1-22. doi: 10.1007/s10389-023-01838-z.
  23. Ehn M, Derneborg M, Revenäs Å, Cicchetti A. User-centered requirements engineering to manage the fuzzy front-end of open innovation in e-health: A study on support systems for seniors’ physical activity. International Journal of Medical Informatics. 2021;154:104547.doi: 10.1016/j.ijmedinf.2021.104547.
  24. Teixeira L, Ferreira C, Santos BS. User-centered requirements engineering in health information systems: A study in the hemophilia field. Computer Methods and Programs in Biomedicine. 2012;106(3):160-174.
  25. Rabiei R, Bastani P, Ahmadi H, Dehghan S, Almasi S. Developing public health surveillance dashboards: a scoping review on the design principles. BMC Public Health. 2024;24(1):392.
  26. Li X, Wang H, Chen C, Grundy J. An empirical study on how well do COVID-19 information dashboards service users’ information needs. IEEE Transactions on Services Computing. 2021;15(3):1178-1192.
  27. Hartzler AL, Izard JP, Dalkin BL, Mikles SP, Gore JL. Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. Journal of the American Medical Informatics Association. 2016;23(1):38-47.
  28. Valero-Ramon Z, Fernandez-Llatas C, Collantes G, Valdivieso B, Billis A, Bamidis P, et al. Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress. Frontiers in Oncology. 2023;12 :1043411. doi: 10.3389/fonc.2022.1043411.
  29. Stattin P, Sandin F, Sandbäck T, Damber JE, Lissbrant IF, Robinson D, et al. Dashboard report on performance on select quality indicators to cancer care providers. Scandinavian Journal of Urology. 2016;50(1):21-28.
  30. Izard J, Hartzler A, Avery DI, Shih C, Dalkin BL, Gore JL. User-centered design of quality of life reports for clinical care of patients with prostate cancer. Surgery. 2014;155(5):789-796.
  31. Tsangaris E, Edelen M, Means J, Gregorowitsch M, O'Gorman J, Pattanaik R, et al. User-centered design and agile development of a novel mobile health application and clinician dashboard to support the collection and reporting of patient-reported outcomes for breast cancer care. Bmj Surgery Interventions & Health Technologies. 2022; 4(1):e000119. doi: 10.1136/bmjsit-2021-000119.
  32. Lyatskaya Y, Killoran J, Kukluk J, Cormack R, Quirk S. Improving Management and Compliance of Radiation Therapy Linear Accelerator Quality Assurance Program With Automated Tracking Tools. Adv Radiat Oncol. 2024;9(5):101469. doi: 10.1016/j.adro.2024.101469.
  33. Munbodh R, Roth TM, Leonard KL, Court RC, Shukla U, Andrea S, et al. Real-time analysis and display of quantitative measures to track and improve clinical workflow. Journal of Applied Clinical Medical Physics. 2022;23(9): e13610.doi: 10.1002/acm2.13610. 
  34. Nelson O, Sturgis B, Gilbert K, Henry E, Clegg K, Tan JM, et al. A Visual Analytics Dashboard to Summarize Serial Anesthesia Records in Pediatric Radiation Treatment. Appl Clin Inform. 2019;10(4):563-569.
  35. Ebben KCWJ, de Kroon CD, Schmeink CE, van der Hel OL, van Vegchel T, Moncada-Torres A, et al. A novel method for continuous measurements of clinical practice guideline adherence. Learning Health Systems. 2023;7(4): e10384. doi: 10.1002/lrh2.10384.
  36. Ahmed SYM, Freire SM, Feitosa TMP, Zardo LMG, de Almeida RT. AD-SISCOLO: A decision-support tool to aid the management of a cervical cancer screening program. Res Biomed Eng. 2018;34(1):19-30.
  37. Ecker S, Kirisits C, Schmid M, De Leeuw A, Seppenwoolde Y, Knoth J, et al. Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer. Radiotherapy and Oncology. 2023;182 :109524. doi: 10.1016/j.radonc.2023.109524.
  38. Udalov A, Kumar L, Gaudette AN, Zhang R, Salomao J, Saigal S, et al. Automated Dashboards for the Identification of Pathogenic Circulating Tumor DNA Mutations in Longitudinal Blood Draws of Cancer Patients. Methods and Protocols. 2023;6(3) :46. doi: 10.3390/mps6030046.
  39. Mehdizadeh H, Asadi F, Emami H, Mehrvar A, Nazemi E. An mHealth Self-management System for Support Children With Acute Lymphocytic Leukemia and Their Caregivers: Qualitative Co-design Study. Jmir Formative Research. 2022;6(4): e36721. doi: 10.2196/36721.
  40. Walsh AM, Hess J, Rees M, Wetmore C, Vadiya V. Creation of a chemotherapy-induced nausea/vomiting dashboard to improve outcomes for pediatric cancer patients. Supportive Care in Cancer. 2021;29(3):1549-1555.
  41. Battis B, Clifford L, Huq M, Pejoro E, Mambourg S. The impacts of a pharmacist-managed outpatient clinic and chemotherapy-directed electronic order sets for monitoring oral chemotherapy. Journal of Oncology Pharmacy Practice. 2017;23(8):582-590.
  42. Ahmadi H, Rezazadeh M, Sheikhtaheri A. Developing an information management dashboard for oncology wards. J Health Administration. 2019;22(2):67-85.
  43. Saqlain F, Shalhout SZ, Flaherty KT, Emerick KS, Miller DM. REDCap-Based Operational Tool to Guide Care Coordination in a Multidisciplinary Cutaneous Oncology Clinic. Jco Oncology Practice. 2021;17(9) :527-533. doi: 10.1200/OP.20.00673.
  44. Brown B, Galpin K, Simes J, Boyer M, Brown C, Chin V, et al. Development of clinically meaningful quality indicators for contemporary lung cancer care, and piloting and evaluation in a retrospective cohort; experiences of the Embedding Research (and Evidence) in Cancer Healthcare (EnRICH) Program. Bmj Open. 2024;14(2): e074399. doi: 10.1136/bmjopen-2023-074399.
  45. Strachna O, Cohen MA, Allison MM, Pfister DG, Lee NY, Wong RJ, et al. Case study of the integration of electronic patient-reported outcomes as standard of care in a head and neck oncology practice: Obstacles and opportunities. Cancer. 2021;127(3):359-371.
  46. Watson L, Delure A, Qi S, Link C, Chmielewski L, Photitai É, et al. Utilizing Patient Reported Outcome Measures (PROMs) in ambulatory oncology in Alberta: Digital reporting at the micro, meso and macro level. Journal of Patient-Reported Outcomes. 2021;5(1) :97. doi: 10.1186/s41687-021-00373-3.
  47. Rogal SS, Yakovchenko V, Gonzalez R, Park A, Beste LA, Rozenberg-Ben-Dror K, et al. The Hepatic Innovation Team Collaborative: A Successful Population-Based Approach to Hepatocellular Carcinoma Surveillance. Cancers. 2021;13(9) :2251. doi: 10.3390/cancers13092251.
  48. Smith CEP, Kamal AH, Kluger M, Coke P, Kelley MJ. National Trends in End-of-Life Care for Veterans With Advanced Cancer in the Veterans Health Administration: 2009 to 2016. Journal of Oncology Practice. 2019;15(6): e568-e575. doi: 10.1200/JOP.18.00559. 
Health Management & Information Science
Volume 11, Issue 2
April 2024
Pages 43-55

Files

Share

How to cite

Statistics