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Health Informatics and Patient Safety in Pharmacotherapy

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Health Information Science (HIS 2023)

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Abstract

Medication management is an essential part of patient safety. Problems related to prescribing, polypharmacy, and the interaction effects of drugs increase morbidity, mortality, and healthcare costs worldwide. Patients’ knowledge of drug treatment could be improved in areas of the most common drug interactions and the general risks caused by drugs. The safety and effectiveness of medication for the elderly requires more effort especially in the area of high-risk medicines. Artificial intelligence–based decision support systems present new approaches for improving patient safety and medication management. However, before the full benefits of assistive technologies can be realized, these tools require proper validation.

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References

  1. Elliott, R.A., Camacho, E., Jankovic, D., Sculpher, M.J., Faria, R.: Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Qual. Saf. 30, 96–105 (2021)

    Article  Google Scholar 

  2. Chowdhary, R., Roshi, Tandon, V.: Role of free web based software in evaluating the profile of drug-drug interactions. J. Cardiovasc. Dis. 13, 399–404 (2022)

    Google Scholar 

  3. Erstad, B.L., Romero, A.V., Barletta, J.F.: Weight and size descriptors for drug dosing: too many options and too many errors. Am. J. Health Syst. Pharm. 80, 87–91 (2023)

    Article  Google Scholar 

  4. Tariq, R.A., Vashisht, R., Sinha, A., Scherbak, Y.: Medication dispensing errors and prevention. StatPearls, Treasure Island, FL, USA (2020)

    Google Scholar 

  5. Rodziewicz, T.L., Hipskind, J.E.: Medical error prevention. StatPearls [Internet]. Treasure Island (FL), StatPearls Publishing (2020)

    Google Scholar 

  6. Elliott, R., Camacho, E.M., Gavan, S., Keers, R., Chuter, A.: Estimating the impact of enabling NHS information systems to share patients’ medicines information digitally. University of Manchester (reports). (2023)

    Google Scholar 

  7. Hole, G., Hole, A.S., McFalone-Shaw, I.: Digitalization in pharmaceutical industry: what to focus on under the digital implementation process? Int. J. Pharm. X. 3, 100095 (2021)

    Google Scholar 

  8. Basile, A.O., Yahi, A., Tatonetti, N.P.: Artificial intelligence for drug toxicity and safety. Trends Pharmacol. Sci. 40, 624–635 (2019)

    Article  Google Scholar 

  9. Kaelber, D.C., Bates, D.W.: Health information exchange and patient safety. J. Biomed. Inform. 40, S40–S45 (2007)

    Article  Google Scholar 

  10. Panagioti, M., et al.: Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ 366, 14185 (2019)

    Google Scholar 

  11. Khalili, M., Mesgarpour, B., Sharifi, H., Daneshvar Dehnavi, S., Haghdoost, A.A.: Interventions to improve adverse drug reaction reporting: a scoping review. Pharmacoepidemiol. Drug Saf. 29, 965–992 (2020)

    Article  Google Scholar 

  12. Ikonen, T., Welling, M.: Parempaa potilasturvallisuutta. Lääkärilehti 75, 1211–1219 (2020)

    Google Scholar 

  13. Organisation: using routinely collected data to inform pharmaceutical policies – OECD. https://www.oecd.org/health/health-systems/routinely-collected-data-to-inform-pharmaceutical-policies.htm. Accessed 09 June 2023

  14. Ogundipe, A., Sim, T.F., Emmerton, L.: Development of an evaluation framework for health information communication technology in contemporary pharmacy practice. Exploratory Res. Clin. Soc. Pharm. 9, 100252 (2023)

    Google Scholar 

  15. Mustonen, P., Ikonen, T., Rauhala, A., Leskelä, R.-L., Virkki, M.: Potilas- ja asiakasturvallisuudelle uusi kansallinen mittaristo 76, 2892–3289 (2021)

    Google Scholar 

  16. Alqenae, F.A., Steinke, D., Keers, R.N.: Prevalence and nature of medication errors and medication-related harm following discharge from hospital to community settings: a systematic review. Drug Saf. 43, 517–537 (2020)

    Article  Google Scholar 

  17. Kopanz, J., et al.: Burden of risks in the analogue and digitally-supported medication use process and potential for solutions to increase patient safety in the hospital: a mixed method study (2022). https://doi.org/10.21203/rs.3.rs-1593296/v1

  18. Burgener, A.M.: Enhancing communication to improve patient safety and to increase patient satisfaction. Health News 39, 128–132 (2020)

    Google Scholar 

  19. Organization, W.H.: Ethics and governance of artificial intelligence for health: WHO guidance. (2021)

    Google Scholar 

  20. Leskur, D., et al.: Adverse drug reaction reporting via mobile applications: a narrative review. Int. J. Med. Inform. 168, 104895 (2022)

    Google Scholar 

  21. Taylor, K., May, E., Powell, D., Ronte, H.: Deloitte insights, Intelligent post-launch patient support, Enhancing patient safety with AI. https://www2.deloitte.com/mt/en/pages/about-deloitte/topics/deloitte-insights.html. Accessed 07 June 2023

  22. Hubbard, R.E., O’Mahony, M.S., Woodhouse, K.W.: Medication prescribing in frail older people. Eur. J. Clin. Pharmacol. 69, 319–326 (2013)

    Article  Google Scholar 

  23. Surale-Patil, A., Salve, P., Singh, L., Shah, A., Shinde, A.: Prevention of drug interaction in geriatric patients. Eur. Chem. Bulletin. 12, 3355–3363 (2023)

    Google Scholar 

  24. Christopher, C., et al.: Medication use problems among older adults at a primary care: a narrative of literature review. Aging Med. 5, 126–137 (2022)

    Article  Google Scholar 

  25. Organization, W.H.: Medication safety in polypharmacy: Technical report. World Health Organization (2019)

    Google Scholar 

  26. Yaghi, G., Chahine, B.: Potentially inappropriate medications use in a psychiatric elderly care hospital: a cross-sectional study using beers criteria. Health Sci. Rep. 6, e1247 (2023)

    Article  Google Scholar 

  27. Ersoy, S., Engin, V.: Accessibility to healthcare and risk of polypharmacy on chronically ill patients. JCPSP-J. Coll. Phys. Surg. Pak. 29 (2019)

    Google Scholar 

  28. Rachamin, Y., et al.: Prescription rates, polypharmacy and prescriber variability in Swiss general practice—a cross-sectional database study. Front. Pharmacol. 13, 832994 (2022)

    Article  Google Scholar 

  29. Roberto, K.A., Teaster, P.B., Lindberg, B.W., Blancato, R.: A first (and disturbing) look at the relationship between the opioid epidemic and elder abuse: insights of human service professionals. J. Appl. Gerontol. 40, 1231–1235 (2021)

    Article  Google Scholar 

  30. Nieciecka, A., et al.: Addictions in the elderly–review article. J. Health Study Med. 22, 43–67 (2022). https://doi.org/10.36145/JHSM2022.10

  31. ELDesoky, E.S.: Pharmacokinetic-pharmacodynamic crisis in the elderly. Am. J .Ther. 14, 488–498 (2007)

    Article  Google Scholar 

  32. Wilder-Smith, O.H.: Opioid use in the elderly. Eur. J. Pain 9, 137–140 (2005)

    Article  Google Scholar 

  33. Pergolizzi, J., et al.: Opioids and the management of chronic severe pain in the elderly: consensus statement of an international expert panel. (Buprenorphine, fentanyl, hydromorphone, methadone, morphine, oxycodone). Pain Pract. 8, 287–313 (2008)

    Google Scholar 

  34. Rissanen, M.: Comprehending translational design scenarios and implications in consumer health informatics. Doctoral Dissertation, Aalto University (2021)

    Google Scholar 

  35. Schofield, P.: The assessment of pain in older people: UK national guidelines. Age Ageing 47, i1–i22 (2018)

    Article  Google Scholar 

  36. Johansson, M.M., et al.: Pain characteristics and quality of life in older people at high risk of future hospitalization. Int. J. Env. Res. Public Health 18, 958 (2021)

    Google Scholar 

  37. Bates, D.W., et al.: The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit. Med. 4, 54 (2021)

    Article  Google Scholar 

  38. Jarab, A.S., Al-Qerem, W., Mukattash, T.L.: Information technology in pharmacy practice: barriers and utilization. J. Appl. Pharm. Sci. 13, 150–155 (2023)

    Google Scholar 

  39. Raza, M.A., et al.: Artificial intelligence (AI) in pharmacy: an overview of innovations. Innov. Pharm. 13, 13–13 (2022)

    Article  Google Scholar 

  40. Bhagat, P.M.: Artificial Intelligence in Healthcare. IJSRET 7, 796–800 (2021)

    Google Scholar 

  41. Singh, A.V., et al.: Integrative toxicogenomics: advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed. Pharmacother. 163, 114784 (2023)

    Article  Google Scholar 

  42. Liu, J., Wang, Y., Huang, L., Zhang, C., Zhao, S.: Identifying adverse drug reaction-related text from social media: a multi-view active learning approach with various document representations. Information 13, 189 (2022)

    Article  Google Scholar 

  43. KafiKang, M., Hendawi, A.: Drug-Drug interaction extraction from biomedical text using relation BioBERT with BLSTM. Mach. Learn. Knowl. Extr. 5, 669–683 (2023)

    Article  Google Scholar 

  44. Tabi, K., et al.: Mobile apps for medication management: review and analysis. JMIR Mhealth Uhealth 7, e13608 (2019)

    Article  Google Scholar 

  45. Ross, M.: What’s the Importance of Medication Education for Patients? https://blog.cureatr.com/the-importance-of-medication-education-for-patients. Accessed 23 June 2023

  46. MyRXprofile It Could Save Your Life (2023). https://www.myrxprofile.com/

  47. Fauque, E.J.A.: Évaluation de l’information et du conseil pharmaceutique numérique existant en dermatologie. Analyse critique et proposition d’un nouvel outil (2020). https://dumas.ccsd.cnrs.fr/dumas-02970039

  48. Vaghefi, I., Tulu, B.: The continued use of mobile health apps: insights from a longitudinal study. JMIR Mhealth Uhealth 7(8), e12983 (2019)

    Article  Google Scholar 

  49. Portenhauser, A.A., Terhorst, Y., Schultchen, D., Sander, L.B., Denkinger, M.D., Stach, M., et al.: Mobile apps for older adults: systematic search and evaluation within online stores. JMIR Aging 4, e23313 (2021)

    Article  Google Scholar 

  50. Fahamin, Ali, R., Lipi, I.A.: Medication Alert: A fore-and-aft Android-Based Hospitality Corps Sturdy for Progressive Repeated Medication Alert System. SSRN 4460501 (Elsevier) (2023). https://doi.org/10.2139/ssrn.4460501

  51. Romero-Jimenez, R., et al.: Design and implementation of a mobile app for the pharmacotherapeutic follow-up of patients diagnosed with immune-mediated inflammatory diseases: eMidCare. Front. Immunol. 13, 915578 (2022)

    Article  Google Scholar 

  52. Al Kuwaiti, A., et al.: A review of the role of artificial intelligence in healthcare. J. Personal. Med. 13, 951 (2023)

    Article  Google Scholar 

  53. Spargo, M., et al.: Shaping the future of digitally enabled health and care. Pharmacy. 9, 17 (2021)

    Article  Google Scholar 

  54. Fainzang, S.: Managing medicinal risks in self-medication. Drug Saf. 37, 333–342 (2014)

    Article  Google Scholar 

  55. Gudala, M., Ross, M.E.T., Mogalla, S., Lyons, M., Ramaswamy, P., Roberts, K.: Benefits of, barriers to, and needs for an artificial intelligence–powered medication information voice chatbot for older adults: interview study with geriatrics experts. JMIR Aging 5, e32169 (2022)

    Article  Google Scholar 

  56. Islam, A.R., et al.: An artificial intelligence-based smartphone app for assessing the risk of opioid misuse in working populations using synthetic data: pilot development study. JMIR Formative Res. 7, e45434 (2023)

    Article  Google Scholar 

  57. Williams, L.: 11 Healthcare Chatbots Which Can Improve Patient Experience. https://getreferralmd.com/2019/03/11-healthcare-chatbots-that-improve-patient-experience/. Accessed 09 June 2023

  58. Lee, D.: AI-based healthcare chatbot. Int. Res. J. Eng. Technol. 10, 563–568 (2023)

    Google Scholar 

  59. Parmar, P., Ryu, J., Pandya, S., Sedoc, J., Agarwal, S.: Health-focused conversational agents in person-centered care: a review of apps. NPJ Digit. Med. 5, 21 (2022)

    Article  Google Scholar 

  60. Zhang, Y., Pei, H., Zhen, S., Li, Q., Liang, F.: Chat Generative pre-trained transformer (ChatGPT) usage in healthcare – science direct. Gastroenterol. Endosc. 1, 139–143 (2023). https://doi.org/10.1016/j.gande.2023.07.002

    Article  Google Scholar 

  61. Juhi, A., et al.: The capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus. 15, 1–7 (2023)

    Google Scholar 

  62. Morath, B., et al.: Performance and risks of ChatGPT used in drug information: an exploratory real-world analysis. Eur. J. Hosp. Pharm. (2023)

    Google Scholar 

  63. Khairat, S., Marc, D., Crosby, W., Al Sanousi, A.: Reasons for physicians not adopting clinical decision support systems: critical analysis. JMIR Med. Inform. 6, e8912 (2018)

    Article  Google Scholar 

  64. Choudhury, A., Asan, O.: Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med. Inform. 8, e18599 (2020)

    Article  Google Scholar 

  65. Kamel Boulos, M.N., Zhang, P.: Digital twins: From personalised medicine to precision public health. J. Personal. Med. 11, 745 (2021)

    Article  Google Scholar 

  66. Fürstenau, D., Gersch, M., Schreiter, S.: Digital therapeutics (DTx). Bus. Inf. Syst. Eng. 65, 1–12 (2023)

    Google Scholar 

  67. Wang, C., Lee, C., Shin, H.: Digital therapeutics from bench to bedside. NPJ Digit. Med. 6, 38 (2023)

    Google Scholar 

  68. Chiang, S., Rao, V.R.: Choosing the best antiseizure medication—can artificial intelligence help? JAMA Neurol. 79, 970–972 (2022)

    Article  Google Scholar 

  69. MacMath, D., Chen, M., Khoury, P.: Artificial intelligence: exploring the future of innovation in allergy immunology. Curr. Allergy Asthma Rep. 23, 1–12 (2023)

    Google Scholar 

  70. Akyon, S.H., Akyon, F.C., Yılmaz, T.E.: Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients. Front. Med. 10, 1029198 (2023)

    Article  Google Scholar 

  71. Lisbona, N.: How artificial intelligence is matching drugs to patients - BBC News, https://www.bbc.com/news/business-65260592. Accessed 17 08 2023

  72. Rissanen, M.: Ways for enhancing the substance in consumer-targeted eHealth. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds.) HIS 2019. LNCS, vol. 11837, pp. 306–317. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32962-4_28

    Chapter  Google Scholar 

  73. Smith, I.P., et al.: The impact of video-based educational materials with voiceovers on preferences for glucose monitoring technology in patients with diabetes: a randomised study. Patient-Patient-Centered Outcomes Res. 1–15 (2023). https://doi.org/10.1007/s40271-022-00612-9

  74. HealthITSecurity, S.: Leveraging Artificial Intelligence to Support Medication Adherence. https://healthitanalytics.com/features/leveraging-artificial-intelligence-to-support-medication-adherence. Accessed 17 Aug 2023

  75. Merchant, S.: Stanford’s AIMI is Revolutionizing Healthcare AI by Providing Free Big Data to Researchers – AIM. https://www.aimblog.io/2021/09/02/stanfords-aimi-is-revolutionizing-healthcare-ai-by-providing-free-big-data-to-researchers/. Accessed 17 Aug 2023

  76. Alghadier, M., Kusuma, K., Manjunatha, D., Kabra, P., Zaleha, M.: A study of various applications of artificial intelligence (AI) and machine learning (ML) for healthcare services. Technology 5(1), 87–94 (2023)

    Google Scholar 

  77. Hevner, A., Chatterjee, S.: Design science research in information systems. In: Design research in information systems. Integrated Series in Information Systems, vol. 22, pp. 9–22. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-5653-8_2

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Rissanen, A., Rissanen, M. (2023). Health Informatics and Patient Safety in Pharmacotherapy. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_31

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  • DOI: https://doi.org/10.1007/978-981-99-7108-4_31

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