{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T09:27:09Z","timestamp":1793698029290,"version":"4.5.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Paracelsus Medical University","id":[{"id":"https:\/\/ror.org\/03z3mg085","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Clin Pharm"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Pharmacotherapy optimization in multimorbid patients is increasingly complex due to polypharmacy, fragmented data, expanding electronic health records, and workforce constraints. Conventional clinical decision support systems remain largely rule-based and often fail to adequately incorporate patient-specific context. While artificial intelligence offers new opportunities, stand-alone models remain insufficiently reliable for high-risk pharmacotherapy decision support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To develop a relevance-driven, clinician-supervised hybrid AI framework for pharmacotherapy optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Using a design science-informed approach, an interdisciplinary research group developed a conceptual framework for AI-supported pharmacotherapy optimization. Framework development was informed by prior feasibility work, published literature, clinical practice requirements, and iterative interdisciplinary discussions. Hybrid AI was defined as the combination of retrieval-augmented generation, deterministic safety rules, and large language model reasoning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Seven design principles were identified, including decomposition of clinical activities, relevance-based prioritization, hybrid reasoning under clinician oversight, integration of patient goals, transparency of evidence sources, longitudinal optimization within a governed closed loop, and evaluation as a design requirement. These principles informed a conceptual architecture integrating structured clinical data, patient preferences, longitudinal patient information, and evidence retrieval within a clinician-governed decision-support framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed framework conceptualizes AI as a relevance-structuring, clinician-governed decision-support layer rather than an autonomous decision-maker. By combining hybrid reasoning, patient-specific context, and professional oversight, it provides a conceptual foundation for future development, implementation, and evaluation of AI-supported pharmacotherapy systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11096-026-02205-0","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T06:42:45Z","timestamp":1787294565000},"page":"2279-2288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of a hybrid artificial intelligence framework for pharmacotherapy optimization"],"prefix":"10.1007","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1681-4909","authenticated-orcid":false,"given":"Olaf","family":"Rose","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8788-9329","authenticated-orcid":false,"given":"Stephanie","family":"Clemens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3776-5704","authenticated-orcid":false,"given":"Andreas","family":"Leiherer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"B\u00fccker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Finn","family":"Petersson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald","family":"Lirk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Mosch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanna","family":"Pachmayr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7652-6074","authenticated-orcid":false,"given":"Kreshnik","family":"Hoti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"2205_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhealeco.2021.102442","volume":"77","author":"S Freedman","year":"2021","unstructured":"Freedman S, Golberstein E, Huang T-Y, et al. Docs with their eyes on the clock? The effect of time pressures on primary care productivity. J Health Econ. 2021;77:102442. https:\/\/doi.org\/10.1016\/j.jhealeco.2021.102442.","journal-title":"J Health Econ"},{"key":"2205_CR2","doi-asserted-by":"publisher","first-page":"e230052","DOI":"10.1001\/jamahealthforum.2023.0052","volume":"4","author":"HT Neprash","year":"2023","unstructured":"Neprash HT, Mulcahy JF, Cross DA, et al. Association of primary care visit length with potentially inappropriate prescribing. JAMA Health Forum. 2023;4:e230052. https:\/\/doi.org\/10.1001\/jamahealthforum.2023.0052.","journal-title":"JAMA Health Forum."},{"key":"2205_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2023.100624","volume":"29","author":"KK Ray","year":"2023","unstructured":"Ray KK, Haq I, Bilitou A, et al. Treatment gaps in the implementation of LDL cholesterol control among high- and very high-risk patients in Europe between 2020 and 2021: the multinational observational SANTORINI study. Lancet Reg Health Eur. 2023;29:100624. https:\/\/doi.org\/10.1016\/j.lanepe.2023.100624.","journal-title":"Lancet Reg Health Eur"},{"key":"2205_CR4","doi-asserted-by":"publisher","DOI":"10.1111\/jep.70189","volume":"31","author":"O Olakotan","year":"2025","unstructured":"Olakotan O, Samuriwo R, Ismaila H, et al. Usability challenges in electronic health records: impact on documentation burden and clinical workflow: a scoping review. J Eval Clin Pract. 2025;31:e70189. https:\/\/doi.org\/10.1111\/jep.70189.","journal-title":"J Eval Clin Pract"},{"key":"2205_CR5","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01783-z","volume":"22","author":"G van de Sijpe","year":"2022","unstructured":"van de Sijpe G, Quintens C, Walgraeve K, et al. Overall performance of a drug-drug interaction clinical decision support system: quantitative evaluation and end-user survey. BMC Med Inform Decis Mak. 2022;22:48. https:\/\/doi.org\/10.1186\/s12911-022-01783-z.","journal-title":"BMC Med Inform Decis Mak"},{"key":"2205_CR6","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1136\/amiajnl-2013-001813","volume":"21","author":"KC Nanji","year":"2014","unstructured":"Nanji KC, Slight SP, Seger DL, et al. Overrides of medication-related clinical decision support alerts in outpatients. J Am Med Inform Assoc. 2014;21:487\u201391. https:\/\/doi.org\/10.1136\/amiajnl-2013-001813.","journal-title":"J Am Med Inform Assoc"},{"key":"2205_CR7","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1136\/ejhpharm-2017-001483","volume":"26","author":"H Bell","year":"2019","unstructured":"Bell H, Garfield S, Khosla S, et al. Mixed methods study of medication-related decision support alerts experienced during electronic prescribing for inpatients at an English hospital. Eur J Hosp Pharm. 2019;26:318\u201322. https:\/\/doi.org\/10.1136\/ejhpharm-2017-001483.","journal-title":"Eur J Hosp Pharm"},{"key":"2205_CR8","doi-asserted-by":"publisher","DOI":"10.2196\/40511","volume":"10","author":"H Park","year":"2022","unstructured":"Park H, Chae MK, Jeong W, et al. Appropriateness of alerts and physicians\u2019 responses with a medication-related clinical decision support system: retrospective observational study. JMIR Med Inform. 2022;10:e40511. https:\/\/doi.org\/10.2196\/40511.","journal-title":"JMIR Med Inform"},{"key":"2205_CR9","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1093\/jamia\/ocx115","volume":"25","author":"KC Nanji","year":"2018","unstructured":"Nanji KC, Seger DL, Slight SP, et al. Medication-related clinical decision support alert overrides in inpatients. J Am Med Inform Assoc. 2018;25:476\u201381. https:\/\/doi.org\/10.1093\/jamia\/ocx115.","journal-title":"J Am Med Inform Assoc"},{"key":"2205_CR10","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1001\/jama.293.10.1223","volume":"293","author":"AX Garg","year":"2005","unstructured":"Garg AX, Adhikari NKJ, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293:1223\u201338. https:\/\/doi.org\/10.1001\/jama.293.10.1223.","journal-title":"JAMA"},{"key":"2205_CR11","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1136\/bmj.38398.500764.8F","volume":"330","author":"K Kawamoto","year":"2005","unstructured":"Kawamoto K, Houlihan CA, Balas EA, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330:765. https:\/\/doi.org\/10.1136\/bmj.38398.500764.8F.","journal-title":"BMJ"},{"key":"2205_CR12","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1177\/1715163520929665","volume":"153","author":"O Rose","year":"2020","unstructured":"Rose O, Cheong V-L, Dhaliwall S, et al. Standards in medication review: an international perspective. Can Pharm J Rev Pharm Can. 2020;153:215\u201323. https:\/\/doi.org\/10.1177\/1715163520929665.","journal-title":"Can Pharm J Rev Pharm Can"},{"key":"2205_CR13","doi-asserted-by":"publisher","DOI":"10.1111\/jep.14123","volume":"31","author":"K Riley","year":"2025","unstructured":"Riley K, Yap K, Foley G, et al. Impact of a clinical decision support system on identifying drug-related problems and making recommendations to providers during community pharmacist-led medication reviews in Ontario, Canada: a pilot study. J Eval Clin Pract. 2025;31:e14123. https:\/\/doi.org\/10.1111\/jep.14123.","journal-title":"J Eval Clin Pract"},{"key":"2205_CR14","doi-asserted-by":"crossref","unstructured":"Syrowatka A, Motala A, Lawson E, et al. Computerized Clinical Decision Support To Prevent Medication Errors and Adverse Drug Events: Agency for Healthcare Research and Quality (AHRQ); 2024.","DOI":"10.23970\/AHRQEPC_MHS4MEDERROR"},{"key":"2205_CR15","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-019398","volume":"8","author":"S Erzkamp","year":"2018","unstructured":"Erzkamp S, Rose O. Development and evaluation of an algorithm-based tool for medication management in nursing homes: the AMBER study protocol. BMJ Open. 2018;8:e019398. https:\/\/doi.org\/10.1136\/bmjopen-2017-019398.","journal-title":"BMJ Open"},{"key":"2205_CR16","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1007\/s11096-025-01936-w","volume":"47","author":"MH Elnaem","year":"2025","unstructured":"Elnaem MH, Okuyan B, Mubarak N, et al. Students\u2019 acceptance and use of generative AI in pharmacy education: international cross-sectional survey based on the extended unified theory of acceptance and use of technology. Int J Clin Pharm. 2025;47:1097\u2013108. https:\/\/doi.org\/10.1007\/s11096-025-01936-w.","journal-title":"Int J Clin Pharm"},{"key":"2205_CR17","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1007\/s11096-025-01868-5","volume":"47","author":"K Tecen-Yucel","year":"2025","unstructured":"Tecen-Yucel K, Ozdemir-Ayduran N, Kara E, et al. Intentions of hospital pharmacists to use digital technology in their daily practice: a cross-sectional survey using the Theory of Planned Behaviour. Int J Clin Pharm. 2025;47:1024\u201333. https:\/\/doi.org\/10.1007\/s11096-025-01868-5.","journal-title":"Int J Clin Pharm"},{"key":"2205_CR18","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s11096-025-01982-4","volume":"47","author":"K Hoti","year":"2025","unstructured":"Hoti K, Weidmann AE, Stewart D. Learnings from research on artificial intelligence enabled solutions in clinical pharmacy practice and education. Int J Clin Pharm. 2025;47:917\u201320. https:\/\/doi.org\/10.1007\/s11096-025-01982-4.","journal-title":"Int J Clin Pharm"},{"key":"2205_CR19","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s11096-025-01905-3","volume":"47","author":"WE Bottacin","year":"2025","unstructured":"Bottacin WE, Souza TTde, Reis WCT, et al. Guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services: MedinAI development, validation and statement. Int J Clin Pharm. 2025;47:945\u201356. https:\/\/doi.org\/10.1007\/s11096-025-01905-3.","journal-title":"Int J Clin Pharm"},{"key":"2205_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcsop.2024.100491","volume":"15","author":"M B\u00fccker","year":"2024","unstructured":"B\u00fccker M, Hoti K, Rose O. Artificial intelligence to assist decision-making on pharmacotherapy: A feasibility study. Explor Res Clin Soc Pharm. 2024;15:100491. https:\/\/doi.org\/10.1016\/j.rcsop.2024.100491.","journal-title":"Explor Res Clin Soc Pharm"},{"key":"2205_CR21","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.1038\/s41591-024-03097-1","volume":"30","author":"P Hager","year":"2024","unstructured":"Hager P, Jungmann F, Holland R, et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat Med. 2024;30:2613\u201322. https:\/\/doi.org\/10.1038\/s41591-024-03097-1.","journal-title":"Nat Med"},{"key":"2205_CR22","doi-asserted-by":"publisher","DOI":"10.2196\/59823","volume":"14","author":"D Roustan","year":"2025","unstructured":"Roustan D, Bastardot F. The clinicians\u2019 guide to large language models: a general perspective with a focus on hallucinations. Interact J Med Res. 2025;14:e59823. https:\/\/doi.org\/10.2196\/59823.","journal-title":"Interact J Med Res"},{"key":"2205_CR23","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1007\/s11096-024-01839-2","volume":"47","author":"B De Busser","year":"2025","unstructured":"De Busser B, Roth L, De Loof H. The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy. Int J Clin Pharm. 2025;47:1001\u201310. https:\/\/doi.org\/10.1007\/s11096-024-01839-2.","journal-title":"Int J Clin Pharm"},{"key":"2205_CR24","doi-asserted-by":"publisher","first-page":"45","DOI":"10.2753\/MIS0742-1222240302","volume":"24","author":"K Peffers","year":"2007","unstructured":"Peffers K, Tuunanen T, Rothenberger MA, et al. A design science research methodology for information systems research. J Manag Inf Syst. 2007;24:45\u201377. https:\/\/doi.org\/10.2753\/MIS0742-1222240302.","journal-title":"J Manag Inf Syst"},{"key":"2205_CR25","doi-asserted-by":"publisher","first-page":"337","DOI":"10.25300\/MISQ\/2013\/37.2.01","volume":"37","author":"S Gregor","year":"2013","unstructured":"Gregor S, Hevner AR. Positioning and presenting design science research for maximum impact. MIS Q. 2013;37:337\u201355. https:\/\/doi.org\/10.25300\/MISQ\/2013\/37.2.01.","journal-title":"MIS Q"},{"key":"2205_CR26","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s40264-020-01016-0","volume":"44","author":"S Erzkamp","year":"2021","unstructured":"Erzkamp S, K\u00f6berlein-Neu J, Rose O. An algorithm for comprehensive medication management in nursing homes: results of the AMBER project. Drug Saf. 2021;44:313\u201325. https:\/\/doi.org\/10.1007\/s40264-020-01016-0.","journal-title":"Drug Saf"},{"key":"2205_CR27","doi-asserted-by":"publisher","first-page":"205520762513371","DOI":"10.1177\/20552076251337177","volume":"11","author":"OK Gargari","year":"2025","unstructured":"Gargari OK, Habibi G. Enhancing medical AI with retrieval-augmented generation: A mini narrative review. Digit Health. 2025;11:20552076251337176. https:\/\/doi.org\/10.1177\/20552076251337177.","journal-title":"Digit Health"},{"key":"2205_CR28","unstructured":"Lewis P, Perez E, Piktus A, et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks: arXiv; 2020."},{"key":"2205_CR29","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m3164","volume":"370","author":"X Liu","year":"2020","unstructured":"Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. BMJ. 2020;370:m3164. https:\/\/doi.org\/10.1136\/bmj.m3164.","journal-title":"BMJ"},{"key":"2205_CR30","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. https:\/\/doi.org\/10.1136\/bmj-2023-078378.","journal-title":"BMJ"},{"key":"2205_CR31","doi-asserted-by":"publisher","first-page":"e0000877","DOI":"10.1371\/journal.pdig.0000877","volume":"4","author":"LM Amugongo","year":"2025","unstructured":"Amugongo LM, Mascheroni P, Brooks S, et al. Retrieval augmented generation for large language models in healthcare: A systematic review. PLOS Digit Health. 2025;4:e0000877. https:\/\/doi.org\/10.1371\/journal.pdig.0000877.","journal-title":"PLOS Digit Health."},{"key":"2205_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01332-6","volume":"20","author":"J Amann","year":"2020","unstructured":"Amann J, Blasimme A, Vayena E, et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20:310. https:\/\/doi.org\/10.1186\/s12911-020-01332-6.","journal-title":"BMC Med Inform Decis Mak"},{"key":"2205_CR33","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1001\/jama.2017.7797","volume":"318","author":"F Cabitza","year":"2017","unstructured":"Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318:517\u20138. https:\/\/doi.org\/10.1001\/jama.2017.7797.","journal-title":"JAMA"},{"key":"2205_CR34","doi-asserted-by":"publisher","first-page":"e848","DOI":"10.1016\/S2589-7500(24)00143-2","volume":"6","author":"Y Ning","year":"2024","unstructured":"Ning Y, Teixayavong S, Shang Y, et al. Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist. Lancet Digit Health. 2024;6:e848\u201356. https:\/\/doi.org\/10.1016\/S2589-7500(24)00143-2.","journal-title":"Lancet Digit Health"}],"container-title":["International Journal of Clinical Pharmacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11096-026-02205-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11096-026-02205-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11096-026-02205-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T08:39:33Z","timestamp":1793695173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11096-026-02205-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,21]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2205"],"URL":"https:\/\/doi.org\/10.1007\/s11096-026-02205-0","relation":{},"ISSN":["2210-7711"],"issn-type":[{"value":"2210-7711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,21]]},"assertion":[{"value":"25 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"O.R., S.C., A.L., M.B., G.L., C.M., F.P., J.P., and K.H declare that no conflict of interest exists.","order":1,"name":"Ethics","label":"Competing Interest","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"According to institutional requirements at Paracelsus Medical Private University, formal ethics approval was not required for this conceptual framework development.","order":2,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"During the preparation of this work the authors used Chat GPT 5.2 to improve readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.","order":3,"name":"Ethics","label":"Declaration of generative AI use","group":{"name":"EthicsHeading","label":"Declaration"}}]}}