Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Pharmaceutical Design
  4. Volume 30, Issue 34
  5. Article
Volume 30, Issue 34
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128321623240719104337
2024-07-31
2025-01-07

  • From This Site

    /content/journals/cpd/10.2174/0113816128321623240719104337
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. BerryL.L. BendapudiN. Health Care.J. Serv. Res.200710211112210.1177/1094670507306682
     [Google Scholar]
  2. HauxR. Medical informatics: Past, present, future.Int. J. Med. Inform.201079959961010.1016/j.ijmedinf.2010.06.00320615752
     [Google Scholar]
  3. SuryadevaraC.K. Towards personalized healthcare-an intelligent medication recommendation system.IEJRD-Int. Multidiscipl. J.20205916
     [Google Scholar]
  4. RodriguesM.A. SilvaV.J. de LucenaV.F. An intelligent medication system designed to improve the medication adherence. 2015 IEEE 5th International Conference on Consumer Electronics, Berlin (ICCE-Berlin), Berlin, Germany, 2015, pp. 46-49.10.1109/ICCE‑Berlin.2015.7391310
     [Google Scholar]
  5. GranthamG. McMillanV. DunnS.V. GassnerL.A. WoodcockP. Patient self-medication a change in hospital practice.J. Clin. Nurs.200615896297010.1111/j.1365‑2702.2006.01398.x16879540
     [Google Scholar]
  6. GimenesF.R. MiassoA.I. de LyraD.P.Junior GrouC.R. Electronic prescription as contributing factor for hospitalized patients’ safety.Pharm. Pract.2006411317
     [Google Scholar]
  7. ZulligL.L. PetersonE.D. BosworthH.B. Ingredients of successful interventions to improve medication adherence.JAMA2013310242611261210.1001/jama.2013.28281824264605
     [Google Scholar]
  8. SteinhublS.R. MuseE.D. TopolE.J. Can mobile health technologies transform health care?JAMA2013310222395239610.1001/jama.2013.28107824158428
     [Google Scholar]
  9. SubramanianM. WojtusciszynA. FavreL. BoughorbelS. ShanJ. LetaiefK.B. PitteloudN. ChouchaneL. Precision medicine in the era of artificial intelligence: Implications in chronic disease management.J. Transl. Med.202018147210.1186/s12967‑020‑02658‑533298113
     [Google Scholar]
  10. ChoudhuryA. AsanO. Role of artificial intelligence in patient safety outcomes: Systematic literature review.JMIR Med. Inform.202087e1859910.2196/1859932706688
     [Google Scholar]
  11. ChinnasamyP. WongW.K. RajaA.A. KhalafO.I. KiranA. BabuJ.C. Health recommendation system using deep learning-based collaborative filtering.Heliyon2023912e2284410.1016/j.heliyon.2023.e2284438144343
     [Google Scholar]
  12. GavanS.P. ThompsonA.J. PayneK. The economic case for precision medicine.Expert Rev. Precis. Med. Drug Dev.2018311910.1080/23808993.2018.142185829682615
     [Google Scholar]
  13. ChalasaniS.H. SyedJ. RameshM. PatilV. Pramod KumarT.M. Artificial intelligence in the field of pharmacy practice: A literature review.Explor. Res. Clin. Soc. Pharm.20231210034610.1016/j.rcsop.2023.10034637885437
     [Google Scholar]
  14. WangX. GuM. GaoX. XiongX. WangN. LiQ. GeM. LuoM. ZhangY. HuaX. ShiC. Application of information-intelligence technologies in pharmacy intravenous admixture services in a Chinese third-class a hospital.BMC Health Serv. Res.2022221123810.1186/s12913‑022‑08580‑436207708
     [Google Scholar]
  15. DickinsonH. TeltschD.Y. FeifelJ. HuntP. Vallejo-YagüeE. VirkudA.V. MuylleK.M. OchiT. DonneyongM. ZabinskiJ. StraussV.Y. Hincapie-CastilloJ.M. The Unseen Hand: AI-based prescribing decision support tools and the evaluation of drug safety and effectiveness.Drug Saf.202447211712310.1007/s40264‑023‑01376‑338019365
     [Google Scholar]
  16. SamadbeikM. AhmadiM. Hosseini AsanjanS.M. A theoretical approach to electronic prescription system: Lesson learned from literature review.Iran. Red Crescent Med. J.20131510e843610.5812/ircmj.843624693376
     [Google Scholar]
  17. SigeristH.E. A history of medicine.Oxford University Press19872120
     [Google Scholar]
  18. CrawfordM.J. GabrielJ.M. Drugs on the Page: Pharmacopoeias and Healing Knowledge in the Early Modern Atlantic World.University of Pittsburgh Press2019133610.2307/j.ctvh4zdn2
     [Google Scholar]
  19. JacksonM. A global history of medicine.Oxford University Press2018
     [Google Scholar]
  20. QureshiN. Al-DossariD. Al-ZaagiI. Al-BedahA. AbudalliA. KoenigH. Electronic health records, electronic prescribing and medication errors: A systematic review of literature, 2000-2014.Br. J. Med. Med. Res.20155567270410.9734/BJMMR/2015/13490
     [Google Scholar]
  21. MelchiorreM.G. PapaR. QuattriniS. LamuraG. BarbabellaF. Integrated care programs for people with multimorbidity in European countries: eHealth adoption in health systems.BioMed Res. Int.202020201902532610.1155/2020/902532632337283
     [Google Scholar]
  22. AmjadA. KordelP. FernandesG. A review on innovation in healthcare sector (telehealth) through artificial intelligence.Sustainability2023158665510.3390/su15086655
     [Google Scholar]
  23. TariqM.U. Revolutionizing health data management with blockchain technology: Enhancing security and efficiency in a digital era.Emerging Technologies for Health Literacy and Medical PracticeIGI Global2024153175
     [Google Scholar]
  24. JariwalaK.S. HolmesE.R. BanahanB.F.III McCaffreyD.J.III Adoption of and experience with e-prescribing by primary care physicians.Res. Social Adm. Pharm.20139112012810.1016/j.sapharm.2012.04.00322695213
     [Google Scholar]
  25. HahnA. LovettA. Electronic prescribing: An examination of cost effectiveness, clinician adoption and limitations.Univ. J. Clin. Med.20142112410.13189/ujcm.2014.020101
     [Google Scholar]
  26. FiggeH.L. FoxB.I. TribbleD.A. Electronic prescribing of controlled substances.Am. J. Health Syst. Pharm.200966141311131610.2146/ajhp08059719574608
     [Google Scholar]
  27. RezaF. PrietoJ. T. JulienS. P. Electronic health records: Origination, adoption, and progression.Public Health Informatics and Information Systems . Health Informatics.SpringerCham202031610.1007/978‑3‑030‑41215‑9_11
     [Google Scholar]
  28. PatelV. JamoomE. HsiaoC.J. FurukawaM.F. BuntinM. Variation in electronic health record adoption and readiness for meaningful use: 2008-2011.J. Gen. Intern. Med.201328795796410.1007/s11606‑012‑2324‑x23371416
     [Google Scholar]
  29. WebsterL. SpiroR.F. Health information technology: A new world for pharmacy.J. Am. Pharm. Assoc.2010502e20e3410.1331/JAPhA.2010.0917020199946
     [Google Scholar]
  30. GjiniE. WertheimerA.I. Review of drug quality and security act of 2013: the drug supply chain security act (DSCSA).Innov. Pharm.20167310.24926/iip.v7i3.462
     [Google Scholar]
  31. PreussC. V. KalavaA. KingK. C. Prescription of controlled substances: benefits and risks.StatPearls PublishingTreasure Island (FL)2019
     [Google Scholar]
  32. OmboniS. PadwalR. S. AlessaT. BenczúrB. GreenB. B. HubbardI. WangJ. The worldwide impact of telemedicine during COVID-19: Current evidence and recommendations for the future.Connect Health20221735
     [Google Scholar]
  33. KaushalR. BatesD.W. Information technology and medication safety: What is the benefit?Qual. Saf. Health Care200211326126510.1136/qhc.11.3.26112486992
     [Google Scholar]
  34. Sinthiya S, Udayadhara A, Gomathy B. Effects of electronic prescription on the clinical practice. Automation and Autonomous Systems. Ischolar 2016; 8(2): 34-6.
  35. IshizukaH. HoriguchiM. WakiY. MaedaM. IshikuraC. Computerized dispensing system: Reducing the time of dispensing medicines.Int. J. Biomed. Comput.1991281-213714610.1016/0020‑7101(91)90033‑B1889903
     [Google Scholar]
  36. AlrabadiN. ShawagfehS. HaddadR. MukattashT. AbuhammadS. Al-rabadiD. Abu FarhaR. AlRabadiS. Al-FaouriI. Medication errors: A focus on nursing practice.J. Pharm. Health Serv. Res.2021121788610.1093/jphsr/rmaa025
     [Google Scholar]
  37. LesarT.S. BricelandL. SteinD.S. Factors related to errors in medication prescribing.JAMA1997277431231710.1001/jama.1997.035402800500339002494
     [Google Scholar]
  38. JungreithmayrV. MeidA.D. BittmannJ. FabianM. KleinU. KuglerS. LöpprichM. ReinhardO. ScholzL. ZeehB. BitzW. BugajT. KihmL. KopfS. LiemannA. WagenlechnerP. ZemvaJ. BenkertC. MerleC. RomanS. WelteS. HaefeliW.E. SeidlingH.M. The impact of a computerized physician order entry system implementation on 20 different criteria of medication documentation-a before-and-after study.BMC Med. Inform. Decis. Mak.202121127910.1186/s12911‑021‑01607‑634635100
     [Google Scholar]
  39. OgnibeneP.J. Smart Pharmacy Cards to automate patient records for patient-reported outcomes (PROs) pective drug utilization review. Proceedings of the Annual Symposium on Computer Application in Medical Care. American Medical Informatics Association 1991; p. 906.
     [Google Scholar]
  40. LalaB. NaherS. MahmoodM.A. HoqueM.M. Development of a smart medical prescription service model. 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), Dhaka, Bangladesh, 2018, pp. 369-374.10.1109/CEEICT.2018.8628071
     [Google Scholar]
  41. MoreiraM.W.L. RodriguesJ.J.P.C. KorotaevV. Al-MuhtadiJ. KumarN. A comprehensive review on smart decision support systems for health care.IEEE Syst. J.20191333536354510.1109/JSYST.2018.2890121
     [Google Scholar]
  42. ShemeikkaT. Bastholm-RahmnerP. ElinderC.G. VégA. TörnqvistE. CorneliusB. KorkmazS. A health record integrated clinical decision support system to support prescriptions of pharmaceutical drugs in patients with reduced renal function: Design, development and proof of concept.Int. J. Med. Inform.201584638739510.1016/j.ijmedinf.2015.02.00525765963
     [Google Scholar]
  43. JohnsonK.B. HoY.X. CalaC.M. DavisonC. Showing your work: Impact of annotating electronic prescriptions with decision support results.J. Biomed. Inform.201043232132510.1016/j.jbi.2009.11.00819995617
     [Google Scholar]
  44. LakaM. MilazzoA. MerlinT. Sustainable implementation of electronic decision support tools for the evidence-based management of antibiotics. DPH2019: Proceedings of the 9th International Conference on Digital Public Health 2019; 7-8. 10.1145/3357729.3357732
     [Google Scholar]
  45. PontefractS.K. ColemanJ.J. VallanceH.K. HirschC.A. ShahS. MarriottJ.F. RedwoodS. The impact of computerised physician order entry and clinical decision support on pharmacist-physician communication in the hospital setting: A qualitative study.PLoS One20181311e020745010.1371/journal.pone.020745030444894
     [Google Scholar]
  46. CornyJ. RajkumarA. MartinO. DodeX. LajonchèreJ.P. BilluartO. BézieY. BuronfosseA. A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.J. Am. Med. Inform. Assoc.202027111688169410.1093/jamia/ocaa15432984901
     [Google Scholar]
  47. GreenesRA Features of computer-based clinical decision support.Clin. Deci. Supp.20077910710.1016/B978‑012369377‑8/50004‑0
     [Google Scholar]
  48. SigeristH.E. A history of medicine.Oxford University Press19872120
     [Google Scholar]
  49. CrawfordM.J. GabrielJ.M. Drugs on the Page: Pharmacopoeias and Healing Knowledge in the Early Modern Atlantic World.University of Pittsburgh Press201910.2307/j.ctvh4zdn2
     [Google Scholar]
  50. JacksonM. A global history of medicine.Oxford University Press2018
     [Google Scholar]
  51. ShahidA.H. KhattakW.A. Improving patient care with machine learning: A game-changer for healthcare.Appl. Res. Artif. Intell. Cloud. Comp.202251150163
     [Google Scholar]
  52. ScheifeR.T. HinesL.E. BoyceR.D. ChungS.P. MomperJ.D. SommerC.D. AbernethyD.R. HornJ.R. SklarS.J. WongS.K. JonesG. BrownM.L. GrizzleA.J. ComesS. WilkinsT.L. BorstC. WittieM.A. MaloneD.C. Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.Drug Saf.201538219720610.1007/s40264‑014‑0262‑825556085
     [Google Scholar]
  53. GaikwadR.K. Comprehensive Evaluation of Two Electronic Medical Record (EMR) Systems in Primary Health Care for Accuracy of Drug Interaction Alerts.Doctoral dissertation, Dalhousie University2005
     [Google Scholar]
  54. TantrayJ. ZaidM. KoseyS. Pharmacovigilance: A meta-analysis on ADRS of past and recent tragedy occurred in Gambia.J. Pharmacovigil.2023111409
     [Google Scholar]
  55. van der SijsH. AartsJ. VultoA. BergM. Overriding of drug safety alerts in computerized physician order entry.J. Am. Med. Inform. Assoc.200613213814710.1197/jamia.M180916357358
     [Google Scholar]
  56. HelmonsP.J. SuijkerbuijkB.O. Nannan PandayP.V. KosterinkJ.G.W. Drug-drug interaction checking assisted by clinical decision support: A return on investment analysis.J. Am. Med. Inform. Assoc.201522476477210.1093/jamia/ocu01025670751
     [Google Scholar]
  57. HahnM. RollS.C. The influence of pharmacogenetics on the clinical relevance of pharmacokinetic drug-drug interactions: Drug- gene, drug-gene-gene and drug-drug-gene interactions.Pharmaceuticals202114548710.3390/ph1405048734065361
     [Google Scholar]
  58. MeiS. ZhangK. A machine learning framework for predicting drug-drug interactions.Sci. Rep.20211111761910.1038/s41598‑021‑97193‑834475500
     [Google Scholar]
  59. PreussC. V. KalavaA. KingK. C. Prescription of controlled substances: benefits and risks.Treasure Island (FL)StatPearls Publishing2019
     [Google Scholar]
  60. AntmanE.M. LoscalzoJ. Precision medicine in cardiology.Nat. Rev. Cardiol.2016131059160210.1038/nrcardio.2016.10127356875
     [Google Scholar]
  61. MirnezamiR. NicholsonJ. DarziA. Preparing for precision medicine.N. Engl. J. Med.2012366648949110.1056/NEJMp111486622256780
     [Google Scholar]
  62. NevesL.S. RodriguesM.T. ReisR.L. GomesM.E. Current approaches and future perspectives on strategies for the development of personalized tissue engineering therapies.Expert Rev. Precis. Med. Drug Dev.2016119310810.1080/23808993.2016.1140004
     [Google Scholar]
  63. CollinsD.C. SundarR. LimJ.S.J. YapT.A. Towards precision medicine in the clinic: From biomarker discovery to novel therapeutics.Trends Pharmacol. Sci.2017381254010.1016/j.tips.2016.10.01227871777
     [Google Scholar]
  64. CarrV.L. SangiorgiD. BüscherM. JungingerS. CooperR. Integrating evidence-based design and experience-based approaches in healthcare service design.HERD201144123310.1177/19375867110040040321960190
     [Google Scholar]
  65. ClaridgeJ.A. FabianT.C. History and development of evidence-based medicine.World J. Surg.200529554755310.1007/s00268‑005‑7910‑115827845
     [Google Scholar]
  66. ZimermanA.L. Evidence-based medicine: A short history of a modern medical movement.Virtual Mentor2013151717623356811
     [Google Scholar]
  67. SackettD.L. RosenbergW.M.C. GrayJ A M. HaynesR.B. RichardsonW.S. Evidence based medicine: what it is and what it isn’t.BMJ19963127023717210.1136/bmj.312.7023.718555924
     [Google Scholar]
  68. GrayR.A. PathmanathanP. Patient-specific cardiovascular computational modeling: Diversity of personalization and challenges.J. Cardiovasc. Transl. Res.2018112808810.1007/s12265‑018‑9792‑229512059
     [Google Scholar]
  69. KirchhofP. SipidoK.R. CowieM.R. EschenhagenT. FoxK.A.A. KatusH. SchroederS. SchunkertH. PrioriS. AlonsoA. ChezaubernardC. DoevendansP. EschenhagenT. FoxK. KatusH. KhderY. KirchhofP. KramerF. KristensenS. Maitland-Van der ZeeA-H. Oertelt-PrigioneS. PintoF. PocockS. PrioriS.G. SartoriusA. SchottD. SchroederS. SchunkertH. SchwabM. SipidoK. SvenssonA. SwedbergK. WallentinL. WeimersM. HerttualaS.Y. The continuum of personalized cardiovascular medicine: A position paper of the European Society of Cardiology.Eur. Heart J.201435463250325710.1093/eurheartj/ehu31225148837
     [Google Scholar]
  70. FullerJ. FloresL.J. Translating trial results in clinical practice: The risk GP model.J. Cardiovasc. Transl. Res.20169316716810.1007/s12265‑016‑9694‑027146316
     [Google Scholar]
  71. NordslettenD.A. NiedererS.A. NashM.P. HunterP.J. SmithN.P. Coupling multi-physics models to cardiac mechanics.Prog. Biophys. Mol. Biol.20111041-3778810.1016/j.pbiomolbio.2009.11.00119917304
     [Google Scholar]
  72. VorpD.A. Biomechanics of abdominal aortic aneurysm.J. Biomech.20074091887190210.1016/j.jbiomech.2006.09.00317254589
     [Google Scholar]
  73. TaylorC.A. FonteT.A. MinJ.K. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: Scientific basis.J. Am. Coll. Cardiol.201361222233224110.1016/j.jacc.2012.11.08323562923
     [Google Scholar]
  74. VottaE. LeT.B. StevanellaM. FusiniL. CaianiE.G. RedaelliA. SotiropoulosF. Toward patient-specific simulations of cardiac valves: State-of-the-art and future directions.J. Biomech.201346221722810.1016/j.jbiomech.2012.10.02623174421
     [Google Scholar]
  75. PittaccioS. MigliavaccaF. DubiniG. KoçyildirimE. de LevalM.R. On the use of computational models for the quantitative assessment of surgery in congenital heart disease.Anadolu Kardiyol. Derg.20055320220916140652
     [Google Scholar]
  76. SmithN. de VecchiA. McCormickM. NordslettenD. CamaraO. FrangiA.F. DelingetteH. SermesantM. RelanJ. AyacheN. KruegerM.W. SchulzeW.H.W. HoseR. ValverdeI. BeerbaumP. StaicuC. SiebesM. SpaanJ. HunterP. WeeseJ. LehmannH. ChapelleD. RezaviR. euHeart: Personalized and integrated cardiac care using patient-specific cardiovascular modelling.Interface Focus20111334936410.1098/rsfs.2010.004822670205
     [Google Scholar]
  77. GaleottiL. van DamP.M. LoringZ. ChanD. StraussD.G. Evaluating strict and conventional left bundle branch block criteria using electrocardiographic simulations.Europace201315121816182110.1093/europace/eut13223703366
     [Google Scholar]
  78. Aguado-SierraJ. KrishnamurthyA. VillongcoC. ChuangJ. HowardE. GonzalesM.J. OmensJ. KrummenD.E. NarayanS. KerckhoffsR.C.P. McCullochA.D. Patient-specific modeling of dyssynchronous heart failure: A case study.Prog. Biophys. Mol. Biol.2011107114715510.1016/j.pbiomolbio.2011.06.01421763714
     [Google Scholar]
  79. ChangS. E-prescribing reduces prescription errors 700 percent.2010Available from: http://ihealthbulletin.com/blog/ 2010/02/26/e-prescribing-reducesprescription-errors-700-percent/
  80. HoughtonJ. Information technology and the revolution in healthcare.Digital Medicine. Health Informatics.SpringerCham200217
     [Google Scholar]
  81. EmuoyibofarheO.J. OmotoshoA. Development of a secure intelligent e-prescription system.Proceedings of The International eHealth, Telemedicine and Health ICT Forum for Education, Networking and Business (Med. Tel 2012) Conference2012261272
     [Google Scholar]
  82. AmmenwerthE. Schnell-InderstP. MachanC. SiebertU. The effect of electronic prescribing on medication errors and adverse drug events: A systematic review.J. Am. Med. Inform. Assoc.200815558560010.1197/jamia.M266718579832
     [Google Scholar]
  83. FranklinB.D. O’GradyK. DonyaiP. JacklinA. BarberN. The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: A before-and-after study.Qual. Saf. Health Care200716427928410.1136/qshc.2006.01949717693676
     [Google Scholar]
  84. LtK. To err is human: building a safer health system.Washington (DC)National Academies Press (US)2000
     [Google Scholar]
  85. HoonhoutL.H.F. de BruijneM.C. WagnerC. ZegersM. WaaijmanR. SpreeuwenbergP. AsschemanH. van der WalG. van TulderM.W. Direct medical costs of adverse events in Dutch hospitals.BMC Health Serv. Res.2009912710.1186/1472‑6963‑9‑2719203365
     [Google Scholar]
  86. ThakkarJ. KurupR. LabaT.L. SantoK. ThiagalingamA. RodgersA. WoodwardM. RedfernJ. ChowC.K. Mobile telephone text messaging for medication adherence in chronic disease: A meta-analysis.JAMA Intern. Med.2016176334034910.1001/jamainternmed.2015.766726831740
     [Google Scholar]
  87. PengY. WangH. FangQ. XieL. ShuL. SunW. LiuQ. Effectiveness of mobile applications on medication adherence in adults with chronic diseases: A systematic review and meta-analysis.J. Manag. Care Spec. Pharm.202026455056110.18553/jmcp.2020.26.4.55032223596
     [Google Scholar]
  88. HuckvaleK. AdomaviciuteS. PrietoJ.T. LeowM.K.S. CarJ. Smartphone apps for calculating insulin dose: A systematic assessment.BMC Med.201513110610.1186/s12916‑015‑0314‑725943590
     [Google Scholar]
  89. HuckvaleK. MorrisonC. OuyangJ. GhaghdaA. CarJ. The evolution of mobile apps for asthma: An updated systematic assessment of content and tools.BMC Med.20151315810.1186/s12916‑015‑0303‑x25857569
     [Google Scholar]
  90. HuckvaleK. CarM. MorrisonC. CarJ. Apps for asthma self-management: A systematic assessment of content and tools.BMC Med.201210114410.1186/1741‑7015‑10‑14423171675
     [Google Scholar]
  91. KnoerS.J. EckA.R. LucasA.J. A review of American pharmacy: education, training, technology, and practice.J. Pharm. Health Care Sci.2016213210.1186/s40780‑016‑0066‑327843574
     [Google Scholar]
  92. McKibbonKA LokkerC HandlerSM Enabling medication management through health information technology.Evid Rep Technol Assess.20112011951
     [Google Scholar]
  93. PevnickJ.M. AschS.M. AdamsJ.L. MattkeS. PatelM.H. EttnerS.L. BellD.S. Adoption and use of stand-alone electronic prescribing in a health plan-sponsored initiative.Am. J. Manag. Care201016318218920225913
     [Google Scholar]
  94. SarkarU. LylesC.R. ParkerM.M. AllenJ. NguyenR. MoffetH.H. SchillingerD. KarterA.J. Use of the refill function through an online patient portal is associated with improved adherence to statins in an integrated health system.Med. Care201452319420110.1097/MLR.000000000000006924374412
     [Google Scholar]
  95. KneuertzP.J. Moffatt-BruceS.D. Search for meaningful use of patient-reported outcomes in thoracic surgery.Ann. Thorac. Surg.202010951317131810.1016/j.athoracsur.2019.09.03931697907
     [Google Scholar]
  96. SingerE.S. MerrittR.E. D’SouzaD.M. Moffatt-BruceS.D. KneuertzP.J. Patient satisfaction after lung cancer surgery: Do clinical outcomes affect hospital consumer assessment of health care providers and systems scores?Ann. Thorac. Surg.201910861656166310.1016/j.athoracsur.2019.06.08031430461
     [Google Scholar]
  97. ArmstrongK.A. SempleJ.L. CoyteP.C. Replacing ambulatory surgical follow-up visits with mobile app home monitoring: Modeling cost-effective scenarios.J. Med. Internet Res.2014169e21310.2196/jmir.352825245774
     [Google Scholar]
  98. KneuertzP.J. JagadeshN. PerkinsA. FitzgeraldM. Moffatt-BruceS.D. MerrittR.E. D’SouzaD.M. Improving patient engagement, adherence, and satisfaction in lung cancer surgery with implementation of a mobile device platform for patient reported outcomes.J. Thorac. Dis.202012116883689110.21037/jtd.2020.01.2333282391
     [Google Scholar]
  99. OyebodeF. Clinical errors and medical negligence.Med. Princ. Pract.201322432333310.1159/00034629623343656
     [Google Scholar]
  100. HeloS. MoultonC.A.E. Complications: Acknowledging, managing, and coping with human error.Transl. Androl. Urol.20176477378210.21037/tau.2017.06.2828904910
     [Google Scholar]
  101. RobertsonJ.J. LongB. Suffering in silence: Medical error and its impact on health care providers.J. Emerg. Med.201854440240910.1016/j.jemermed.2017.12.00129366616
     [Google Scholar]
  102. BattardJ. Nonpunitive response to errors fosters a just culture.Nurs. Manage.2017481535510.1097/01.NUMA.0000511184.95547.b328033214
     [Google Scholar]
  103. Al-NomayN.S. AshiA. Al-HarganA. AlshalhoubA. MasuadiE. Attitudes of dental professional staff and auxiliaries in Riyadh, Saudi Arabia, toward disclosure of medical errors.Saudi Dent. J.2017292596510.1016/j.sdentj.2017.01.00328490844
     [Google Scholar]
  104. WilliamsL. What is the ethical course of action for a dentist whose patient’s previous dentist may have treated the wrong tooth?J. Am. Dent. Assoc.2012143891791810.14219/jada.archive.2012.029822855908
     [Google Scholar]
  105. GhazalL. SaleemZ. AmlaniG. A medical error: To disclose or not to disclose.J. Clin. Res. Bioeth.201451
     [Google Scholar]
  106. LedermanR. DreyfusS. MatchanJ. KnottJ.C. MiltonS.K. Electronic error-reporting systems: A case study into the impact on nurse reporting of medical errors.Nurs. Outlook2013616417426.e510.1016/j.outlook.2013.04.00823838568
     [Google Scholar]
  107. Al MutairA. Al MutairiA. ChaglaH. AlawamK. AlsalmanK. AliA. Examining and adapting the psychometric properties of the Maslach burnout inventory-health services survey (MBI-HSS) among healthcare professionals.Appl. Sci.2020105189010.3390/app10051890
     [Google Scholar]
  108. MayoA.M. DuncanD. Nurse perceptions of medication errors: What we need to know for patient safety.J. Nurs. Care Qual.200419320921710.1097/00001786‑200407000‑0000715326990
     [Google Scholar]
  109. EvansS.M. BerryJ.G. SmithB.J. EstermanA. SelimP. O’ShaughnessyJ. DeWitM. Attitudes and barriers to incident reporting: A collaborative hospital study.Qual. Saf. Health Care2006151394310.1136/qshc.2004.01255916456208
     [Google Scholar]
  110. EadieA. Medical error reporting should it be mandatory in Scotland?J. Forensic Leg. Med.201219743744110.1016/j.jflm.2012.04.00722920772
     [Google Scholar]
  111. RichardsonJ. McKieJ. Increasing the options for reducing adverse events: Results from a modified Delphi technique.Aust. New Zealand Health Policy2008512510.1186/1743‑8462‑5‑2519014562
     [Google Scholar]
  112. Department of HealthAn Organisation with a Memory: Report of an Expert Group on Learning from Adverse Events in the NHS Chaired by the Chief Medical Officer.London, UKHM Stationery Office2000
     [Google Scholar]
  113. StavropoulouC. DohertyC. ToseyP. How effective are incident- reporting systems for improving patient safety? A systematic literature review.Milbank Q.201593482686610.1111/1468‑0009.1216626626987
     [Google Scholar]
  114. KunacD.L. TatleyM.V. Detecting medication errors in the New Zealand pharmacovigilance database: A retrospective analysis.Drug Saf.2011341597110.2165/11539290‑000000000‑0000021142271
     [Google Scholar]
  115. KaplanH.S. CallumJ.L. FastmanB.R. MerkleyL.L. The medical event reporting system for transfusion medicine: Will it help get the right blood to the right patient?Transfus. Med. Rev.20021628610210.1053/tmrv.2002.3145911941572
     [Google Scholar]
  116. SantellJ.P. HicksR.W. McMeekinJ. CousinsD.D. Medication errors: experience of the United States Pharmacopeia (USP) MEDMARX reporting system.J. Clin. Pharmacol.200343776076710.1177/009127000325483112856391
     [Google Scholar]
  117. BillingsC.E. Some hopes and concerns regarding medical event-reporting systems: Lessons from the NASA Aviation Safety Reporting System.Arch. Pathol. Lab. Med.199812232142159823857
     [Google Scholar]
  118. National Aeronautics and Space Administration (NASA) the Patient Safety Reporting System (PSRS).Available from: https://psrs.arc.nasa.gov/ (accessed on 12 July 2020).
  119. Institute for Safe Medication Practices (ISMP) Report an Error.Available from: https://www.ismp.org/report-medication-error (accessed on 12 July 2020).
  120. The Academy of Managed Care Pharmacy’s Concepts in Managed Care Pharmacy, Medication Errors.Available from: https://www.amcp.org/sites/default/files/2019-03/Medication%20Errors.pdf (accessed on 26 November 2019).
  121. FortescueE.B. KaushalR. LandriganC.P. McKennaK.J. ClappM.D. FedericoF. GoldmannD.A. BatesD.W. Prioritizing strategies for preventing medication errors and adverse drug events in pediatric inpatients.Pediatrics2003111472272910.1542/peds.111.4.72212671103
     [Google Scholar]
  122. EldenN.M.K. IsmailA. The importance of medication errors reporting in improving the quality of clinical care services.Glob. J. Health Sci.20158824310.5539/gjhs.v8n8p24327045415
     [Google Scholar]
  123. GinsburgG. McCarthyJ.J. Personalized medicine: revolutionizing drug discovery and patient care.Trends Biotechnol.2001191249149610.1016/S0167‑7799(01)01814‑511711191
     [Google Scholar]
  124. HamburgM.A. CollinsF.S. The path to personalized medicine.N. Engl. J. Med.2010363430130410.1056/NEJMp100630420551152
     [Google Scholar]
  125. LiuY. LvX. XieN. FangZ. RenW. GongY. JinY. ZhangJ. Time trends analysis of statin prescription prevalence, therapy initiation, dose intensity, and utilization from the hospital information system of Jinshan Hospital, Shanghai (2012-2018).BMC Cardiovasc. Disord.202020120110.1186/s12872‑020‑01482‑532334525
     [Google Scholar]
  126. Adler-MilsteinJ. LinS.C. JhaA.K. The number of health information exchange efforts is declining, leaving the viability of broad clinical data exchange uncertain.Health Aff.20163571278128510.1377/hlthaff.2015.143927385245
     [Google Scholar]
  127. ObermeyerZ. EmanuelE.J. Predicting the future-big data, machine learning, and clinical medicine.N. Engl. J. Med.2016375131216121910.1056/NEJMp160618127682033
     [Google Scholar]
  128. LavielleM. Puyraimond-ZemmourD. RomandX. GossecL. SenbelE. PouplinS. BeauvaisC. GutermannL. MezieresM. DougadosM. MoltoA. Methods to improve medication adherence in patients with chronic inflammatory rheumatic diseases: A systematic literature review.RMD Open201842e00068410.1136/rmdopen‑2018‑00068430116556
     [Google Scholar]
  129. ArnoldA. BentleyJ.P. PatelA. HolmesE. Predictors of pharmacists’ likelihood to query prescription drug monitoring program databases.J. Am. Pharm. Assoc.2021615614622.e310.1016/j.japh.2021.04.01933994328
     [Google Scholar]
  130. SahebT. SahebT. CarpenterD.O. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis.Comput. Biol. Med.202113510466010.1016/j.compbiomed.2021.10466034346319
     [Google Scholar]
  131. HoT.B. LeL. Tran ThaiD. TaewijitS. Data-driven approach to detect and predict adverse drug reactions.Curr. Pharm. Des.201622233498352610.2174/138161282266616050912504727157416
     [Google Scholar]
  132. MiottoR. WangF. WangS. JiangX. DudleyJ.T. Deep learning for healthcare: Review, opportunities and challenges.Brief. Bioinform.20181961236124610.1093/bib/bbx04428481991
     [Google Scholar]
  133. CaropreseL. VeltriP. VocaturoE. ZumpanoE. Deep learning techniques for electronic health record analysis. 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), Zakynthos, Greece, 2018, pp. 1-4.10.1109/IISA.2018.8633647
     [Google Scholar]
  134. LiangL. HuJ. SunG. HongN. WuG. HeY. LiY. HaoT. LiuL. GongM. Artificial intelligence-based pharmacovigilance in the setting of limited resources.Drug Saf.202245551151910.1007/s40264‑022‑01170‑735579814
     [Google Scholar]
  135. RaghupathiW. RaghupathiV. Big data analytics in healthcare: Promise and potential.Health Inf. Sci. Syst.201421310.1186/2047‑2501‑2‑325825667
     [Google Scholar]
  136. ZhangY. LiB. LingZ. ZhouF. Mitigating label bias in machine learning: Fairness through confident learning.Proc. Conf. AAAI Artif. Intell.20243815169171692510.1609/aaai.v38i15.29634
     [Google Scholar]
  137. TopolE.J. High-performance medicine: The convergence of human and artificial intelligence.Nat. Med.2019251445610.1038/s41591‑018‑0300‑730617339
     [Google Scholar]
/content/journals/cpd/10.2174/0113816128321623240719104337
Loading
Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems
Curr. Pharm. Des. 30, 2671 (2024); https://doi.org/10.2174/0113816128321623240719104337
/content/journals/cpd/10.2174/0113816128321623240719104337
/content/journals/cpd/10.2174/0113816128321623240719104337
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): artificial intelligence; electronic health records; Intelligent prescription systems; personalized medicine, biomarkers; precision medicine

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test