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Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med 2024; 178:108702. [PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 07/24/2024]
Abstract
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
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Affiliation(s)
- Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar, Maharashtra, 401404, India.
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Md Faiyazuddin
- School of Pharmacy, Al-Karim University, Katihar, Bihar, 854106, India; Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India.
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, ON, P3E 2C6, Canada.
| | - S Gowri
- PG & Research, Department of Physics, Cauvery College for Women, Tiruchirapalli, Tamil Nadu, 620018, India
| | - Mohammad Khalid
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
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Li L, Badgery-Parker T, Merchant A, Fitzpatrick E, Raban MZ, Mumford V, Metri NJ, Hibbert PD, Mccullagh C, Dickinson M, Westbrook JI. Paediatric medication incident reporting: a multicentre comparison study of medication errors identified at audit, detected by staff and reported to an incident system. BMJ Qual Saf 2024:bmjqs-2023-016711. [PMID: 38621921 DOI: 10.1136/bmjqs-2023-016711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 04/01/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVES To compare medication errors identified at audit and via direct observation with medication errors reported to an incident reporting system at paediatric hospitals and to investigate differences in types and severity of errors detected and reported by staff. METHODS This is a comparison study at two tertiary referral paediatric hospitals between 2016 and 2020 in Australia. Prescribing errors were identified from a medication chart audit of 7785 patient records. Medication administration errors were identified from a prospective direct observational study of 5137 medication administration doses to 1530 patients. Medication errors reported to the hospitals' incident reporting system were identified and matched with errors identified at audit and observation. RESULTS Of 11 302 clinical prescribing errors identified at audit, 3.2 per 1000 errors (95% CI 2.3 to 4.4, n=36) had an incident report. Of 2224 potentially serious prescribing errors from audit, 26.1% (95% CI 24.3 to 27.9, n=580) were detected by staff and 11.2 per 1000 errors (95% CI 7.6 to 16.5, n=25) were reported to the incident system. Although the prescribing error detection rates varied between the two hospitals, there was no difference in incident reporting rates regardless of error severity. Of 40 errors associated with actual patient harm, only 7 (17.5%; 95% CI 8.7% to 31.9%) were detected by staff and 4 (10.0%; 95% CI 4.0% to 23.1%) had an incident report. None of the 2883 clinical medication administration errors observed, including 903 potentially serious errors and 144 errors associated with actual patient harm, had incident reports. CONCLUSION Incident reporting data do not provide an accurate reflection of medication errors and related harm to children in hospitals. Failure to detect medication errors is likely to be a significant contributor to low error reporting rates. In an era of electronic health records, new automated approaches to monitor medication safety should be pursued to provide real-time monitoring.
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Affiliation(s)
- Ling Li
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Tim Badgery-Parker
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Alison Merchant
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Erin Fitzpatrick
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Magdalena Z Raban
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Virginia Mumford
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Najwa-Joelle Metri
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Peter Damian Hibbert
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Cheryl Mccullagh
- Executive, Beamtree, Redfern, New South Wales, Australia
- The Sydney Children's Hospitals Network Randwick and Westmead, Sydney, New South Wales, Australia
| | - Michael Dickinson
- The Sydney Children's Hospitals Network Randwick and Westmead, Sydney, New South Wales, Australia
| | - Johanna I Westbrook
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
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Benavent D, Muñoz-Fernández S, De la Morena I, Fernández-Nebro A, Marín-Corral J, Castillo Rosa E, Taberna M, Sanabra C, Sastre C. Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study. Ther Adv Musculoskelet Dis 2023; 15:1759720X231220818. [PMID: 38146537 PMCID: PMC10749530 DOI: 10.1177/1759720x231220818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/28/2023] [Indexed: 12/27/2023] Open
Abstract
Background Spondyloarthritis (SpA) is a group of related but phenotypically distinct inflammatory disorders that include axial SpA (axSpA) and psoriatic arthritis (PsA). Information on the characteristics and management of these patients in the real world remains scarce. Objectives To explore the characteristics and management [disease activity assessment and treatment with secukinumab (SEC) or other biologic disease-modifying antirheumatic drugs (bDMARDs)] of axSpA and PsA patients using natural language processing (NLP) in Electronic Health Records (EHRs). Design National, multicenter, observational, and retrospective study. Methods We analyzed free-text and structured clinical information from EHR at three hospitals. All adult patients with axSpA, PsA or non-classified SpA from 2018 to 2021 with minimum follow-up of three months were included when starting SEC or other bDMARDs. Clinical variables were extracted using EHRead® technology based on Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) terminology. Results Out of 887,735 patients, 758 were included, of which 328 had axSpA [58.5% male; mean (SD) age of 50.7 (12.7) years], 365 PsA [54.8% female, 53.9 (12.4) years], and 65 non-classified SpA. Mean (SD) time since diagnosis was 36.8 (61.0) and 24.1 (35.2) months for axSpA and PsA, respectively. Only 116 axSpA patients (35.3%) had available Ankylosing Spondylitis Disease Activity Score (ASDAS) or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) at bDMARD onset, of which 61 presented active disease. Disease Activity in PSoriatic Arthritis (DAPSA) or Disease Assessment Score - 28 joints (DAS-28) values at bDMARD onset were available for only 61 PsA (16.7%) patients, with 23 of them having active disease. The number of patients with available tender joint count or swollen joint count assessment was 68 (20.7%) and 59 (18%) for axSpA, and 115 (31.5%) and 119 (32.6%) for PsA, respectively. SEC was used in 63 (19.2%) axSpA patients and in 63 (17.3%) PsA patients. Conclusion Using NLP, the study showed that around one-third of axSpA and one-sixth of PsA patients have disease activity assessments with ASDAS/BASDAI or DAPSA/DAS-28, respectively, highlighting an area of improvement in these patients' management.
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Affiliation(s)
- Diego Benavent
- SAVANA Research S.L., Calle de Larra 12, Madrid 28013, Spain
| | - Santiago Muñoz-Fernández
- Hospital Universitario Infanta Sofía, Universidad Europea de Madrid, San Sebastián de los Reyes, Madrid, Spain
| | - Isabel De la Morena
- Department of Rheumatology, Hospital Clínico Universitario de Valencia, Valencia, Valencia, Spain
| | - Antonio Fernández-Nebro
- Instituto de Investigación Biomédica de Málaga (IBIMA)-Plataforma Bionand, Málaga, Spain
- UGC de Reumatología, Hospital Regional Universitario de Málaga, Málaga, Spain
- Departamento de Medicina, Universidad de Málaga, Málaga, Spain
| | | | | | | | | | - Carlos Sastre
- Medical Department, Novartis Farmacéutica SA., Barcelona, Spain
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Oborotov GA, Koshechkin KA, Orlov YL. Application of Artificial Intelligence or machine learning in risk sharing agreements for pharmacotherapy risk management. J Integr Bioinform 2023; 20:jib-2023-0014. [PMID: 38073025 PMCID: PMC10757074 DOI: 10.1515/jib-2023-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/17/2023] [Indexed: 12/31/2023] Open
Abstract
Applications of Artificial Intelligence in medical informatics solutions risk sharing have social value. At a time of ever-increasing cost for the provision of medicines to citizens, there is a need to restrain the growth of health care costs. The search for computer technologies to stop or slow down the growth of costs acquires a new very important and significant meaning. We discussed the two information technologies in pharmacotherapy and the possibility of combining and sharing them, namely the combination of risk-sharing agreements and Machine Learning, which was made possible by the development of Artificial Intelligence (AI). Neural networks could be used to predict the outcome to reduce the risk factors for treatment. AI-based data processing automation technologies could be also used for risk-sharing agreements automation.
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Affiliation(s)
- Grigory A. Oborotov
- Chair of Information and Internet Technologies, Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Konstantin A. Koshechkin
- Chair of Information and Internet Technologies, Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia
| | - Yuriy L. Orlov
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow, Russia
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Loscertales J, Abrisqueta-Costa P, Gutierrez A, Hernández-Rivas JÁ, Andreu-Lapiedra R, Mora A, Leiva-Farré C, López-Roda MD, Callejo-Mellén Á, Álvarez-García E, García-Marco JA. Real-World Evidence on the Clinical Characteristics and Management of Patients with Chronic Lymphocytic Leukemia in Spain Using Natural Language Processing: The SRealCLL Study. Cancers (Basel) 2023; 15:4047. [PMID: 37627075 PMCID: PMC10452602 DOI: 10.3390/cancers15164047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
The SRealCLL study aimed to obtain real-world evidence on the clinical characteristics and treatment patterns of patients with chronic lymphocytic leukemia (CLL) using natural language processing (NLP). Electronic health records (EHRs) from seven Spanish hospitals (January 2016-December 2018) were analyzed using EHRead® technology, based on NLP and machine learning. A total of 534 CLL patients were assessed. No treatment was detected in 270 (50.6%) patients (watch-and-wait, W&W). First-line (1L) treatment was identified in 230 (43.1%) patients and relapsed/refractory (2L) treatment was identified in 58 (10.9%). The median age ranged from 71 to 75 years, with a uniform male predominance (54.8-63.8%). The main comorbidities included hypertension (W&W: 35.6%; 1L: 38.3%; 2L: 39.7%), diabetes mellitus (W&W: 24.4%; 1L: 24.3%; 2L: 31%), cardiac arrhythmia (W&W: 16.7%; 1L: 17.8%; 2L: 17.2%), heart failure (W&W 16.3%, 1L 17.4%, 2L 17.2%), and dyslipidemia (W&W: 13.7%; 1L: 18.7%; 2L: 19.0%). The most common antineoplastic treatment was ibrutinib in 1L (64.8%) and 2L (62.1%), followed by bendamustine + rituximab (12.6%), obinutuzumab + chlorambucil (5.2%), rituximab + chlorambucil (4.8%), and idelalisib + rituximab (3.9%) in 1L and venetoclax (15.5%), idelalisib + rituximab (6.9%), bendamustine + rituximab (3.5%), and venetoclax + rituximab (3.5%) in 2L. This study expands the information available on patients with CLL in Spain, describing the diversity in patient characteristics and therapeutic approaches in clinical practice.
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Affiliation(s)
- Javier Loscertales
- Hematology Department, Hospital Universitario de la Princesa, Calle de Diego de León 62, 28006 Madrid, Spain;
| | - Pau Abrisqueta-Costa
- Hematology Department, Hospital Universitari Vall d’Hebron, Pg de la vall d’Hebron 199, 08035 Barcelona, Spain
| | - Antonio Gutierrez
- Hematology Department, Hospital Son Espases/IdISBa, Carretera de Valldemossa 79, 07120 Palma de Mallorca, Spain;
| | - José Ángel Hernández-Rivas
- Hematology Department, Hospital Universitario Infanta Leonor, Avda. Gran Vía del Este 80, 28031 Madrid, Spain;
| | - Rafael Andreu-Lapiedra
- Hematology Department, Hospital Universitario La Fe, Avinguda de Fernando Abril Martorell 106, 46026 Valencia, Spain;
| | - Alba Mora
- Hematology Department, Hospital de la Santa Creu i Sant Pau, Calle de St. Antoni Maria Claret 167, 08025 Barcelona, Spain;
| | - Carolina Leiva-Farré
- Medical Department, Astrazeneca Farmacéutica Spain S.A., Calle del Puerto de Somport 21, 28050 Madrid, Spain; (C.L.-F.); (M.D.L.-R.); (Á.C.-M.); (E.Á.-G.)
| | - María Dolores López-Roda
- Medical Department, Astrazeneca Farmacéutica Spain S.A., Calle del Puerto de Somport 21, 28050 Madrid, Spain; (C.L.-F.); (M.D.L.-R.); (Á.C.-M.); (E.Á.-G.)
| | - Ángel Callejo-Mellén
- Medical Department, Astrazeneca Farmacéutica Spain S.A., Calle del Puerto de Somport 21, 28050 Madrid, Spain; (C.L.-F.); (M.D.L.-R.); (Á.C.-M.); (E.Á.-G.)
| | - Esther Álvarez-García
- Medical Department, Astrazeneca Farmacéutica Spain S.A., Calle del Puerto de Somport 21, 28050 Madrid, Spain; (C.L.-F.); (M.D.L.-R.); (Á.C.-M.); (E.Á.-G.)
| | - José Antonio García-Marco
- Hematology Department, Hospital Universitario Puerta de Hierro-Majadahonda, Calle Joaquín Rodrigo 1, 28222 Majadahonda, Spain;
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Ranchon F, Chanoine S, Lambert-Lacroix S, Bosson JL, Moreau-Gaudry A, Bedouch P. Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review. Int J Med Inform 2023; 172:104983. [PMID: 36724730 DOI: 10.1016/j.ijmedinf.2022.104983] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/15/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Artificial Intelligence (AI) offers potential opportunities to optimize clinical pharmacy services in community or hospital settings. The objective of this systematic literature review was to identify and analyse quantitative studies using or integrating AI for clinical pharmacy services. MATERIALS AND METHODS A systematic review was conducted using PubMed/Medline and Web of Science databases, including all articles published from 2000 to December 2021. Included studies had to involve pharmacists in the development or use of AI-powered apps and tools.. RESULTS 19 studies using AI for clinical pharmacy services were included in this review. 12 out of 19 articles (63.1%) were published in 2020 or 2021. Various methodologies of AI were used, mainly machine learning techniques and subsets (natural language processing and deep learning). The datasets used to train the models were mainly extracted from electronic medical records (6 studies, 32%). Among clinical pharmacy services, medication order review was the service most targeted by AI-powered apps and tools (9 studies), followed by health product dispensing (4 studies), pharmaceutical interviews and therapeutic education (2 studies). The development of these tools mainly involved hospital pharmacists (12/19 studies). DISCUSSION AND CONCLUSION The development of AI-powered apps and tools for clinical pharmacy services is just beginning. Pharmacists need to keep abreast of these developments in order to position themselves optimally while maintaining their human relationships with healthcare teams and patients. Significant efforts have to be made, in collaboration with data scientists, to better assess whether AI-powered apps and tools bring value to clinical pharmacy services in real practice.
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Affiliation(s)
- Florence Ranchon
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Hospices Civils de Lyon, Hôpital Lyon Sud, unité de pharmacie clinique oncologique, Pierre-Bénite, France; Université Lyon-1, EA 3738 CICLY, Oullins cedex F-69921, France.
| | - Sébastien Chanoine
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Pôle Pharmacie, CHU Grenoble Alpes, F-38043 Grenoble, France; Université Grenoble Alpes, Faculté de Pharmacie, F-38041 Grenoble, France
| | | | - Jean-Luc Bosson
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France
| | | | - Pierrick Bedouch
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Pôle Pharmacie, CHU Grenoble Alpes, F-38043 Grenoble, France; Université Grenoble Alpes, Faculté de Pharmacie, F-38041 Grenoble, France
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Segura T, Medrano IH, Collazo S, Maté C, Sguera C, Del Rio-Bermudez C, Casero H, Salcedo I, García-García J, Alcahut-Rodríguez C, Taberna M. Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence. Sci Rep 2023; 13:702. [PMID: 36639403 PMCID: PMC9839769 DOI: 10.1038/s41598-023-27863-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.
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Affiliation(s)
- Tomás Segura
- University Hospital of Albacete, Albacete, Spain.
| | | | | | | | - Carlo Sguera
- Savana Research, Madrid, Spain.,UC3M-Santander Big Data Institute, Madrid, Spain
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Valdés Sanz N, García-Layana A, Colas T, Moriche M, Montero Moreno M, Ciprandi G. Clinical Characterization of Inpatients with Acute Conjunctivitis: A Retrospective Analysis by Natural Language Processing and Machine Learning. APPLIED SCIENCES 2022; 12:12352. [DOI: 10.3390/app122312352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2023]
Abstract
Background Acute bacterial conjunctivitis (ABC) is a relatively common medical condition caused by different pathogens. Although it rarely threatens vision, it is one of the most common conditions that cause red eyes and may be accompanied by discomfort and discharge. The study aimed to identify and characterize inpatients with ABC treated with topical antibiotics. Methods The EHRead® technology, based on natural language processing (NLP) and machine learning, was used to extract and analyze the clinical information in the electronic health records (EHRs) of antibiotic-treated patients with conjunctivitis and admitted to five hospitals in Spain between January 2014 and December 2018. Categorical variables were described by frequency, whereas numerical variables included the mean, standard deviation, median, and quartiles. Results From a source population of 2,071,812 adult patients who attended the participating hospitals in the study period, 11,110 patients diagnosed with acute conjunctivitis were identified. Six thousand five hundred eighty-three patients were treated with antibiotics, comprising the final study population. Microbiology was tested only on 12.1% of patients. Antibiotics, mainly tobramycin, and corticosteroids, mainly dexamethasone, were usually prescribed. NSAIDs were also used in about 50% of patients, always combined with antibiotics. Conclusions The present study provided a realistic representation of the hospital practice concerning managing patients with acute antibiotic-treated conjunctivitis. The diagnosis is usually based on the clinical ground, microbiology is rarely tested, few bacteria species are involved, and local antibiotics are frequently associated with corticosteroids and/or NSAIDs. Moreover, this study provided clinically relevant outcomes, based on new technology, that could be applied in clinical practice.
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Poveda JL, Bretón-Romero R, Del Rio-Bermudez C, Taberna M, Medrano IH. How can artificial intelligence optimize value-based contracting? J Pharm Policy Pract 2022; 15:85. [PMID: 36401303 PMCID: PMC9673444 DOI: 10.1186/s40545-022-00475-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022] Open
Abstract
Efforts in the pharmaceutical market have been aimed at ensuring that the benefits obtained from the introduction of new therapies justify the associated costs. In recent years, drug payment models in healthcare have undergone a dramatic shift from focusing on volume (i.e., size of the target clinical population) to focusing on value (i.e., drug performance in real-world settings). In this context, value-based contracts (VBCs) were designed to align the payment of a drug to its clinical performance outside clinical trials by evaluating the effectiveness using real-word evidence (RWE). Despite their widespread implementation, different factors jeopardize the application of VBCs to most marketed drugs in a near future, including the need for easily measurable and relevant outcomes associated with clinical improvements, and access to a large patient population to assess said outcomes. Here, we argue that the extraction and analysis of massive amounts of RWE captured in patients' electronic health records (EHRs) will circumvent these issues and optimize negotiations in VBCs. Particularly, the use of Natural Language Processing (NLP) has proven successful in the analysis of structured and unstructured clinical information in EHRs in multicenter research studies. Thus, the application of NLP to analyze patient-centered information in EHRs in the context of innovative contracting can be utterly beneficial as it enables the real-time evaluation of treatment response and financial impact in real-world settings.
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Affiliation(s)
- Jose Luis Poveda
- Pharmacy Department, Drug Clinical Area, University and Polytechnic Hospital La Fe, Avda. Fernando Abril Martorell 106, 46026, Valencia, Spain.
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Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing. J Clin Med 2022; 11:jcm11206004. [PMID: 36294325 PMCID: PMC9605132 DOI: 10.3390/jcm11206004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/26/2022] [Accepted: 10/06/2022] [Indexed: 11/22/2022] Open
Abstract
Patients with Type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) are at high risk of developing major adverse cardiovascular events (MACE). This is a multicenter, retrospective, and observational study performed in Spain aimed to characterize these patients in a real-world setting. Unstructured data from the Electronic Health Records were extracted by EHRead®, a technology based on Natural Language Processing and machine learning. The association between new MACE and the variables of interest were investigated by univariable and multivariable analyses. From a source population of 2,184,662 patients, we identified 4072 adults diagnosed with T2DM and CAD (62.2% male, mean age 70 ± 11). The main comorbidities observed included arterial hypertension, hyperlipidemia, and obesity, with metformin and statins being the treatments most frequently prescribed. MACE development was associated with multivessel (Hazard Ratio (HR) = 2.49) and single coronary vessel disease (HR = 1.71), transient ischemic attack (HR = 2.01), heart failure (HR = 1.32), insulin treatment (HR = 1.40), and percutaneous coronary intervention (PCI) (HR = 2.27), whilst statins (HR = 0.73) were associated with a lower risk of MACE occurrence. In conclusion, we found six risk factors associated with the development of MACE which were related with cardiovascular diseases and T2DM severity, and treatment with statins was identified as a protective factor for new MACE in this study.
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Gomollón F, Gisbert JP, Guerra I, Plaza R, Pajares Villarroya R, Moreno Almazán L, López Martín MC, Domínguez Antonaya M, Vera Mendoza MI, Aparicio J, Martínez V, Tagarro I, Fernández-Nistal A, Lumbreras S, Maté C, Montoto C. Clinical characteristics and prognostic factors for Crohn's disease relapses using natural language processing and machine learning: a pilot study. Eur J Gastroenterol Hepatol 2022; 34:389-397. [PMID: 34882644 PMCID: PMC8876385 DOI: 10.1097/meg.0000000000002317] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND The impact of relapses on disease burden in Crohn's disease (CD) warrants searching for predictive factors to anticipate relapses. This requires analysis of large datasets, including elusive free-text annotations from electronic health records. This study aims to describe clinical characteristics and treatment with biologics of CD patients and generate a data-driven predictive model for relapse using natural language processing (NLP) and machine learning (ML). METHODS We performed a multicenter, retrospective study using a previously validated corpus of CD patient data from eight hospitals of the Spanish National Healthcare Network from 1 January 2014 to 31 December 2018 using NLP. Predictive models were created with ML algorithms, namely, logistic regression, decision trees, and random forests. RESULTS CD phenotype, analyzed in 5938 CD patients, was predominantly inflammatory, and tobacco smoking appeared as a risk factor, confirming previous clinical studies. We also documented treatments, treatment switches, and time to discontinuation in biologics-treated CD patients. We found correlations between CD and patient family history of gastrointestinal neoplasms. Our predictive model ranked 25 000 variables for their potential as risk factors for CD relapse. Of highest relative importance were past relapses and patients' age, as well as leukocyte, hemoglobin, and fibrinogen levels. CONCLUSION Through NLP, we identified variables such as smoking as a risk factor and described treatment patterns with biologics in CD patients. CD relapse prediction highlighted the importance of patients' age and some biochemistry values, though it proved highly challenging and merits the assessment of risk factors for relapse in a clinical setting.
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Affiliation(s)
| | - Javier P. Gisbert
- Gastroenterology Unit, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS-IP), Universidad Autónoma de Madrid, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD)
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12
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Montoto C, Gisbert JP, Guerra I, Plaza R, Pajares Villarroya R, Moreno Almazán L, López Martín MDC, Domínguez Antonaya M, Vera Mendoza I, Aparicio J, Martínez V, Tagarro I, Fernandez-Nistal A, Canales L, Menke S, Gomollón F. Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project. JMIR Med Inform 2022; 10:e30345. [PMID: 35179507 PMCID: PMC8900906 DOI: 10.2196/30345] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/22/2021] [Accepted: 01/02/2022] [Indexed: 12/29/2022] Open
Abstract
Background The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs. Objective The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD. Methods We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables. Results The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively. Conclusions This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish.
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Affiliation(s)
| | - Javier P Gisbert
- Hospital Universitario de La Princesa, Madrid, Spain.,Instituto de Investigación Sanitaria Princesa (IIS-IP), Madrid, Spain.,Universidad Autónoma de Madrid, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Madrid, Spain
| | - Iván Guerra
- Hospital Universitario de Fuenlabrada, Madrid, Spain
| | - Rocío Plaza
- Hospital Universitario Infanta Leonor, Madrid, Spain
| | | | | | | | | | | | | | | | | | | | - Lea Canales
- Department of Software and Computing System, University of Alicante, Alicante, Spain
| | | | - Fernando Gomollón
- Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.,Instituto de Investigación Sanitaria Aragón (IISA), Zaragoza, Spain.,Universidad de Zaragoza, Zaragoza, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Zaragoza, Spain
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González-Juanatey C, Anguita-Sá́nchez M, Barrios V, Núñez-Gil I, Gómez-Doblas JJ, García-Moll X, Lafuente-Gormaz C, Rollán-Gómez MJ, Peral-Disdie V, Martínez-Dolz L, Rodríguez-Santamarta M, Viñolas-Prat X, Soriano-Colomé T, Muñoz-Aguilera R, Plaza I, Curcio-Ruigómez A, Orts-Soler E, Segovia J, Maté C, Cequier Á. Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing. PLoS One 2022; 17:e0263277. [PMID: 35143527 PMCID: PMC8830700 DOI: 10.1371/journal.pone.0263277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 01/15/2022] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION AND OBJECTIVES Patients with type 2 diabetes (T2D) and stable coronary artery disease (CAD) previously revascularized with percutaneous coronary intervention (PCI) are at high risk of recurrent ischemic events. We aimed to provide real-world insights into the clinical characteristics and management of this clinical population, excluding patients with a history of myocardial infarction (MI) or stroke, using Natural Language Processing (NLP) technology. METHODS This is a multicenter, retrospective study based on the secondary use of 2014-2018 real-world data captured in the Electronic Health Records (EHRs) of 1,579 patients (0.72% of the T2D population analyzed; n = 217,632 patients) from 12 representative hospitals in Spain. To access the unstructured clinical information in EHRs, we used the EHRead® technology, based on NLP and machine learning. Major adverse cardiovascular events (MACE) were considered: MI, ischemic stroke, urgent coronary revascularization, and hospitalization due to unstable angina. The association between MACE rates and the variables included in this study was evaluated following univariate and multivariate approaches. RESULTS Most patients were male (72.13%), with a mean age of 70.5±10 years. Regarding T2D, most patients were non-insulin-dependent T2D (61.75%) with high prevalence of comorbidities. The median (Q1-Q3) duration of follow-up was 1.2 (0.3-4.5) years. Overall, 35.66% of patients suffered from at least one MACE during follow up. Using a Cox Proportional Hazards regression model analysis, several independent factors were associated with MACE during follow up: CAD duration (p < 0.001), COPD/Asthma (p = 0.021), heart valve disease (p = 0.031), multivessel disease (p = 0.005), insulin treatment (p < 0.001), statins treatment (p < 0.001), and clopidogrel treatment (p = 0.039). CONCLUSIONS Our results showed high rates of MACE in a large real-world series of PCI-revascularized patients with T2D and CAD with no history of MI or stroke. These data represent a potential opportunity to improve the clinical management of these patients.
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Affiliation(s)
| | | | | | - Iván Núñez-Gil
- Hospital Clínico Universitario San Carlos, Madrid, Spain
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ángel Cequier
- Hospital Universitario de Bellvitge and Universidad de Barcelona, IDIBELL, Barcelona, Spain
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Canales L, Menke S, Marchesseau S, D'Agostino A, Del Rio-Bermudez C, Taberna M, Tello J. Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology. JMIR Med Inform 2021; 9:e20492. [PMID: 34297002 PMCID: PMC8367121 DOI: 10.2196/20492] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/31/2020] [Accepted: 06/17/2021] [Indexed: 12/22/2022] Open
Abstract
Background Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient’s clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them. Objective Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured. Methods The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called “EHRead Technology” (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard. Results The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs. Conclusions We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.
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Affiliation(s)
- Lea Canales
- Department of Software and Computing System, University of Alicante, Alicante, Spain
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