https://doi.org/10.4081/tmi.2023.375

The development of a new tool useful to control and limit the spread of pathologies using drug dispensation data

Vol. 1 (2023)
Published: December 27 2023
Telemedicine, monitoring the spread of disease, active pharmacovigilance, real-world evidence
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Authors

Felice Simiele
felice.simiele@asl.pe.it
ASL Pescara General Hospital, Italy.
Mario D'Intino
ASL Pescara General Hospital, Italy.
Alberto Costantini
ASL Pescara General Hospital, Italy.

The literature reports several studies to improve adherence to therapy through the development of new technologies. However, a system which promptly displays the trend of a disease has never been reported. We have created a new tool that can immediately identify the development of a disease and the high-risk areas. During the COVID pandemic, we understood the importance of monitoring the trend of pathology in real-time, on a map. Through a software named Qlik Sense, we identified the areas with the greatest distribution of anticoagulant drugs. The software geolocated the pharmacies and, when dispensing the drugs, it created a bright spot on the map of the Abruzzo region, Italy, with an intensity proportional to the number of patients who received the drug for the first time. In this study, we were able to visualize immediately the presence of comorbidities that could worsen the health of patients. The tool created identifies the predominance of a pathology in order to establish correct health policies, and, in the future, it could allow clinicians to monitor patient therapy.

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Aronson JK, Heneghan C, Ferner RE. Medical Devices: Definition, Classification, and Regulatory Implications. Drug Saf 2020;43:83-93.
deBronkart D. From patient centred to people powered: autonomy on the rise. BMJ 2015;350:h148.
Finset A. Patient Participation, Engagement and Activation: Increased emphasis on the role of patients in healthcare. Patient Educ Couns 2017;100:1245-6.
Aripiprazole with digital ingestion tracking (Abilify MyCite). Med Lett Drugs Ther 2019;61:15-6.
Kane JM. Comments on Abilify MyCite. Clin Schizophr Relat Psychoses 2018;11:205-6.
Karas C. US Food and Drug Administration Unique Device Identifier Rule: Perioperative Basics. AORN J 2016;103:329-32.
Yu W, Jiang C, Xie B, et al. Ratiometric fluorescent sensing system for drug residue analysis: Highly sensitive immunosensor using dual-emission quantum dots hybrid and compact smartphone based-device. Anal Chim Acta. 2020;1102:91-8.
Bulaj G. Combining non-pharmacological treatments with pharmacotherapies for neurological disorders: a unique interface of the brain, drug-device, and intellectual property. Front Neurol 2014;5:126.
Packer M. Compelling First-Line Drug and Device Therapies for the Prevention of Sudden Death in Patients With Chronic Heart Failure and a Reduced Ejection Fraction Who Are Candidates for an Implantable Cardioverter-Defibrillator. Circ Arrhythm Electrophysiol 2019;12:e007430.
Khalid N, Rogers T, Shlofmitz E, et al. Overview of the 2018 US Food and Drug Administration Circulatory System Devices Panel Meeting on Device-Based Therapies for hypertension. Cardiovasc Revascularization Med Mol Interv 2019;20:891-6.
Proesmans T, Mortelmans C, Van Haelst R, et al. Mobile Phone-Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App. JMIR MHealth UHealth 2019;7:e12284.
Guyatt GH, Sackett DL, Cook DJ. Users’ guides to the medical literature. II. How to use an article about therapy or prevention. A. Are the results of the study valid? Evidence-Based Medicine Working Group. JAMA 1993;270:2598-601.
Barrett M, Boyne J, Brandts J, et al. Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care. EPMA J 2019;10:445-64.
McGinty GB, Allen B. The ACR Data Science Institute and AI Advisory Group: Harnessing the Power of Artificial Intelligence to Improve Patient Care. J Am Coll Radiol JACR 2018;15:577-9.
Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 2018;15:e1002686.
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 2017;318:2199-210.
Sayres R, Taly A, Rahimy E, et al. Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy. Ophthalmology 2019;126:552-64.
Jim HSL, Hoogland AI, Brownstein NC, et al. Innovations in research and clinical care using patient-generated health data. CA Cancer J Clin. 2020;70:182-99.
Hartzler AL, McDonald DW, Park A, et al. Evaluating health interest profiles extracted from patient-generated data. AMIA Annu Symp Proc AMIA Symp 2014;2014:626-35.
Aschettino L, Baldwin K, Friedman B, et al. Including Patient-Generated Health Data in Electronic Health Records. J AHIMA 2015;86:54-7.
Lai AM, Hsueh PYS, Choi YK, Austin RR. Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data. Yearb Med Inform 2017;26:152-9.
Lindroth T, Islind AS, Steineck G, Lundin J. From Narratives to Numbers: Data Work and Patient-Generated Health Data in Consultations. Stud Health Technol Inform 2018;247:491-5.
Bradley SM. Use of Mobile Health and Patient-Generated Data-Making Health Care Better by Making Health Care Different. JAMA Netw Open 2020;3:e202971.
Alpert JM, Manini T, Roberts M, et al. Secondary care provider attitudes towards patient generated health data from smartwatches. NPJ Digit Med 2020;3:27.
Lavallee DC, Lee JR, Austin E, et al. mHealth and patient generated health data: stakeholder perspectives on opportunities and barriers for transforming healthcare. mHealth 2020;6:8.
Tiase VL, Sward KA, Cummins MR. Navigating the Search for Patient Generated Health Data. Stud Health Technol Inform 2019;264:1992.
Nittas V, Lun P, Ehrler F, et al. Electronic Patient-Generated Health Data to Facilitate Disease Prevention and Health Promotion: Scoping Review. J Med Internet Res 2019;21:e13320.
Jung SY, Kim JW, Hwang H, et al. Development of Comprehensive Personal Health Records Integrating Patient-Generated Health Data Directly From Samsung S-Health and Apple Health Apps: Retrospective Cross-Sectional Observational Study. JMIR MHealth UHealth 2019;7:e12691.
Abdolkhani R, Borda A, Gray K. Quality Management of Patient Generated Health Data in Remote Patient Monitoring Using Medical Wearables - A Systematic Review. Stud Health Technol Inform 2018;252:1-7.
Plastiras P, O’Sullivan D. Exchanging personal health data with electronic health records: A standardized information model for patient generated health data and observations of daily living. Int J Med Inf 2018;120:116-25.
Tiase VL, Hull W, McFarland MM, et al. Patient-generated health data and electronic health record integration: a scoping review. JAMIA Open 2020;3:619-27.
Park YR, Lee Y, Kim JY, et al. Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data. JMIR MHealth UHealth 2018;6:e89.
Naets T, Vervoort L, Tanghe A, Braet C. Adherence and barriers in e-health self-control training for enhancing childhood multidisciplinary obesity treatment. Clin Psychol Psychother 2020;27:42-51.
Chan AHY, Reddel HK, Apter A, et al. Adherence monitoring and e-health: how clinicians and researchers can use technology to promote inhaler adherence for asthma. J Allergy Clin Immunol Pract 2013;1:446-54.
Ehrmann D, Spengler M, Jahn M, et al. Adherence Over Time: The Course of Adherence to Customized Diabetic Insoles as Objectively Assessed by a Temperature Sensor. J Diabetes Sci Technol 2018;12:695-700.
Botros A, Schütz N, Camenzind M, et al. Long-Term Home-Monitoring Sensor Technology in Patients with Parkinson’s Disease-Acceptance and Adherence. Sensors 2019;19:5169.
Nishimura K, Tabuchi H, Nakakura S, Net al. Evaluation of Automatic Monitoring of Instillation Adherence Using Eye Dropper Bottle Sensor and Deep Learning in Patients With Glaucoma. Transl Vis Sci Technol 2019;8:55.

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The development of a new tool useful to control and limit the spread of pathologies using drug dispensation data. (2023). TeleMedicine International, 1(1). https://doi.org/10.4081/tmi.2023.375
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