Noa Hurvitz, Hillel Lehman, Yoav Hershkovitz, Yotam Kolben, Khurram Jamil, Samuel Agus, Marc Berg, Suhail Aamar, Yaron Ilan
{"title":"基于约束无序原理的第二代人工智能数字医用大麻系统：现实世界数据分析。","authors":"Noa Hurvitz, Hillel Lehman, Yoav Hershkovitz, Yotam Kolben, Khurram Jamil, Samuel Agus, Marc Berg, Suhail Aamar, Yaron Ilan","doi":"10.1177/22799036251337640","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Adhering to treatment plans can be challenging for medical cannabis patients. According to the constrained-disorder principle (CDP), biological systems are defined by their degree of variability. CDP-based second-generation artificial intelligence (AI) systems use personalized variability signatures to improve chronic medication response.</p><p><strong>Aim: </strong>We retrospectively analyzed real-world data regarding chronic pain patients using the second generation of artificial intelligence systems to improve adherence to medical cannabis and increase its effectiveness.</p><p><strong>Design and methods: </strong>A retrospective analysis of real-world data of 27 patients using prescribed medical cannabis for chronic pain was conducted. Patients received treatment according to a regimen provided by the CDP-based second-generation AI Altus Care™ app that managed the product's dosage and administration times. The app offers a therapeutic regimen by varying dosages and administration times within predefined ranges. We included 16 patients who participated for more than a week. We assessed adherence to therapy and clinical response in real life based on pain scale measurements.</p><p><strong>Results: </strong>The patients were followed up for 64 days (30-189). Second-generation, AI-based, personalized regimens had a high engagement rate and adherence. 50% of patients showed a high compliance rate. Chronic pain improved in patients who reported their pain score.</p><p><strong>Summary: </strong>This preliminary real-world data analysis suggests that an algorithm-based approach using a second-generation AI system may enhance the adherence to and clinical effectiveness of medical cannabis. These findings require confirmation through prospective controlled studies.</p>","PeriodicalId":45958,"journal":{"name":"Journal of Public Health Research","volume":"14 2","pages":"22799036251337640"},"PeriodicalIF":1.6000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149626/pdf/","citationCount":"0","resultStr":"{\"title\":\"A constrained disorder principle-based second-generation artificial intelligence digital medical cannabis system: A real-world data analysis.\",\"authors\":\"Noa Hurvitz, Hillel Lehman, Yoav Hershkovitz, Yotam Kolben, Khurram Jamil, Samuel Agus, Marc Berg, Suhail Aamar, Yaron Ilan\",\"doi\":\"10.1177/22799036251337640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Adhering to treatment plans can be challenging for medical cannabis patients. According to the constrained-disorder principle (CDP), biological systems are defined by their degree of variability. CDP-based second-generation artificial intelligence (AI) systems use personalized variability signatures to improve chronic medication response.</p><p><strong>Aim: </strong>We retrospectively analyzed real-world data regarding chronic pain patients using the second generation of artificial intelligence systems to improve adherence to medical cannabis and increase its effectiveness.</p><p><strong>Design and methods: </strong>A retrospective analysis of real-world data of 27 patients using prescribed medical cannabis for chronic pain was conducted. Patients received treatment according to a regimen provided by the CDP-based second-generation AI Altus Care™ app that managed the product's dosage and administration times. The app offers a therapeutic regimen by varying dosages and administration times within predefined ranges. We included 16 patients who participated for more than a week. We assessed adherence to therapy and clinical response in real life based on pain scale measurements.</p><p><strong>Results: </strong>The patients were followed up for 64 days (30-189). Second-generation, AI-based, personalized regimens had a high engagement rate and adherence. 50% of patients showed a high compliance rate. Chronic pain improved in patients who reported their pain score.</p><p><strong>Summary: </strong>This preliminary real-world data analysis suggests that an algorithm-based approach using a second-generation AI system may enhance the adherence to and clinical effectiveness of medical cannabis. 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A constrained disorder principle-based second-generation artificial intelligence digital medical cannabis system: A real-world data analysis.
Introduction: Adhering to treatment plans can be challenging for medical cannabis patients. According to the constrained-disorder principle (CDP), biological systems are defined by their degree of variability. CDP-based second-generation artificial intelligence (AI) systems use personalized variability signatures to improve chronic medication response.
Aim: We retrospectively analyzed real-world data regarding chronic pain patients using the second generation of artificial intelligence systems to improve adherence to medical cannabis and increase its effectiveness.
Design and methods: A retrospective analysis of real-world data of 27 patients using prescribed medical cannabis for chronic pain was conducted. Patients received treatment according to a regimen provided by the CDP-based second-generation AI Altus Care™ app that managed the product's dosage and administration times. The app offers a therapeutic regimen by varying dosages and administration times within predefined ranges. We included 16 patients who participated for more than a week. We assessed adherence to therapy and clinical response in real life based on pain scale measurements.
Results: The patients were followed up for 64 days (30-189). Second-generation, AI-based, personalized regimens had a high engagement rate and adherence. 50% of patients showed a high compliance rate. Chronic pain improved in patients who reported their pain score.
Summary: This preliminary real-world data analysis suggests that an algorithm-based approach using a second-generation AI system may enhance the adherence to and clinical effectiveness of medical cannabis. These findings require confirmation through prospective controlled studies.
期刊介绍：
The Journal of Public Health Research (JPHR) is an online Open Access, peer-reviewed journal in the field of public health science. The aim of the journal is to stimulate debate and dissemination of knowledge in the public health field in order to improve efficacy, effectiveness and efficiency of public health interventions to improve health outcomes of populations. This aim can only be achieved by adopting a global and multidisciplinary approach. The Journal of Public Health Research publishes contributions from both the “traditional'' disciplines of public health, including hygiene, epidemiology, health education, environmental health, occupational health, health policy, hospital management, health economics, law and ethics as well as from the area of new health care fields including social science, communication science, eHealth and mHealth philosophy, health technology assessment, genetics research implications, population-mental health, gender and disparity issues, global and migration-related themes. In support of this approach, JPHR strongly encourages the use of real multidisciplinary approaches and analyses in the manuscripts submitted to the journal. In addition to Original research, Systematic Review, Meta-analysis, Meta-synthesis and Perspectives and Debate articles, JPHR publishes newsworthy Brief Reports, Letters and Study Protocols related to public health and public health management activities.
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