{"id":33054,"date":"2018-11-14T17:38:13","date_gmt":"2018-11-13T21:10:47","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/can-machine-learning-predict-prevent-and-diagnose-diabetes-effectively\/"},"modified":"2018-11-14T17:38:13","modified_gmt":"2018-11-14T22:38:13","slug":"can-machine-learning-predict-prevent-and-diagnose-diabetes-effectively","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-machine-learning-predict-prevent-and-diagnose-diabetes-effectively\/","title":{"rendered":"Can Machine Learning predict, prevent and diagnose Diabetes effectively?"},"content":{"rendered":"

Why do you think the megatrend you selected is important to your organization\u2019s management of process improvement and \/ or product development?<\/strong><\/p>\n

\u201cClearly rice consumption in Kerala has led to maximum prevalence of Diabetes<\/em>[1]<\/strong><\/em><\/a> \u201d remarked the marketing manager, Abbott Diabetes Care business in India, as he looked at the reports prepared by an in-house analytics team\u00a0[2]<\/a>\u00a0using Machine Learning (ML).<\/p>\n

The results meant huge potential for Abbott, that had a continuum of care products in Diabetes accounting for over $1.5bn of sales globally and a growth of 33%. In India, Diabetes products accounted for nearly $300mm of annual sales.[3]<\/a><\/p>\n

These insights reinforced the power of ML in healthcare to identify critical factors in disease management. Diabetes has about 422mm patients globally in 2014 and seventh leading cause of death in 2016, as per WHO. In India, 72mm cases were recorded in 2017, the figure expected to nearly double by 2025.[4]<\/a><\/p>\n

Extensive research in all aspects of diabetes has led to the generation of huge amounts of data, making the disease a great fit for applying ML to find better solutions and improve outcomes.[5]<\/a> A dual approach of developing inhouse capability and forging external partnership has enabled Abbott to take advantage of the new trend.<\/p>\n

What is organizations management doing to address this issue in the short term (next two years) and the medium term (two to ten years out)?<\/strong><\/p>\n

In the short term, Abbott is leading the development of \u201csmart\u201d products that can interact with patients in real time and generate high quality patient data. Some of the products launched are as mentioned below:\u00a0[6]<\/a><\/p>\n

    \n
  1. FreeStyleLibre system: It consists of a small, disposable sensor placed under the skin and worn on the back of the arm for 14 days<\/li>\n
  2. FreeStyle Libre reader: A handheld device, that scans the sensor to instantly obtain current glucose levels and historical patterns and trends<\/li>\n
  3. LibreLink: A mobile app that enables a compatible Android phone to scan the FreeStyle Libre sensor<\/li>\n
  4. LibreLinkUp: An app that allows caregivers to receive notifications when a user scans the sensors<\/li>\n<\/ol>\n
    \"\"<\/a>
    FreeStyleLibre<\/strong><\/figcaption><\/figure>\n

    To capture the data produced, Abbott is seeking required patient and regulatory approval. <\/strong>In Europe, Abbott sought permission from its patients scanning their sensors for accessing their data through a cloud-based service to help guide treatment decisions. To date, more than 50,000 people who used the system from 2014 and 2016 granted Abbott access, yielding more than 409.4mm glucose measurements, 86.4mm monitoring hours and 63.8mm scans.<\/p>\n

    In the long term, Abbott plans to use ML for process improvement of the continuum of care by intervention at all stages of Diabetes management.<\/p>\n

      \n
    1. Disease prediction<\/strong>: Abbott plans to support world-wide studies using ML to improve accuracy of heart risk prediction within a large population.[7]<\/a><\/li>\n
    2. Disease prevention<\/strong>: Abbott is exploring partnerships with companies that leverage ML from smartphones to help patients \u2013 under a physician’s guidance – make better decisions on diet, medications and other health-related issues[8]<\/a>.<\/li>\n
    3. Disease management<\/strong>: Abbott is evaluating partnerships with tech-giants to co-develop apps that would use voice-enabled software ( e.g. Amazon’s Alexa) to help diabetic patients manage disease at home \u2013 including lifestyle modification, dietary checks or pill reminders[9]<\/a>.<\/li>\n<\/ol>\n

      What other steps do you recommend the organization\u2019s management take to address this issue in the short and medium term?<\/strong><\/p>\n

      In India, diagnosis of Diabetes is 55% and the adherence to medicine is 40%. In the short term, Abbott can use ML to drive improvement in diagnosis and medicine adherence for Diabetes.<\/p>\n

        \n
      1. Targeted diagnosis campaigns: Using ML, identify regions with higher distribution of diabetes population and conduct targeted campaigns or educational programs<\/li>\n
      2. Identify associated factors: Identify related markers indicating onset of Diabetes such as chronic kidney disease, cardio vascular complications and eye-complications to proactively manage Diabetes.<\/li>\n
      3. Pill plus models for active diabetes patients: Make the LibreLink App smarter integrating with mobile phone and setting reminders for pill consumption \/ routine checkups.<\/li>\n<\/ol>\n
        \"\"<\/a>
        Pill Reminder App Interface<\/strong><\/figcaption><\/figure>\n

        Medium terms<\/strong><\/p>\n

          \n
        1. Integration with mobile health apps such as Fitbit to provide recommendations on lifestyle management<\/li>\n
        2. ML coupled with big data to be used in improving outcomes of drug-discovery or product enhancement for rare diseases such as oncology.<\/li>\n
        3. With large sales force, ML can be used to improve the internal reporting and control of salesforce. ML can also be utilized to better manage supply demand planning for critical drugs that require expensive cold storage.<\/li>\n<\/ol>\n
          \"\"<\/a>
          Disease Management Continuum<\/strong><\/figcaption><\/figure>\n

          In the context of this organization, what are the one or two important open questions related to this issue that that you are unsure about that merits comments from your classmates?<\/strong><\/p>\n

            \n
          1. How to reduce dependence on human intervention and remove initial biases?\u00a0<\/em>ML is still dependent on human input as a human \u201ctrains\u201d a system with data and categorizes a good pattern versus an inaccurate pattern.<\/li>\n
          2. How to improve knowledge of limited variable?\u00a0<\/em>Scientists don’t know all of the components \/ unknown variables that affects disease manifestation \/ progresses.<\/li>\n
          3. How\u00a0to address concerns around privacy and regulations?\u00a0<\/em>Data access is an important input for success of ML, however regulations around patient data safety could be a potential hindrance.<\/li>\n<\/ol>\n
            \n
              References<\/ol>\n
                \n
              1. Diabetes – Symptoms And Causes”. 2018.\u00a0Mayo Clinic<\/i>. https:\/\/www.mayoclinic.org\/diseases-conditions\/diabetes\/symptoms-causes\/syc-20371444.<\/li>\n
              2. “Kerala Has High Rate Of Diabetes Incidence”. 2018.\u00a0The Hindu<\/i>. https:\/\/www.thehindu.com\/news\/cities\/Thiruvananthapuram\/kerala-has-high-rate-of-diabetes-incidence\/article8457105.ece.<\/li>\n
              3. “Abbott Reports Fourth-Quarter 2017 Results”. 2018.\u00a0Prnewswire.Com<\/i>. https:\/\/www.prnewswire.com\/news-releases\/abbott-reports-fourth-quarter-2017-results-300587414.html.<\/li>\n
              4. “Diabetes”. 2018.\u00a0World Health Organization<\/i>. http:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes.<\/li>\n
              5. “Big Pharma Outlook Webinar | Pharma Intelligence”. 2018.\u00a0Pharmaintelligence.Informa.Com<\/i>. https:\/\/pharmaintelligence.informa.com\/resources\/product-content\/artificial-intelligence-and-future-health-care-innovation<\/span><\/li>\n
              6. “Continuous Glucose Monitoring System”. 2018.\u00a0Freestylelibre.Us<\/i>. https:\/\/www.freestylelibre.us\/.<\/li>\n
              7. “Can machine-learning improve cardiovascular risk prediction using routine clinical data?”\u00a0https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0174944<\/li>\n
              8. “Big Pharma Outlook Webinar | Pharma Intelligence”. 2018.\u00a0Pharmaintelligence.Informa.Com<\/i>. https:\/\/pharmaintelligence.informa.com\/resources\/product-content\/artificial-intelligence-and-future-health-care-innovation<\/span><\/li>\n
              9. “Big Pharma Outlook Webinar | Pharma Intelligence”. 2018.\u00a0Pharmaintelligence.Informa.Com<\/i>. https:\/\/pharmaintelligence.informa.com\/resources\/product-content\/artificial-intelligence-and-future-health-care-innovation<\/span><\/li>\n<\/ol>\n

                 <\/p>\n","protected":false},"excerpt":{"rendered":"

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