{"id":20461,"date":"2016-11-18T17:49:46","date_gmt":"2016-11-18T22:49:46","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/a-change-of-heart-infobionics-cheaper-better-way-to-analyze-heart-arrhythmias\/"},"modified":"2016-11-18T17:49:46","modified_gmt":"2016-11-18T22:49:46","slug":"a-change-of-heart-infobionics-cheaper-better-way-to-analyze-heart-arrhythmias","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/a-change-of-heart-infobionics-cheaper-better-way-to-analyze-heart-arrhythmias\/","title":{"rendered":"A change of heart: InfoBionic\u2019s cheaper, better way to analyze heart arrhythmias"},"content":{"rendered":"

InfoBionic shows how advances in cloud computing and wireless devices enable more seamless, efficient diagnosis and monitoring of heart arrhythmias, which until now have been costly and labor intensive diseases to diagnose.\u00a0By simplifying\u00a0the diagnosis of heart arrhythmias with just one device, InfoBionic is creating an opportunity for itself to use machine learning on huge, novel datasets to help doctors and patients better manage this challenging condition.<\/p>\n

Heart arrhythmias are episodic, irregular heartbeats that occur in several million Americans,[1] leaving them at greater risk for stroke and heart disease and contributing to 130,000 deaths each year in the United States.[2] Unfortunately, heart arrhythmias are very difficult to diagnose as they occur unpredictability with not yet fully understood triggers that vary across patients.[3] Currently, diagnosis works in three stages, where patients whose condition is not identified in an earlier stage moving on to a costlier, more involved method that culminates in mobile cardiac telemetry where real-time patient data is sent to humans who flag unusual heart activity to doctors. The current system has the disadvantages that it requires numerous patient visits to try and trade-in the different diagnostic devices, requires doctors to have three types of devices on hand and be expert at analyzing their different kinds of reports, and costs a lot to the healthcare system (up to $1,000 for the most involved test).[4]<\/p>\n

InfoBionic is a start-up taking advantages of two powerful aspects of ongoing digital transformations \u2013 the rapid decrease in the cost and size of wireless devices [5] and the computational power available through cloud computing [6] \u2013 to create a breakthrough product called MoMe Kardia that helps doctors better diagnose arrhythmias and capture more of the value of their services.<\/p>\n

MoMe Kardia\u2019s wireless technology provides several benefits to its doctor customers and patient end users. The small device that patients can wear on their belts captures all the different data that the three existing arrhythmia diagnostic tools use, saving doctors from having to have different devices on hand.<\/p>\n

MoMe Kardia\u2019s device:<\/p>\n

\"MoMe<\/a>
Source: InfoBionics, \u201cThe System.\u201d https:\/\/infobionic.com\/the-system\/, accessed November 2016.<\/figcaption><\/figure>\n

Based on advances in wireless technology, the MoMe Kardia streams the data it collects over cellular networks, eliminating patients’ need to carry additional wireless transmission devices or return to the doctor\u2019s office to upload their data.[7] Additionally, doctors can wirelessly change the data collection settings of the device to switch between the three different diagnostic tools currently in use, again saving patients’ time from having to visit the doctor\u2019s office to swap out different devices.[8]<\/p>\n

MoMe Kardia\u2019s use of cloud computing power creates easy-to-use data-analysis for doctors, eliminating lots of previously manual labor and helping doctor\u2019s capture more of the value of the care they deliver. By using the cheap, abundant processing power available through cloud computing, InfoBionic can apply automated algorithms to analyze the data the MoMe Kardia collects.[9] This allows MoMe Kardia to integrate the different kinds of data collected in the different diagnostic modes in one integrated app, eliminating doctor\u2019s current need to make sense of three different reports for each diagnostic method. Additionally, the cloud-enabled algorithms are powerful enough to eliminate the current need for expensive ($1,000 a month), manual review of heartbeat data in mobile cardiac telemetry.[10] As a result, doctors can now get reimbursed for the \u201ctechnical\u201d part of mobile cardiac telemetry, capturing more of the value of the service. Currently, the vast majority of the insurance reimbursement for the $1,000\/month diagnostic treatment goes to the humans scanning the heartbreak data as it comes in, with only $30 shared with the doctor.[11]<\/p>\n

MoMe Kardia charges doctor\u2019s a monthly fee of around $500 for the device and data analysis. InfoBionic believes doctor\u2019s will be able to diagnose 1.5 to 2 patients a month with the device, replacing the three different devices doctor\u2019s currently use which have dramatically different costs (from $100 to $1,000) [12]. MoMe Kardia\u2019s 3-in-1 device with a flat fee is appealing to doctors given the move to value-based pricing in healthcare. [13] It allows doctors to more confidently forecast the cost of diagnosing arrhythmias when entering into contracts to provide those services.<\/p>\n

InfoBionic should aggressively apply machine learning, another digital transformation, to the enormous datasets MoMe Kardia is collecting. Machine learning could improve MoMe Kardia\u2019s algorithms to more accurately and quickly spot arrhythmias. This would leverage MoMe Kardia\u2019s unique position of having datasets from all three types of traditional diagnostic methods and having that data in scale on the cloud as opposed to more locally stored with individual doctor\u2019s practices. MoMe Karida also has a great opportunity to move from the diagnosis of arrhythmias to help with their management by better forecasting arrhythmias. By applying machine learning to its unique data, MoMe Kardia could help doctors better understand what may be triggering their patients\u2019 arrhythmias and how those triggers might vary across different patient populations.<\/p>\n

 <\/p>\n

797 words (excluding citations)<\/p>\n

[1] Zoni-Berisso, M. et al. \u201cEpidemiology of atrial fibrillation: European perspective.\u201d Journal of Clinical Epidemiology<\/em>. Volume 6, 2014, pg. 214.<\/p>\n

[2] Stuart, Mary. \u201cInfoBionic: Set to Disrupt Arrhythmia Monitoring.\u201d The MedTech Strategist.<\/em> Vol. 3, No. 12, pg. 47. August 24, 2016.<\/p>\n

[3] Ibid.<\/p>\n

[4] Ibid.<\/p>\n

[5] Iansiti, Marco and Lakhani, Karim R. \u201cDigital Ubiquity: How Connections, Sensors and Data Are Revolutionizing Business.\u201d 性视界 Business Review.<\/em> November 2014, 4.<\/p>\n

[6] Kim, Eugene. \u201cThis One Chart Shows the Vicious Price War Going on in Cloud Computing.\u201d Business Insider<\/em>. January 14, 2015. http:\/\/www.businessinsider.com\/cloud-computing-price-war-in-one-chart-2015-1<\/a>.<\/p>\n

[7] InfoBionics, \u201cThe System.\u201d https:\/\/infobionic.com\/the-system\/<\/a>, accessed November 2016.<\/p>\n

[8] Ibid.<\/p>\n

[9] Stuart, Mary. \u201cInfoBionic: Set to Disrupt Arrhythmia Monitoring.\u201d The MedTech Strategist.<\/em> Vol. 3, No. 12, pg. 49. August 24, 2016.<\/p>\n

[10] Ibid.<\/p>\n

[11] Ibid.<\/p>\n

[12] Ibid<\/p>\n

[13] Gerhardt, Wendy et al. \u201cThe road to value-based care: Your mileage may vary.\u201d Deloitte University Press<\/em>. March 20, 2015. https:\/\/dupress.deloitte.com\/dup-us-en\/industry\/life-sciences\/value-based-care-market-shift.html<\/a>, accessed November 2016.<\/p>\n","protected":false},"excerpt":{"rendered":"

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