Internet of Medical Things (IMoT)


Many healthcare firms and consumers have latched onto the Internet of Medical Things (IMoT) by way of wearables, such as FitBits and Garmin watches, referred to as “FitTech.” With over 2/3 of medical devices estimated to be connected over the next 3 years, IMoT is going to have a significant impact in Healthcare operational and financial processes. Let us examine the impact of IMoT on healthcare payers, health providers, and consumers.

A) IMoT and Payers: 

i) Underwriting: The first process comes to mind is Underwriting. By equipping consumers with IoT-enabled medical devices underwriters can better understand what an individual’s health looks like daily, rather than at long historical intervals. With this wealth of information at the helm for an individual, underwriters gain access to health data from periods of time that used to be non-existent in health records and claims. IMoT can enable underwriting for

  • improved bottom-line of the payer by better understanding what each individual new member will cost them
  • increased wallet share by preventing lower-risk members from being improperly marked as high-risk based on one-off health encounters
  • Optimized underwriters’ time spent on due diligence, especially avoiding unnecessary full medical underwriting (FMU.)

ii) Preventive Care: Preventative care is a perfect application of IoMT. Biometric sensors and other devices can collect real-time data from health plan members, help point to higher-risk metrics or lifestyle choices, and notify payers to get the correct members enrolled in prevention programs. Oe successful use case is Beam Dental. The Beam Brush tracks an individual’s tooth brushing habits (such as the dental habits of employees under their employer’s insurance plan) and allows their good habits to drive down the cost of dental insurance for their group. By activating members to take control of their health before chronic or acute health issues arise, payers will see success in loss prevention as well as a happier (and healthier!) member base.

iii) Claims and billing efficiencies: IoT can aid in cumbersome tasks that waste administrative hours by leveraging AI led solutions, such as determining whether a claim should be accepted or rejected for minor claims or processing payments. By freeing up administrative time from these tasks that can be automated, payers can invest more in programming for their members to drive focus towards prevention leading to savings in administrative costs and savings in claims payments from healthier members.

B) IMoT and Providers:

IMoT has the potential to facilitate remote patient care to optimizing hospital operations to streamlining data management, healthcare providers can leverage the lucrative potential of IoT. These use cases are elaborated below.

i) Improved hospital operations: IMoT can be introduced and ramped up to optimize a hospital’s daily functions and cut unnecessary costs. Tracking medical assets within a facility is a good use case. Every year, millions of dollars bleed from hospitals from lost or stolen equipment. By attaching sensors (e.g., RFID or Bluetooth) to equipment, hospital staff can track the exact locations at any point in time, allowing for better oversight. This can solve the problem of lost equipment, reduce theft, and even track overall use of equipment. The life of medical equipment varies greatly based on the frequency of use. By tracking movement over the life of a piece of equipment, hospital administration can get a better idea of when to replace or schedule maintenance to avoid periods of time where equipment is unusable.

A second IMoT use case is in intake or discharge processes. With IoT, unobtrusive sensors can be placed in patient wristbands and staff badges better to track how quickly patients flow through different areas of the hospital (such as pre-op rooms to the operating room) or how efficiently staff attends to a given patient. This can remove backup from current bottlenecks in flow at the hospital, including but not limited to Emergency Department wait times, intake, discharge, and shift changes.

ii) Interoperability and Data Monetization: IMoT at a basic level improves existing systems for providers. For example, biometric devices and sensors are often system-agnostic and can connect through APIs to multitudes of EHR systems. If a patient has doctors in multiple health systems, their disparate EHRs (and therefore doctors and care plans) can be updated accordingly. The IMoT combined with AI/ML and NLP can nurture the massive loads of HC data. By relying on IoT-enabled technologies, providers will no longer deal with unusable, unstructured data but rather well-organized and insightful data systems. The world of well-managed data in hospitals and health systems opens up with the adoption of forward-thinking technology. Doctors can better tailor care plans to patients’ specific needs based on historical data of like patients and avoid oversight of potential complications such as contraindications.

iii) Expanding remote care revenue streams: IoMT eases the implementation of remote patient care. With IMoT doctors can help patients purchase and set up remote equipment to measure biometrics, provide care, and talk face-to-face over the internet i.e. telemedicine. Doctors then are able to receive the data they need to adequately modify care plans without requiring a patient to walk into the office as well as have more frequent communication and therefore a better understanding of a patient’s day-to-day health status. IoMT not only allows for better continuous care but also boosts patient satisfaction and engagement. Patients that spend more face time with their providers tend to have better relationships and therefore better patient satisfaction—a critical component of healthcare with more and more models shifting to value-based reimbursement from health payers.

IMoT implementation roadmap:

While the Internet of Medical Things has the potential to fuel HC growth, IMoT implementation sought to be a rocky path. But approaching IMOT implementations with a pragmatic approach leads to a better navigation path. Let us evaluate some basics steps of IMoT roadmap.

  • Identifying Healthcare organization business goals to build IMoR ecosystem
  • Develop a viable and convincing business case to roll-out IMoT
  • Next coming up with a clear vision and goals to realize with connecting medical devices
  • Big-Bang approach may lead to burn-out, and hence identify pilots or PoCs od IMoT success areas
  • Take an iterative approach to reiterate the ideation process and move forward with an implementation initiative

Sounds generic! That is the stepping stone for IMoT implementation. Imagine that healthcare companies manufacture more than half a million different types of medical devices, including wearable external medical devices like insulin pumps, blood glucose monitors, etc, implanted medical devices – implantable cardioverter defibrillator devices, and stationary medical devices – scanning machines, etc. to name a few. Most patient interactions with the HC system involve the use of medical equipment and devices. IMoT brings these interactions to life. Hence taking an incremental approach is the only way forward.

The true implementation of IMoT involves, “developing an in-depth understanding of end users”, “defining funding, business and operating models”, “clearly understand device interoperability requirements”, “embed security at the core”, “ensuring regulatory compliance”, “more importantly attract talent and build digital capabilities”, “improve the adoption of medical technology at scale and with trust”, and finally “create an ecosystem of seamless partnerships”.

IMoT Solution Providers:

Colleagues on this forum have highlighted many advantages of IMoT like cutting emergency room wait times, remote health monitoring, ensuring critical equipment availability, improved drug management, optimized staffing and workflow, better diagnoses, better outcomes with fewer false alarms, etc. As IMoT value proposition is gaining more traction, many solution providers are offering products and solution to tap this value.

With an estimated market value for IMoT technologies >$150 billion in over next 3 to 4 years, Philips, Siemens, GE Healthcare and Medtronic are currently leading IoMT technology investments, with Philips primarily dealing with cardiac monitoring, remote patient communication devices and sensor-related products, and GE and Medtronic instead focusing on cloud-based technologies in existing monitoring devices, implants, and cardiac pacemakers.. Listing below few examples.

  • IMoT and Telehealth: Health Net Connect offers various remote patient monitoring packages that monitor conditions like CHF, COPD, diabetes, and hypertension with devices like BP/BG monitors, Handheld ECGs, pulse oximeters and spirometers. Not only is this technology leading to reduced costs as patients handle everything in-house, but by eliminating the need to visit health professionals and vice versa, it’s also improving their overall patient experience.
  • IMoT and Drug Management: Proteus Discover is a health company that measures medication treatment effectiveness and helps physicians improve clinical outcomes and patients reach health goals through sensor-embedded pills like the one mentioned above. Once the ingestible sensor-containing pill reaches the stomach, it sends a signal to patch the patient is wearing, which monitors each time a pill is taken, as well as their general rest and activity patterns. another example is, Abilify MyCite approved by the U.S. by the Food and Drug Administration
  • IMoT and Medical Device Monitoring: e-Alert from Philips are also ensuring that critical hardware is always accessible, and if something like a breakdown does happen, staff members will be immediately alerted.
  • Siemens IoT solutions for the medical device industry are powered by combining big data with digital twins, a virtual representation of actual devices, moving in tandem across the lifecycle and connected by digital threads. By connecting virtual development and production planning environments with real support and lifecycle production data, Siemens equipping med-tech organizations with the transparency and advanced analytic tools required to gain a competitive edge using big data.
  • eVisit is a telemedicine platform that enables doctors to conduct examinations and prescribe remedies for their patients by remote.
  • Amiko.IO focuses on providing products for respiratory disease management, complete with an AI-powered platform.
  • InfoBionic’s MoMe Kardia provides remote monitoring of cardiac arrhythmia.
Challenges implementing Healthcare IoT / IMoT:

HC firms have to overcome a few key challenges ranging from data security to legacy infrastructure that may hinder health care IoT initiatives. Alongside these evident challenges, IMoT should address the following areas for widespread adoption.

  1. Health data explosion and sensitivities: HC is one the largest sector contributing to massive data creation. HC organizations to use IMoT technology effectively have to address growing data storage needs. As well HC has to be exceptionally careful to treat patient data from IoT devices according to federal and state regulations. The flood of data created by the IoT gadgets and devices used in the HC industry could also cause unforeseen problems if organizations are not equipped to handle it properly and verify its quality.
  2. Lack of EHR system integration. While the data that is collected from IMoT devices can include a patient’s vital signs, physical activity that information does not typically travel to an EHR system and, in most cases, is not centralized or made easily available to providers. This limits the information’s value since it is not always presented to the provider in a clinical context.
  3. An increase of available attack surfaces with IoT devices: IMoT devices explosion in health care present concerning vulnerabilities as device use rises, so does the number of ways hackers could infiltrate the system and mine for the most valuable data. Hackers could potentially learn about how a connected medical device operates by getting into the system and reading its error logs. The knowledge the hackers gain could facilitate breaking into a hospital network or making devices publish incorrect readings that influence patient care. It is high time for vendors, providers, and manufacturers’ to collaborate to reduce patient risks by closing the gaps that can form between the layers of an IMoT system by reinforcing standards and normalizing secure protocols. It’s not possible to know all the cybersecurity risks health organizations may face. Nonetheless, facilities planning to implement IoT technology must take care to increase awareness of existing threats and understand how to protect networks and gadgets from hackers’ efforts.
  4. IMoT data in silos due to interoperability challenges: Patients are likely to collect different sets of data when using different medical devices depending on each device’s purpose and, in some cases, the ordering physician. IMoT data alone may not be as meaningful if it is not within the context of a full health record. With the lack of wider adoption of adequate interoperability, data from different IMoT devices may remain locked in each individual system and lose its potential value to the rest of a patient’s care team.
  5. Data security causes concerns in the IMoT implementations: From the time that the data is collected at the device level to the point that it is transmitted over to its final destination, securing that information is critical and is required under HIPAA. But with the lack of common security standards and practices, many health IT professionals have concerns about the risks associated with IMoT device tampering and data breaches.
  6. Plan for ecosystem needs to be successful: According to a recent Cisco survey, ~60% of projects encounter trouble at the PoC stage or shortly thereafter. The study suggested that utilizing external partnerships (e.g. platforms) was a crucial factor for those organizations that achieved successful implementations. When it comes to the starting small and prioritizing projects that align with their most prominent business objectives or patient needs is key to the success.
  7. Overcoming legacy infrastructure challenges: Outdated infrastructure is a known fact in HC. Even though retrofitting can breathe new life into aging infrastructure, truly taking advantage of IoT is tricky if a facility’s infrastructure is outdated. Hence using IMoT in ways that make sense for the needs, budgets, and infrastructures of HC organization and having robust plan to ramping up resources to fill the gaps is the key to the success of IMoT implementations.
  8. Stringent high availability and near-zero tolerance for failure: One of the common use of IMoT technology in HC is to apply it to patient monitoring systems. While it is handy to take that approach, unlike other IT systems (ex: websites), these devices typically cannot go through planned periods of downtime. Hence, updates have to occur seamlessly as people use the monitoring devices. For the hospitals to depend on IMoT-enabled supply cabinets to track resources reducing inventory management issues, IMoT devices devices are to be audited correctly eliminating human errors.

AI in Healthcare

Healthcare fueled by AI/ML/DL:
AI Use Cases in Healthcare:
Roadmap to Implement AI in HC
Commoditizing AI/ML in Healthcare:
AI/ML Impacts on HC. AI and machine learning are already delivering value in HC. The following are high impact areas.

Blockchain “Potential Value” in the Healthcare Industry

BC in HC

Visualize the Healthcare ecosystem comprising of patients, payers, providers, pharma/bio majors, and medical device companies. The Blockchain Technology combined with other relevant digital forces can augment the right set of capabilities in the Healthcare Ecosystem. The blockchain technology alongside Electronic Health Records, IMoT (Internet of Medical Things), Healthbots, AI/ML, Cloud and Analytics can create the capability foundation for the healthcare industry.  The blockchain bundled capability engine thereby enables the four drivers as described below.

  • Consumerization: Transforming from wholesale to retail healthcare. The patient or consumer now can expect the same experience in healthcare like in all other parts of their “consumer life.” Blockchain can enable a radical change in driving patients to take advantage of connected technologies, social tools, and information activities in their own health and that extends further into the broader marketplace.
  • Personalization: Healthcare industry has historically treated patients en masse. But the move from the group to the individual is inevitable now. Blockchain empowers health players to build loyal relationships with consumers offering more choices.
  • Diagnosis & treatments: Blockchain can create a single source of medical truth of patient that can’t be tampered making the doctors better at their jobs – quicker, more accurate, and fact-based expediting the quality of diagnosis and treatment.
  • Communications: Enabling doctor’s effective and easy communication with patients for improvising care coordination is another pertinent role of blockchain technology in healthcare.

The above drivers collectively are positioned to deliver the following outcomes to healthcare ecosystem.

  • Patients: Improved experience with better care coordination
  • Payers: Shift from B2B to B2B2C models
  • Providers/ISVs: Better usability enabling on-the-go services and health predictability

To better contextualize Blockchain Technology in Healthcare ecosystems, the relevance of technology for Patients, Payers, and Providers is discussed below.

I. Blockchain prominence for Patients:

Patients can benefit from improved experience from better health coordination. With blockchain technology, patient health records can be cryptographically secured and shared among healthcare stakeholders, increasing interoperability in the ecosystem. Use cases for blockchain are getting started with projects that reduce duplicative work but eventually shift to a system where the patient’s control access rights to their data. The following is one of the paths of evolution of blockchain in healthcare,

  • In short term it is more of a closed consortia, PoCs, managing providers information, bringing drug supply chain on the blockchain, but not really porting patient data on the blockchain.
  • In the medium term, systems can scale with permission of stakeholders and handle some patient data. Applications include claims management, payments, and prior authorization, health information exchange & research data, and trial design data etc.
  • But over a long term, a patient-driven blockchain system with master health records and access rights in the hands of patients is a definite possibility.

To design a robust blockchain solution, the architecture should store and scale voluminous transactions, urgent data, and more on a blockchain, while larger data storage needs could be met by private repositories. The bundled On-Chain and Off-Chain solutions can be built to solving both scalability and data sensitivity needs. A typical blockchain solution for healthcare patients’ data can be described as follows.

Data is generated about a patient, a doctor’s visit occurs etc. Transactions are recorded on a public, view-able blockchain, which also designates the location of the data. The data is stored “Off-chain” in private data repositories. Patients give a third-party access to their records via public/private keys. Data is located, decrypted, and retired from storage on-demand.

Blockchain could bring patients to the center of the healthcare ecosystem by giving them the power over one of their most valuable resources – data

II. Blockchain driven Improvements for Payers:

The blockchain is driving the transformation in Payers and/or Health Insurance space to reimagine business models progressing from BB to B2B2C channels. As per the analyst reports, 5 to 7% of claims are denied due to inaccurate or lack of information. Imagine blockchain technology offering an opportunity to automate the claim process and simplify the administrative processes to reduce transaction costs and minimizing frauds.

How Blockchain technology does this is by leveraging the consensus with smart contracts, maintaining a benefits database, determining patient insurance for self-execution with SOPs driving terms and conditions. This will potentially bring in a discipline of pay for outcomes and incentive-based behavioral health programs that offer peer-to-peer insurance models. Imagine a day where patients have a peer-reviewed and/or a peer-adjusted claims system.

III. Blockchain-based Collaboration for Providers:

Healthcare providers could be hospitals, medical device companies, pharma or bio majors and many more. Let us examine the following opportunities.

  • What if blockchain enables a multi-fold increase in medical device makers ability to bring their devices onto a medical IoT platform solving the current data privacy and security concerns? Blockchain can enforce medical device identity management by promoting IMoT and as well cryptography techniques can offer an additional layer of trust to minimize cybersecurity threats for medical devices. Blockchain also ensures patient privacy by proving secure and selective access to their health data.
  • Serialization and counterfeiting are few of critical issues pharma supply chain faces today. It is a multi-billion dollar problem to solve. Blockchain ability to create a chain-of-custody log of a pharma value chain can enable drug manufacturers to track each step of the supply chain at the source by raw martial or constituents and their origins. Blockchain also offers the technological feasibility to automate serialization process across the pharma supply chains

Blockchain technology has the potential to exponentially add value to the healthcare ecosystem offering significant cost savings, enforcing privacy and security, creating a chain of custody for pharma value chain, improving collaboration, and simplifying the claims processing.

I welcome further discussions on this topic via email

Refer to related blog posts below:

Combating Counterfeiting with Blockchain Technology

Healthcare Simplified with AI


Healthcare industry is a front runner in creating business value applying AI followed by Automotive and Financial Services industries.

In simple and direct interpretation, Artificial Intelligence fundamentally helps either completing tasks at a basic level to making decisions at advanced level. AI help in completing tasks is twofold, either totally automate the tasks without human intervention or help humans in completing the tasks in faster and effective ways. Similarly AI can enable decision making in fully autonomous ways with NO to very little human involvement or amplify the human decision making. Let us examine these scenarios in context of Healthcare industry encompassing the four dimensions of applied AI in healthcare.

  1. AI completing healthcare tasks without humans: Chatbot to connect scheduling Electronic health record systems and automate the confirmation and scheduling of patients
  2. AI aiding humans in completing tasks: AI powered diagnostics which helps humans/users in analyzing patient’s unique history as a baseline against which trigger a possible health condition that is in necessity of future investigation and potential treatment.
  3. AI augmenting decision making in healthcare: AI is able to provide clinicians evidence-based treatment options which is kind of a data driven diagnosis. AI as well is aiding in virtual drug development process.
  4. AI autonomously making decisions in healthcare: Will humans let an AI’led robot perform your surgery by itself? As AI proves dealing with complication, it can aid intelligent implantation with improved health benefits and lives.

AI reach in healthcare is everywhere from answering specific patient queries to intelligent implantation. It is evident from recent developments including,

  • Google’s DeepMind platform: Detecting certain health risks thro’ data collected via a mobile app or analysis of medical images to develop computer vision algorithms to detect cancerous tissues
  • Intel’s Lumiata: Using AI to identify at-risk patients and develop care options
  • IBM’s Watson: AI enabled Oncology alongside Cleveland Clinic or work with CVS Health on AI applications in chronic disease treatment, etc.
  • Microsoft’s Hanover project: Medical research to predict the most effective cancer drug treatment options or medical image analysis of tumor progression and development of programmable cells
  • Refer for other examples @

There are a number of startups entering the healthcare AI space has increased in recent years. AI systems are getting involved in full spectrum across Healthcare industry encompassing providers, consumers, payers and pharma and PBM players. The future seems to be very promising as the potential of commercial benefit of applying AI in healthcare will be substantial.

With the earlier posts in this space, I have been discussing on various topics pinpointing the application of AI in healthcare and listing below details for reference.

  1. Intelligent Algorithms in Healthcare
  2. AI in Pharma
  3. New age Healthcare evolution fueled by Digital reality

Intelligent Algorithms & Healthcare


Healthcare industry is generating enormous amounts of structured and unstructured data. Finding innovative insights from data is of particular interest to Providers to incentivize predictive and preventive health management. This leads to healthcare industry focus on breaking data silos and leverage intelligent algorithms backed with advance analytics to create value. New age machine learning algorithms including natural language processing, pattern recognition, and deep learning are enabling better healthcare. I would like to bring up the following developments,

  • Algorithms for Cybersecurity:  Patient privacy and Cybersecurity are key focus areas for every healthcare provider. Sophisticated algorithms are aiding human skills with an ability to patrol security perimeters with more sensitivity and responsiveness. Algorithms identify patterns of normal usage and alert or flag events that are out of the ordinary by calculating a risk score for specific events as they happen based on the similarity or not to the normal behavior observed for the user performing the specific events. Supervise machine learning can cull things out that are less risky or classify them in terms of making multiple categories, like in terms of malware families. Getting Healthcare industry is in an early stages of implementing cognitive technologies ensuring security and reduce the rising threat of ransomware.
  • Algorithms for Unstructured Data (EHR to MRI Data): Extracting usable meaning from voice recordings of patient interactions, PDF images of faxed lab reports, and free-text HER are critical to an effective healthcare provision. This is where algorithms like natural language processing (NLP) can turn images of text into editable documents, extract semantic meaning from those documents, or process search queries written in plain text to return accurate results.
  • Algorithms for Clinical Support: Another important focus area is an ability extract meaning from large volumes of free text for better clinical decision support. Algorithms are helping ranging from precision medicine techniques to augment physician-guided diagnosis. Identifying and addressing risks quickly can significantly improve outcomes for patients with number of serious conditions, both clinical and behavioral. The silo nature of data organization and analysis limits the insights that doesn’t tell us a great deal about whether or not the patient has actually gotten better as a result of accessing that care. Semi-supervised and unsupervised machine learning algorithms like Clustering & Dimension Reduction can help improve the processes and dig deeper into that data and all the other variables that impact an individual’s life.
  • Algorithms for Pathology and Imaging: Healthcare organizations in improving patient outcomes is relying on Improved imaging analytics and pathology. Machine learning can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses. Google research published during March 2017 – a new approach to imaging analytics driven by machine learning algorithms can identify metastasized breast cancer with sensitivity rates that exceed other automated methods and even rival human pathologists, is one of the best examples.

21st Century Healthcare industry has a tremendous opportunity to seize with smart usage of data science breakthroughs and evolution of intelligent machine learning algorithms.

AI in Pharma


Artificial Intelligence is leapfrogging in healthcare and pharma field with enabling technologies spanning across research and drug development, clinical trials, pharmacovigilance, supply chain, health records, compliance, data privacy and security.

AI is a study acknowledged to imitate human knowledge into PC innovation that could help both specialists and patients in the accompanying way, by giving a research facility to the examination, representation, and classification of restorative data, by concocting novel devices to bolster choice making and research, by incorporating exercises in medicinal, programming and psychological sciences lastly, by offering a substance rich order for future logical restorative group.

To convey a medication from introductory revelation to the hands of patients takes more than a decade time and billions of dollars. AI can significantly reduce the lead times, and also cut the costs significantly by 30% to 50%+. Early adopters like MedRespond is offering initial uses case by consolidating AI and streaming media showcasing how the company permits clients to sort in their inquiries, in their own words, and the framework chooses the pre-recorded video that best answers their inquiries. This process is helping MedRespond in both patient recruitment and retention.

Precision remedy is an area where AI is making inroads in getting the right treatment to the right patient at the ideal time. Examination of illness cells routinely takes years – yet the advent of AI’s fake awareness is that it works speedier than any human could. Today there are diverse associations who are leveraging AI, for example “Berg” a fast growing biotech, lies at the nexus of artificial intelligence, precision medicine and big data. Its AI-based drug discovery platform roots through reams of patient data to find and validate disease-causing biomarkers and efficiently craft therapies based on the newly found data.

In another example, AI is bolstering pharmaceutical adherence. AI Cure is a start-up that uses AI on patient’s cell phones to affirm solution ingestion support in clinical trials and high-chance populaces. AI Cure’s HIPAA-agreeable programming catches and dissects proof of drug ingestion. A cell phone’s camera is utilized to comprehend whether patients took the medicine effectively.

AI is also predicating the patience drug resistance, and enabling patients to become dynamic members of clinical trials. A year ago, IBM declared that the pharmaceutical mammoth Johnson and Johnson and contender Sanofi would participate in a joint effort with IBM Watson’s Discovery Advisor group. J&J will likely educate the supercomputer to peruse and comprehend experimental papers that contain clinical trial results, and afterward create and assess medicines and different medications. While this may not sound excessively energizing, it could have inconceivable outcomes on how pharmaceutical organizations do similar viability examines.

Moving to Healthcare, with most of today’s U.S. adolescents, adults and seniors owning a smartphone, they are likely to have access to an intelligent personal virtual assistant on their device. The likes of Cortana and Siri are backed by powerful systems with robust AI capabilities. These systems have the potential to provide tremendous value when combined with healthcare apps. Also, one of the new areas of AI that is beginning to gain adoption is in the field of customer service, and healthcare bots are likely to be available soon as part of what healthcare providers offer.

From the patient privacy and compliance perspective, the advent of AI is demanding technologists to ensure preventing cyber-security attacks, and develop advanced AI security monitoring solutions. AI could revolutionize compliance by offering software platforms that promise to automate otherwise routine tasks and improve upon fraud detection audits, anti-money laundering protocols, and know-your-customer screening. Keep in mind that tools and technologies are enablers and are not the foundation of a robust monitoring program. As pharma industry move toward the use of compliance intelligence, behavioral analytics, and Big Data, first we need to ask if more data feeds will lead to more alerts or even more noise, and whether analysts are just going to get buried in this noise. AI definitely promising a helping hand to humans to manage data noise.

Being technology watch dog closely following the progression of AI in pharma transformation, combined with experience around developments in this area, I welcome valuable inputs in collaborating and triggering thought provoking discussions to trend-spotting and lead the path to strike a common ground in crystalizing positive impacts of AI in future of pharm and healthcare.

New age Healthcare evolution fueled by Digital reality

Digital Health

As Healthcare is shifting from being reactive to proactive, Digital is progressing that move a step further through intuitive aids that anticipate patient problems before they happen. Healthcare IT companies are attracting a lot of interest as a result of this. As per Analysts reports the digital healthcare industry has reached a new investment high in 2015. The past year saw nearly $5.8B invested, a two digit increase from the breakout year in 2014 (which itself saw more than double the funding over 2013). 2015 saw more than 1,000 entities that made an equity investment in at least 1 digital health company, up more than a 361% from the 234 that invested in digital health in 2010. The federal government is also on the rolls to spend up to $29 billion in incentives to encourage healthcare players to take advantage of digital investments.


Digital is contributing to enhance the outcomes of entire value chain of Healthcare touching all players in the ecosystem.

  • Providers: Achieving “meaningful use”, which is the use of certified Electronic Health Records (EHR) technology to achieve health and efficiency goals.
  • Payers: Enable payers to shift from B2B to B2C model of business for new consumer market that survive and thrive in this new reality
  • Patients: Transforming healthcare delivery with promising care coordination and improved patient experience
  • ISVs: Meeting healthcare organizations demand of better quality modules and features to allow enhanced usability, access to data in the cloud and on the go, and the liberty to analyze data for predicting patients’ future health


Just how is digital technology enabling healthcare evolution? Here are few drivers helping to achieving the above healthcare outcomes.

  • CONSUMERIZATION: Healthcare is transforming from wholesale to retail. The patient or consumer, now expects the same experience in healthcare like in all other parts of their “consumer life.” This is a radical change is driving Patients take advantage of connected technologies, social tools, and information resources in more active role in their own health, and it extends further into the payer market. Consumers are no longer limited to the single plan offered by their employer – they have more options than ever on the open insurance market. To compete in this new marketplace, payers and providers need to rethink their offerings to give tailored experience to patients considering to provide plans that include performance incentives, transparency, and flexibility. The consumerization in Healthcare defines the problem statement for the Technology. Technology should enable industry collaboration and pricing transparency, increase hospitals use of business intelligence tools to derive patterns and consumer trends, solve information asymmetry between the medical professional and the patient and help overcoming the dichotomy between consumer and payer
  • PERSONALIZATION: Healthcare industry has historically treated patients en masse. But the move from the group to the individual is inevitable now. Today’s healthcare consumers expect to be able to engage in a highly individualized, personalized manner, whether it’s in the services and treatments they receive, or the way they pay for that treatment afterwards. Technology should lead the personalization in Healthcare by building consumer centric CRM solution driving loyalty and providing personalized care is a key factor for sustaining long term growth for a healthcare organization. As well deploying advanced analytics will enable us to better understand which treatments deliver the best outcomes and to tailor treatment, messages, and services, as well as provide early alerts. And an increased emphasis from payers on branding themselves and sharing personalized, engaging content will help to differentiate them and build loyal relationships with consumers who have more choice than ever.
  • DIAGNOSIS AND TREATMENT: The belief among industry practitioners is that Technology will replace 80% of what doctors do. Data-driven healthcare won’t replace physicians entirely, but it will help those receptive to technology perform their jobs better. Lifecom showed in clinical trials that medical assistants using a diagnostic knowledge engine were 91% accurate without using labs, imaging, or exams. A MassGen study found that 25% of the time, a medical record for patients who wound up with ‘high risk diagnoses’ had ‘high information clinical findings’ before a physician finally made the diagnosis — in other words, there was a significant delay that might have been avoided had a clinical decision support system been used to parse the notes! New technologies will make the receptive doctors better at their jobs – quicker, more accurate, and more fact-based. There is a tremendous opportunity in the influx of data that has never before been available. Once we have a large enough dataset, and an addressable database of research studies, we’ll be able to identify patterns and physiological interactions in ways that weren’t possible before. Another development worth mentioning is IBM invention of the computer “Dr. Watson.” the supercomputer to help physicians make better diagnoses and recommend treatments. Doctors could potentially rely on Watson to keep track of patient history, stay up-to-date on medical research and analyze treatment options.
  • COMMUNICATION: Enabling doctor’s effective and easy communication with patients for improvising care coordination is another pertinent role of technology in Healthcare. One example to provide a perspective here is, Science Applications International Corporation (SAIC) development of Omnifluent Health, a translation program for doctors and others in the medical field. The suite of products includes a mobile app that lets doctors speak into the app — asking, for example, if a patient is allergic to penicillin — and translate the message instantly into another language. Given that there are 47 million U.S. residents who don’t speak English fluently, the program could be a boon for doctors who would otherwise need to rely on translators and medical assistants to communicate with their patients.


Building healthcare digital capability backbone encompassing all players of value chain – payers, providers and ISVs is critical in adopting to digital reality and realizing the true benefits. The key capabilities and their high-level usage patterns is discussed below.

  • Internet of Medical Things (IoMT): IoMT is enabling remote patient monitoring of consumers with chronic or long-term conditions, tracking patient medication orders and the location of patients admitted to hospitals, and patients’ wearable devices, which can send information to caregivers. Telemedicine which is gaining momentum also use IoMT devices to remotely monitor patients at their homes.
  • Electronic Health Records (HER): EHR is a digital version of a patient’s paper chart. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users. While an EHR does contain the medical and treatment histories of patients, an EHR system should be built to go beyond standard clinical data collected in a provider’s office and can be inclusive of a broader view of a patient’s care
  • Cloud – Big Data – Analytics: For healthcare industry, the cloud seems a natural fit. From EHRs to data storage to software as a service (SaaS) capabilities, cloud-based products offer lower costs, greater capacity for scalability, dedicated service and support, and near-continuous uptime. But huge volumes of clinical data added to EHRs at every moment cannot be quickly and thoroughly translated into concrete, timely clinical decision support (CDS) information due to the limited computing resources of most healthcare organizations. Cloud-based analytics-as-a-service tools alleviate those pressure points and provide real-time CDS capabilities that will improve the quality of patient care by “combining the on-demand aspects of cloud computing with the democratization of information enabled by big data analytics.”
  • Wearables & Bio-sensing: A growing number of mobile apps and gadgets aim to help people stay active, sleep well and eat healthy. Among them are Fitbit, a pedometer that tracks daily sleep and activity and uses social networking and gaming to motivate its users. Lark is a silent alarm clock and sleep monitor that tracks and analyzes a person’s quality of sleep over time, offering suggestions to help the person get better rest. And there are dozens of calorie-counting, food-monitoring and menu-tracking apps to aid the diet-conscious.
  • healthbots: There is a growing experimentation to using robots as health aids for the elderly. People are opening up to the idea that robots and drones can be used as a force in healthcare. As aging population grows, so too will use of robotic health aids or ‘healthbots’.
  • Deep Learning and Artificial Intelligence (AI): AI finds purpose in healthcare. IBM’s Watson made a splash in 2015, and catalyzed the concept of AI in healthcare. These innovations are transitioning out of the lab and into the spotlight
  • User Experience (UX): UX focus has acted as an important opening salvo in the integration of user-centered design principles into the healthcare industry processes, products, and workflows.
  • Digital Channels: Omni channel digital capabilities are making healthcare more accessible, cost-effective, and engaged. Omni-channel healthcare opportunities empower people to seek care from anywhere and at any time, from their channel of choice (smartphone, tablet, computer, in-person). Importantly, it has the potential to improve overall health outcomes by minimizing a few key constraints that prevent people from receiving proper care — time, money, and a lack of engagement or knowledge

In summary, the future of healthcare is bright and exciting. Enormous strides have been made to move to a more personalized, meaningful model of care. New digital technologies and analytics are changing the way healthcare is delivered, and it’s important that healthcare players keep up this momentum to meet the needs of today’s patients.