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.