By using big data technology along with machine learning and artificial intelligence, healthcare industry can make accurate decisions, significantly improve operating efficiencies, and do away with unwanted costs.
Recent advancements in medical technology have dramatically increased the quantity of data available in the healthcare industry. Patient reports, genomic data, electronic medical records, etc., give a wide range of insights for discovering therapies and drugs that can provide high value at low costs. But, if technology continues to advance without corresponding improvements in the ability to digest and interpret data, the technological innovations are effectively wasted. For example, big data has brought an explosion of information in the healthcare field. Technologies that were previously adopted to analyze genomics, DNA, and cancer took more than a decade to understand and analyze the actual composition of DNA and the patterns of the data.
New analytics and artificial intelligence (AI) tools and techniques can now be used to analyze chronic diseases for prevention and cure. Machine learning (ML) in healthcare is providing a promising avenue for ensuring that technological innovations in healthcare do not go to waste. Trends also point out that the comprehensive onboarding of cost-effective cloud solutions will proliferate the adoption of HMIS and EMRs. This strategy will make healthcare services easily accessible, and ensure swifter response times and patient convenience, besides reducing the scope of errors. The healthcare industry is billed to undergo tectonic changes with the boom of startups that are using clinical data to gain insightsinto effective patient care. By using big data technology along with ML and AI, healthcare industry can make accurate decisions, significantly improve operating efficiencies, and do away with unwanted costs.
Data mining applications can be developed to evaluate the effectiveness of medical treatments. By comparing and contrasting causes, symptoms, and courses of treatments, data mining can deliver an analysis of which courses of action prove effective. To aid healthcare management, its applications can be developed to better identify and track chronic disease states and high-risk patients, design appropriate interventions, and reduce the number of hospital admissions and claims. The medical institutions apply this on their existing data base and can uncover latest, valid, and useful knowledges. More over this data mining helps in better medical policies like strict sterilization and sanitation, vaccination planning etc.
Data mining also can be used for hospital infection control or as an automated early-warning system in the event of epidemics. A syndromic system, based on patterns of symptoms, is likely to be more efficient and effective than a traditional system that is based on diagnosis. Pharma companies can also benefit from healthcare data mining too. By tracking which physicians prescribe which drugs and for what purposes, the companies can decide whom to target, show what is the least expensive or most effective treatment plan for an ailment, help identify physicians whose practices are suited to specific clinical trials, and map the course of an epidemic to support pharmaceutical salespersons, physicians, and patients. Data mining is also becoming popular in various research arenas due to its wide variety of applications and system of methods used to mine information that are useful in correct manner.
As a developing country, India is undertaking significant initiatives to encourage the use of analytics across every sectors and industries, but in healthcare, trends suggest the country has lacked behind in adoption of analytics. Governing bodies and policy makers are gradually acknowledging the potential healthcare analytics proposes in terms of generating insights fromclinical, financial, and operational databy leveraging visual analytics.
Data analytics is healthcare has the power to democratize and decentralize healthcare delivery through data, providing absolute transparency as well as accuracy. It has institutional as well as patient-centric applications such as discovery of the most suitable choice of doctor, hospital, or treatment to more advanced applications for healthcare organizations such as accessing and applying latest research in daily practice. Data mining and analytics paired with cloud technology enables ease of sharing and accessing patients’ data, which leads to efficient management of time for doctors, thus, improving the overall standard of operations for a hospital.
For hospitals,healthcare analytics can impact multiple areas from customer acquisition to operational efficiency to clinical delivery. It can be the backbone of marketing teams to target and retain the right type of customers, help operations teams understand where the hospital truly excels in and where it needs to work on to achieve high-cost efficiencies. For the pharmaceutical sector, even though India is currently a generics market, there is immense potential to fundamentally rethink how real world evidence can power R&D, clinical trials, etc.
Companies are currently investing their efforts towards mining path-breaking insights by analyzing the existing volumes of healthcare and patient data available. Though the current funding landscape is nascent, few companies such as SigTuple, Niramai, QirQI, Qure.ai, and Predicle Health are making significant inroads.There is a real buzz around India on the absolute benefits analytics and data discovery can deliver to an organization.Wockhardt Hospitals Ltd., one of India’s leading super-specialty care, deployed theQlik Platformas a key tool in its journey on establishing a companywide adoption of data analytics. Similarly, a health data science company called Lumiata, is working on developing predictive analytics tools for discovering insights and making predictions related to various healthcare aspects, such as symptoms, procedures, diagnosis, medications, etc. for patients.
While data analytics holds a lot of promise, it also faces some challenges in the Indian ecosystem. Firstly,the talent needed in organizations to leverage data analytics is in limited supply.So any analytical solution needs to account for this and have a truly world-align usability for business users and offer shrink-wrapped solutions that demand little by way of deployment efforts.Secondly, most healthcare organizations including hospitalsspend less than 1 percent of their budget on software technologiesas they have not seen serious business value generated from such initiatives in the past. This will slowly get reversed as they start seeing tangible value.
As in any nascent industry, adoption will be gradual, beginning with early adopters and then to mass market. But there is no doubt that the field of health data analytics is one whose time has come and will create immense value to the entire ecosystem in the next decade.
In this digital era, healthcare industry is being transformed by the advancements in machine learning (or deep learning). ML is an extended version of AI that makes use of computer algorithms that do not require an input and can explicitly perform on their own by making use of high-dimensional data. The actions and outcomes of ML programs are highly scalable and have the capacity to alter and modify when exposed to a new data or information. Although the industry was slow in adopting this technology, it is now rapidly catching up, to provide successful preventive and prescriptive healthcare solutions.
By harnessing enormous big data, ML is now being used in healthcare to provide superior patient care and has resulted in improved business outcomes. Today, many major companies and startups, including Enlitic, MedAware, and Google, have launched massive projects focused on improving ML and bringing it to the healthcare system. More recently, current ML algorithms being tested and developed include k-nearest neighbors, naive and semi-naive Bayes, look ahead feature construction, Backpropagation neural networks, and more. Google has developed a ML algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithmto identify skin cancer. A recent JAMA articlereported the results of a deep machine-learning algorithm that was able to diagnose diabetic retinopathy in retinal images.
Moreover, Pathway Genomics is working on creating a one-of-its-kind blood test, which can determine whether early detection, or prediction for that matter, of certain types of cancer is possible or not. Oncologists today are making the most of IBM Watson to improve the survival rates of cancer patients, while also helping them reduce the overall costs of their treatment. Care Trio, with the help of Watson, has created a three-faced approach (CareEdit, CareGuide, and CareView) that aims at providing the best possible care to cancer patients.
While healthcare companies face challenges such as re-admissions in hospitals, doctors find it difficult to find out whether the patients are promptly following the prescribed after-care course or not. Today, several companies are harnessing machine learning and big data to tackle this problem.
AiCure has come up with a face-recognition mobile app, which helps physicians ensure that the patients are taking the correct medication, on time. In case a patient misses the course of medication or takes the wrong medication, an alert is sent to the doctor.
NextIT has introduced a new, intelligent product, named Health Coach, which helps patients with chronic conditions strictly adhering to their care plans. This product promptly keeps a track of the patients’ after-care prescription/course and reminds them to take medications on time.
IBM Watson’s NLP feature is being used by the Cafwell Concierge app to better understand the health conditions and health goals of the users. Based on the user input, the app provides alerts, which helps users to successfully achieve their goals successfully.
Ginger.io is working on the development of a mobile app, which remotely treats patients with psychiatric disorders. Using their app, patients can analyze their moods, understand what makes them angry, identify their behavioral patterns, and access the strategies suggested by doctors to cope with the condition.
Machine learning lends itself to some processes better than others. Algorithms can provide immediate benefit to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. ML can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of all these processes. Long term, ML will benefit the family practitioner or internist at the bedside. All these applications of ML in healthcare are only the tip of the iceberg. Machine learning can offer an objective opinion to improve efficiency, reliability, and accuracy.
As impressive as this progress is, it is just a beginning. The potential of predictive analytics, machine learning, AI, and HMIS to drive meaningful outcomes for users and practitioners is enormous. For users, these technologies portend to make health increasingly personalized, demystified and unintimidating. For practitioners, these technologies can be powerful aids helping them engage with users in a targeted manner and drive to measurable outcomes. As these technologies grow and evolve due to user adoption and advances from research, newer application realms will emerge within preventive healthcare and beyond.
The ability to rapidly analyze very large and often sparse data sets is the foundation for the predictive, learning, and personalization pillars. A combination of these technologies can make the preventive health journey personalized and one-size-fit-one, thus making it insightful, engaging, and effective.