Introduction

Commercial platforms for healthcare data analytics

In order to tackle big data challenges and perform smoother analytics, various companies have implemented AI to analyze published results, textual data, and image data to obtain meaningful outcomes. IBM Corporation is one of the biggest and experienced players in this sector to provide healthcare analytics services commercially. IBM's Watson Health is an AI platform to share and analyze health data among hospitals, providers and researchers. Similarly, Flatiron Health provides technology-oriented services in healthcare analytics specially focused in cancer research. Other big companies such as Oracle Corporation and Google Inc. are also focusing to develop cloud-based storage and distributed computing power platforms. Interestingly, in the recent few years, several companies and start-ups have also emerged to provide health care-based analytics and solutions. Some of the vendors in healthcare sector are provided in Table 2. Below we discuss a few of these commercial solutions.

Table 2 List of some of big companies which provide services on big data analysis in healthcare sector

Company Description Web link
IBM Watson Health Provides services on sharing clinical and health related data among hospital, researchers, and provider for advance researches https://www.ibm.com/watson/health/index-1.html
MedeAnalytics Provides performance management solutions, health systems and plans, and health analytics along with long track record facility of patient data https://medeanalytics.com/
Health Fidelity Provides management solution for risks assessment in workflows of healthcare organization and methods for optimization and adjustment https://healthfidelity.com/
Roam Analytics Provides platforms for digging into big unstructured healthcare data for getting meaningful information https://roamanalytics.com/
Flatiron Health Provides applications for organizing and improving oncology data for better cancer treatment https://flatiron.com/
Enlitic Provides deep learning using large-scale data sets from clinical tests for healthcare diagnosis https://www.enlitic.com/
Digital Reasoning Systems Provides cognitive computing services and data analytic solutions for processing and organizing unstructured data into meaningful data https://digitalreasoning.com/
Ayasdi Provides AI accommodated platform for clinical variations, population health, risk management and other healthcare analytics https://www.ayasdi.com/
Linguamatics Provides text mining platform for digging important information from unstructured healthcare data https://www.linguamatics.com/
Apixio Provides cognitive computing platform for analyzing clinical data and pdf health records to generate deep information https://www.apixio.com/
Roam Analytics Provides natural language processing infrastructure for modern healthcare systems https://roamanalytics.com/
Lumiata Provides services for analytics and risk management for efficient outcomes in healthcare https://www.lumiata.com
OptumHealth Provides healthcare analytics, improve modern health system's infrastructure and comprehensive and innovative solutions for the healthcare industry https://www.optum.com/


AYASDI

Ayasdi is one such big vendor which focuses on ML based methodologies to primarily provide machine intelligence platform along with an application framework with tried & tested enterprise scalability. It provides various applications for healthcare analytics, for example, to understand and manage clinical variation, and to transform clinical care costs. It is also capable of analyzing and managing how hospitals are organized, conversation between doctors, risk-oriented decisions by doctors for treatment, and the care they deliver to patients. It also provides an application for the assessment and management of population health, a proactive strategy that goes beyond traditional risk analysis methodologies. It uses ML intelligence for predicting future risk trajectories, identifying risk drivers, and providing solutions for best outcomes. A strategic illustration of the company's methodology for analytics is provided in Fig. 4.


Fig. 4 Illustration of application of "Intelligent Application Suite" provided by AYASDI for various analyses such as clinical variation, population health, and risk management in healthcare sector


Linguamatics

It is an NLP based algorithm that relies on an interactive text mining algorithm (I2E). I2E can extract and analyze a wide array of information. Results obtained using this technique are tenfold faster than other tools and does not require expert knowledge for data interpretation. This approach can provide information on genetic relationships and facts from unstructured data. Classical, ML requires well-curated data as input to generate clean and filtered results. However, NLP when integrated in EHR or clinical records per se facilitates the extraction of clean and structured information that often remains hidden in unstructured input data (Fig. 5).


Fig. 5 Schematic representation for the working principle of NLP-based AI system used in massive data retention and analysis in Linguamatics


IBM Watson

This is one of the unique ideas of the tech-giant IBM that targets big data analytics in almost every professional sector. This platform utilizes ML and AI based algorithms extensively to extract the maximum information from minimal input. IBM Watson enforces the regimen of integrating a wide array of healthcare domains to provide meaningful and structured data (Fig. 6). In an attempt to uncover novel drug targets specifically in cancer disease model, IBM Watson and Pfizer have formed a productive collaboration to accelerate the discovery of novel immune-oncology combinations. Combining Watson's deep learning modules integrated with AI technologies allows the researchers to interpret complex genomic data sets. IBM Watson has been used to predict specific types of cancer based on the gene expression profiles obtained from various large data sets providing signs of multiple druggable targets. IBM Watson is also used in drug discovery programs by integrating curated literature and forming network maps to provide a detailed overview of the molecular landscape in a specific disease model.

IBM Watson in healthcare data analytics

Fig. 6 IBM Watson in healthcare data analytics. Schematic representation of the various functional modules in IBM Watson's big-data healthcare package. For instance, the drug discovery domain involves network of highly coordinated data acquisition and analysis within the spectrum of curating database to building meaningful pathways towards elucidating novel druggable targets

In order to analyze the diversified medical data, healthcare domain, describes analytics in four categories: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics refers for describing the current medical situations and commenting on that whereas diagnostic analysis explains reasons and factors behind occurrence of certain events, for example, choosing treatment option for a patient based on clustering and decision trees. Predictive analytics focuses on predictive ability of the future outcomes by determining trends and probabilities. These methods are mainly built up of machine leaning techniques and are helpful in the context of understanding complications that a patient can develop. Prescriptive analytics is to perform analysis to propose an action towards optimal decision making. For example, decision of avoiding a given treatment to the patient based on observed side effects and predicted complications. In order to improve performance of the current medical systems integration of big data into healthcare analytics can be a major factor; however, sophisticated strategies  need to be developed. An architecture of best practices of different analytics in healthcare domain is required for integrating big data technologies to improve the outcomes. However, there are many challenges associated with the implementation of such strategies.