Author: Sathyanarayana Vantipalli

Every patient is different, every patient’s health is different, some medication works for some patients, at the same time that same medications won’t work for other patients. In the chronic condition population (patients suffering from chronic diseases) the development of complications varies from patient to patient for Eg: a patient suffering from diabetes will probably go on to develop kidney related complications but on the other hand patient having/suffering from diabetes may not end up developing any other complications or may even start developing heart related problems.

In order to treat patients more effectively the treatment needs to be more personalized and it has to be evidence-based. The personalized and evidence-based treatments essentially require one to understand the patient from a 360 degree view and a complete clinical, patient historical and family history data needs to be provided, only then it helps to better understand patient allergies, medication allergies (if previously encountered), family histories etc helps to understand the patient better.

Co-relating these clinical conditions and care-provider’s experience on treating such specific cohort patients helps to treat future patients better. On the other hand mining similar patient data and generating data of similar diseases faced by other patients helps in understanding disease progression and major factors influencing it and also helps a medical practitioner in figuring out how to control it.

But, there are several challenges inhibiting the utilization of such data. Today there is no promising technology platform to collect, store, exchange, normalize and analyse all this patient centric data to help health professionals in this regard. Apart from that supply-demand gap, the security and privacy of the health information exchange and storage is a much more challenging task when compared to collect patient data.

The new technologies popular today right from blockchain, artificial intelligence, digital transformations and robotic process automation show promise in overcoming the challenges faced by healthcare organisations in the exchanging, analysing, integrating and automating of health information and intelligence to mine deep insights from the hidden data, for providing sophisticated and meaningful insights for implementing advanced patient care and understanding patient health dynamics effectively.

aciana is pioneering the healthcare blockchain technology by developing a patient centric health information exchange and healthcare artificial intelligence and machine learning technologies to extract the hidden insights from available health information to advance patient care and to improve population health. aciana’s blockchain-enabled health information exchange platform is integrated with artificial intelligence and can be used for secure health information exchange while heeding to privacy and security concerns pertaining to the health information. The exchanged information is authorized at all levels of the healthcare system, thereby ensuring trust.

Advanced healthcare analytics are plugged in to the platform and these analytics extract patient’s 360 degree information for better understanding patient health. The healthcare blockchain technology promises a trusted and authenticated health information exchange implemented primarily between institutions and individuals mediating for the patients. It means that the patient is now the owner of his health information. Hospitals, health systems, independent practitioners, CRO’s and other care giving and research organisations benefit by addressing health information exchange challenges and aciana’s state-of-the-art healthcare analytics helps one in analyzing and predicting adverse events wherever possible and in-turn also suggesting patient-centric precautions to take. aciana healthcare analytics and RPA advances the efficiencies in disease management and analysis and plays a major role in providing personalized and evidence-based treatments to the patients. 

Aciana, is an innovative healthcare technology advancement company which brings advanced technologies and processes together to help health institutions and professionals in delivering value-based care with improved healthcare analytics, while at the same time also controlling costs.

To improve the trust and reduce the barriers in the pervasive use of clinical data analytics requires cross Organizational data sharing infrastructure to collect trusted, authenticated health information. The collected data should be patient-centric, historically consolidated clinical data. while exchanging cross organizational patient centric clinical data, more than 80% of the data is unstructured or Semistructured like out patient SOAP notes, discharge summaries, radiology notes and consultant notes etc.

To intervene hidden insights from this unstructured data needs deep domain clinical artificial intelligence technology to convert unstructured data to Structured data. The converted structured data can be leveraged for precisioned clinical analytics to get more substantial improvement in sophisticated quality metrics and hidden insights drawn from the ecosystem of inter connected digital health systems

The major challenges of dealing with the healthcare data is of its sensitive nature and maintaining the privacy, security and ownership is paramount concern.

At the same time ever changing healthcare reforms demanding for value based care rather than volume of care. Today’s care coordination organizations like ACO’s and other healthcare delivery centres faces several challenges in delivering continuum of care.

To provide continuum of care for care coordination, patient centric health information needs to be exchanged between the organizations to eliminate the duplication of services and enabling personalized care. There are several challenges while exchanging the health information across the organizations like

1. Information may be lost during hand off and take over
2. Information may be modified or wrongly routed
3. Exchanged information may not be portable.

Currently, healthcare data split among different entities with different formats in different locations. Patient centric precession analytics requires bundles of cross organizational patient centric data needs to be collected aggregated and purified and transformed to fit into analytical models. To perform precissioned clinical analytics and predictive modelling for the given cohort of disease requires many of such patient centric cross organizational data bundles.

aciana developed private and permissioned blockchain health information exchange products and solutions for cross organizational health information exchange and healthcare interoperability. aciana’s advanced clinical NLP solutions extracts hidden information from the unstructured clinical notes and converts structured  information. aciana’s deep domain artificial intelligence and analytical solutions leverages these structured information to do precisioned analytics.

aciana integrated blockchain-Artificial intelligence-Big data-Products and Solutions helps to securely exchange cross organizational data and performs deep domain clinical, financial and administrative analytics.

In 2009, I was involved in developing artificial intelligence and Machine learning algorithms for speech recognition and computer vision problems in semiconductor industry, for entertainment applications.  This made me passionate and curious about the power of artificial Intelligence algorithms and applying these ideas for early detection of diseases in the clinical domain.

With limited knowledge about healthcare domain, I started studying and developing fragmented solutions with computer vision techniques to detect the diabetic retinopathy and also measure the intima-media-adventitia thickness in the carotid arteries from the ultrasound images. But I realized that these fragmented solutions does not have much impact since providers or any specialist doctors suggest the scanning techniques when there is a clear evidence of the diseases.

I wanted to implement these ideas for early detection of the diseases with respect to the patient historical data such as family histories, patient clinical history and socio economic factors and other third party data.

Later, I started practicing this to predict the risk of readmissions from the transactional data such as claims data. As I started putting efforts on these transactional data sets, I felt these data sets are highly inadequate as the data was not majorly helpful because a lot of hidden insights reside in the unstructured data. Clearly, this was the data which should be aggregated and collected from the multiple populations to study the cohort of disease. Unfortunately, owing to multiple factors, there is no such platform to exchange clinical, financial and all other available data to do any meaningful analytics.

So, I started researching on getting lot of data sets in later part of my carieer, but my attempts were not as expected. That was when I decided to solve the problems of collecting cross organizational dataset by considering the regulations and data security aspects like HIPPA and PHI etc. In parallel, I was observing the developments with Block Chain – which is solving data security aspects, decentralization and immutability of the data, permissions for the given participants and eases the complex process to get the data. Based on these aspects I decided to build this platform to deploy the Block Chain network on the cross institutional data exchange for multiple hospitals. I created a pilot project and to develop a Block Chain enabled network to exchange the health information across the health organizations which is more patient centric, trusted and more authenticated information.

The other challenge is once we have cross organizational data sharing, the data will be in unstructured formats like unstructured clinical notes, images, signals and tabular data sets. To solve this we started building the capabilities to convert unstructured clinical notes into structured data using advanced NLP techniques. We also focused on several computer vision algorithms to understand the metrics from the radiological datasets and big data solutions to hold this large amount of data. This helped us to create the entire echo system to securely exchange the data and then deploy the analytical platforms using these advanced technologies.

Several attempts were made to understand the domain and technical challenges to solve this cross organizational data sharing. Ultimately, all my attempts succeeded to develop this pilot project which motivated me to setup a new organization to solve these kind of complex problems. I am happy to be announcing the launch ‘aciana’ as a healthcare company. We leverage 360 degree data set pertaining to the patient to generate actionable insights and also assist the providers to give more meaningful and coordinated value based care. Our platform also helps to give precision and real-time personalized medicines. To know more about us and what we do at aciana, visit our website, follow us on the social media and contact us.