Mihail Jekov, CTO and Partner, Sqilline
Electronic Health Records (EHRs) contain a large quantity of machine-readable data. However, institutions choose different EHR vendors, and the same product may be implemented differently at different sites. In contrast to data elements required for successful billing, clinically relevant data elements are rarely standardised, even though applicable standards exist. The results are obvious: the level of interoperability is low, data is locked in silos, and its use for decision-making is insufficient.
A recent study of EHR interoperability found that 68% of data was “understood” when exchanged across different sites using the same vendor, but only 22% was “understood” when exchanged across different EHR vendors. Two sites that share the same vendor are, on average, more interoperable. However, even for the implementation of the same EHR product, interoperability is not guaranteed.
How could we practically solve this long-lasting interoperability problem?
Gartner presents a strategic approach to the solution in four key words: (1) data, (2) analytics, (3) decisions, and (4) governance. Gartner also raises a couple of very important questions. Do we have the right data? Do we make the right decisions? Does our analysis drive outcomes? Do we have the right processes?
Real-world data comes first. At Sqilline, we understand that data is key
We have created and continue to develop our Danny Platform: a big data healthcare analytics platform that integrates massive amounts of Real-World Data (RWD) from various hospital sources (EHRs, laboratories, registries, etc.). Powered by SAP HANA and built with propriety ML and NLP algorithms, Danny Platform extracts both structured and unstructured (free text) data to preprocess, normalize and ensure high data quality.
Danny Platform provides comprehensive searches, in-depth analyses, predictions, treatment solutions, and decision support to physicians, researchers, and payers. Sqilline has already accumulated powerful data reports and insights from oncology, cardiology, ophthalmology, rare diseases, and other specific disease areas. Currently, Danny Platform processes more than 1.6 million unique patient records from more than 50 leading hospitals in CEE.
Danny Platform is our analytics engine
Danny Platform serves as our analytics engine, aggregating and harmonizing various heterogeneous data sources in digitalized and structured medical records to support informed clinical decisions. The structured medical data can be directly utilized by the physicians in charge or accessed in anonymized and aggregated manner by external stakeholders beyond а specific medical institution. The unified structures created by the ML/NLP algorithms allow for the creation of patient cohorts consisting of patients meeting specific inclusion and exclusion criteria defined by the customer, forming the foundation of our three key solutions: (1) Danny Analytics, (2) Danny Decision Support, and (3) Danny e-Clinical Research. These three solutions respectively bolster our core services: (1) real-world data analysis and health outcomes research, (2) clinical decision support to identify the appropriate patient for precise treatment, at the optimal moment, and (3) clinical research support covering feasibility, enrolment, and the conduct of clinical trials.
We envision a substantial impact on cost optimization, the acceleration of clinical studies, decision support at various levels of the healthcare system, and the practical implementation of precision medicine in aspects such as diagnosis, risk prediction, and treatment planning. It becomes evident that Danny Platform offers a practical solution to the interoperability problem on the top of the existing EHRs.