Implement various critical condition surveillance solutions to monitor sepsis, mortality, and patient safety data. Detect signs of clinical decompensation that could be life threatening or warrant urgent transfer to a higher level of care and then trigger alerts to appropriate care team members. Plug in the trained AI model in real time to receive the data and provide results

Define or integrate existing Models using python framework

Filter the data stream with key indicators required for the model

Integrate with EHR like Epic, Cerner and others to receive real time HL7 stream of data

Either send HL7 data back to EHR or call API or update database to reflect the results in EHR

Enable earlier intervention on deteriorating patients, improve their chance of survival, and reduce the need for higher-cost care.
Automatically notify caregivers to potential clinical decompensation, thereby reducing the burden on care teams.
Provide the research and evidence-base for predictive AI algorithms and associated interventions.

Scale and Performance: AI needs data in petabytes and current solutions to load data are not scalable and take lot of time. HiPaaS is highly scalable and we have successfully converted large sets of healthcare data like medical records, lab results, encounters, and patient demographics of Terabytes size each. HiPaaS converter is auto scalable using AWS serverless fargate architecture. Performance is key, we are continuously optimizing to convert data faster (e.g. 5-10 GB in few minutes). Recently, we were able to transforms 10+TB or observations data in few days.

Quality data is vital for achieving accurate and reliable outcomes in healthcare AI applications. Robust and well-curated datasets help in reducing errors, false positives/negatives, and bias within AI models. At HiPaaS, we have developed required validations, lookups, crosswalks which will check, clean the data before converting. Codes like ICD10, ICD9, Snomed, Ionic are mapped and can be configured based on source data. Furthermore, HiPaaS also handle data entry mistakes (e.g. decimal "." can be entered in 5 different ways).

There are various formats, standards , types and sources of healthcare data. Mainframe systems, public healthcare domain data and old EHR systems data is often available in custom csv files. EHR's like EPIC, Cerner and others provide CCDA, FHIR and/or HL7 data feeds. Payers and Insurance companies often have EDI X12 data related to claims and authorizations. To further complicate things, the data sets can be a point in time or over series of timeframes. HiPaaS has inbuilt mappers for standard formats and a mapping tool for custom data formats.

HiPaaS enables production grade, highly available infrastructure for deployment of AI Models