Scalian Spain solutions

Cloud, Big Fast Data, IA, Advance Visual Analytics solutions according to Scalian Spain

Our approach

Our mission is to support our clients in their digital transformation.

Our solutions:

CliniCoder

The main goal of CliniCoder is to improve data quality and reliability, and increase productivity in the hospital discharge report coding process.

It offers:

• encoding assistance -> Encoder
• coding in Origin -> Medical
• automatic coding -> Machine learning
• generation of reports on hospital admissions, major surgery, outpatient consultations, emergencies, etc.

The tool helps you answer the following questions:

• How can we increase coder productivity?
• How can we improve encoding quality?
• Can doctors code outpatient consultations?
• We have to code ER reports but we have no resources!!!

Implementation of the CliniCoder coding system based on the ICD-10-ES (diagnosis and procedures – Spanish version) and ICD-0 (oncology) disease classifications.

Main strengths:

Indexing and processing of medical reports written in normal script or free text (unstructured data), in order to code diagnoses and procedures using natural language processing (NLP) techniques and ontological rules. The solution uses technologies such as Java, JavaScript, MongoDB, natural language processing (SpaCy) and machine learning (Python).

CliniTerm

This solution’s main goal is to make electronic medical record systems and their health data interoperable, to enable them to support Big Data & Analytics processes:

• SNOMED terminology (diagnoses, procedures, health data, etc.)
• Drugs (Nomenclator)
• LOINC laboratory tests, etc.
• Nursing care (NNN)
• Local and/or company baseline data
• Data mapping
• Compliance with the FHIR standard

Implementation of the CliniTerm baseline data interoperability and management system, based on SNOMED, LOINC, Nomenclator (drugs).

Main strengths:

Standardises data and manages baseline data for an organisation (standardisation of structured data).

Helps you answer the following questions:

• Who knows what the official allergy catalogue is?
• Where is the table of services?
• What are the active prescription drugs?
• When is ICD-10-ES updated?
• Are the master catalogues synchronised in the different applications?

Use of technologies such as Java, JavaScript, MongoDB, natural language processing (SpaCy) and machine learning (Python).

Smart Bot Framework

A rapid development solution that enables companies to develop and deploy bots with minimal coding time and a multi-channel, multi-architecture approach.

Main strengths:

A solution that provides all industry sectors with a framework for agile development of intelligent bots, which can be applied to countless use cases in every company department or business.

Indizen’s Smart Bots stand out from other solutions on the market thanks to:

• Simplicity and consistency in their programming, through the use of text and APIs.
• Easy development, upgrade and maintenance based on plugins.
• Highly configurable, they enable dialogues and messages to be decoupled from the application’s main feature.
• Multi-channel, with integrated provider channels or the option of developing new ones.
• Multi-cloud, on-site and hybrid architectures: agile technology. Avoids supplier lock-in.
• Scalable and expandable to meet high-demand cases.
• Customisable for a wide range of use cases, flexible dialogues and conversations based on the user’s natural language.

Indizen Bot architecture can be based on Cloud services technology offered by major technology players such as Microsoft Azure LUIS, Amazon Web Services, Google Cloud Dialogflow, IBM Watson, Nuance, etc. It can also offer a wide range of components from other major Cloud providers for more capabilities and features, such as Twilio Cloud voice and telecoms solutions, WhatsApp integration, Google Maps geolocation, image processing, voice transcription and sentiment analysis, and can even be connected to in-house solutions.