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All about clinical decision support systems


Clinical decision support systems (CDSS) are computer-based systems used to generate actionable insights into the condition of the patient so that the primary physician and healthcare staff can make right decisions based on available information. Simply put, a CDSS is a computer program that takes inputs from a user about a patient's condition, checks them against predefined rules and provides an output that can be used to arrive at a diagnosis.


CDSS is like a decision support system (DSS) widely used to support business management. It helps healthcare providers make right and fast decisions to improve patient care.


Clinical decision support systems can

  1. Help primary healthcare workers or non-expert medical staff to perform initial diagnosis and determine cases that have to be referred to a speciality hospital or an expert. In cases, where time is of essence such a system can be a life-saver.

  2. Can send notifications and reminders for preventive tests and checkups.

  3. Give alerts about potentially dangerous drug interactions

  4. Alert clinicians to possible redundant testing their patient has been scheduled to undergo

CDSS can lower costs and increase efficiency of healthcare to patients.


The use of CDSS increased in the US after passage of the HITECH Act (Health Information Technology for Economic and Clinical Health Act). This act stipulates that US healthcare providers demonstrate meaningful use of health IT or face reductions in Medicare reimbursements.


Meaningful use means - implementing one clinical decision support rule, including diagnostic test ordering and the ability to track compliance with that rule. That rule should apply to a specialty or high-priority condition.


Types of clinical decision support systems


There are two main types of clinical decision support systems.


1. Knowledge-based


A knowledge-based CDSS consists of three main elements. It uses a knowledge base, applies rules to patient data using an inference engine and then displays the results.

A knowledge based CDSS can help in faster diagnosis. It can empower the primary healthcare workers to decide on cases that need immediate attention. Healthcare can be made accessible to rural population where speciality hospitals and experts are difficult to find.
Knowledge based CDSS
  1. Computer based interface which serves as input collector and output provider to the user

  2. A knowledge base which is a data repository of information and rules

  3. An inference engine that consumes the input, checks the knowledge base, perform evaluation and provides an output. This engine mostly operates using if-then-else rules.


2. Non-knowledge-based


A CDSS without a knowledge base relies on machine learning to analyze data. There are no predefined rules. Just data that the machine learning algorithm uses to find patterns in patient data and determine relationships between symptoms and a diagnosis.


CDSS is often integrated with electronic health records (EHRs) and Computerized physician order entry (CPOEs) to streamline workflows and make use of existing information.


Challenges


There have been some challenges in adopting CDSS:

  1. Integration with a healthcare organization's existing systems. Compatibility with EHR and CPOE systems can be an issue and consume a lot of time to ensure that the three work together seamlessly.

  2. Fine-tuning the rules-engine or updating protocol based on newer research and findings is difficult as it involves changes to the system.

  3. While alerts and notifications about tests and drug doses and other preventive checks are good, they can overwhelm the healthcare workers. Too many alerts can actually result in the workers ignoring them all.

  4. CDSS requires a lot of data entry. The data entry has to be accurate and diligent in order that the output is reliable. There is general reluctance by medical staff in entry large amount data into a system.


CDSS with low-code tech


The challenges with CDSS have mostly been about the lack of ability to change the rules and protocols in a timely manner and performing integrations with systems like EHR and CPOE. The dependency on IT vendor for every change meant a high cost of ownership of the CDSS.


Both these challenges are addressed by CDSS built using low-code, like the one FeldsparTech built for a national hospital for mental health.


A system built using low-code technology does not require code changes for updates to the flows or rules. Fine tuning of rules for accuracy is easy. Updating rules does not require any user to know programming.


A knowledge based CDSS by FeldsparTech using its low-code platform. The knowledge base is easy to update and so is the inference engine. None of the updates or changes require coding. Even the clinical staff can make changes without depending on the IT department or vendor.
CDSS by FeldsparTech

Advantages of CDSS built by FeldsparTech :

  1. Making changes is easy and fast

  2. Rules /protocols can be updated by medical staff with instructions from doctors

  3. The CDSS application will be on a secure cloud

  4. The system can easily scale up to manage multiple thousand patients

  5. In built EHR system along with a CDSS.

  6. Integrations with CPOE and existing EHR can be performed using APIs

  7. Web responsive, hence available on all devices. One code base for use everywhere. No need for separate mobile apps

  8. Can operate in an offline mode. Areas where the internet is patchy the applications can store data and sync up later when the connection resumes


Contact us for a demo of the FeldsparTech CDSS.

 

For information about CDSS by FeldsparTech, please write to: info@feldspartech.com