Algorithmic AI applications are used commonly in today’s healthcare. When humans embed known data into algorithms, computers can extract information and apply it to a problem.When it comes to effectiveness of machine learning, more data almost always yields better results and the healthcare sector is sitting on a data goldmine.

Classifying medical, non-medical items and services is one of the major challenge in hospital administration services where the concept of ML has scope to play a vital role in mitigating the human efforts. AI uses human-created algorithms as the basis for analyzing data and recommending treatments. One of the most emerging algorithm is Recommendation algorithm implemented in engine called Recommendation engine.

How it works?

Recommendation engine algorithm typically produce a list of recommendation items based on approaches like collaborative filtering, hybrid filtering and cluster filtering.

Collaborative filtering relies predominantly on the behavior of users. It is well suited to highly diverse sets of items and produces more serendipitous recommendations. Items recommended by means of collaborative filtering have a greater affinity with one another than a strict comparison of their attributes would suggest relying on actual human experiences. There are two approaches to collaborative filtering, one based on items, the other on users. Item based approach relies on the distance between the items relationships, whereas user-based approach relies on distance between the user’s interest and frequency of items.

On the other hand, Hybrid model delivers more precise suggestions and its approach can be created using various means like by adding collaborative capabilities to a content-based model, by designing two separate models and then combining them into a custom model. By combining the two approaches you can enjoy the benefit of both without handling their disadvantages. It requires good data science skills to create a custom model.

Cluster filtering identifies user groups and recommend each user in this group the same items. It’s better to use cluster analysis for the selection of relevant neighbors in collaborative filtering algorithms when we have enough data. Apparently, it can also improve the performance of complex recommendation systems. Each cluster will be assigned to typical preferences, based on preferences of customers who belong to the cluster.

Role of RA in EMR:

EMR plays a vital role in hospital administration in terms of patient medical record management. Challenges in areas like clinical documentations, selecting medical orders and sorting through the in-basket are faced by the users when handling EMR.

By implementing recommendation algorithm it simplifies the effort of the user by categorizing the data and suggests the user to choose the relevant data directly and not by using search engines.

For example: When a care provider is prescribing a medicine or other medical services to a patient, the search engine will display the items based on the search criteria only. Whereas, the recommendation algorithm will display the item as ‘Recommended items’ in ordering screen based on patient’s diagnosis, alerts, allergies, treatment procedures, past medical histories details, stock availability. The algorithm reads the inputs given in various sections across the EMR, priorities the items and recommends the appropriate items to the care provider.

The algorithm can be implemented in two ways,  active and retrospective, for fetching the details when categorizing the items.

Active approach deals with the current patient data as the source of algorithm pattern which are efficient for small scale hospitals. This approach is easy to implement in applications, since it does not requires any additional ML algorithms.

Retrospective approach deals with the past medical history date as the source of algorithm pattern which are efficient for large scale multi specialty hospitals. Through retrospective approach is advanced, it needs ‘Deep learning’ AI methodology to form its own pattern for recommendation engine.

Authored by: Ram Kumar

Test Lead @ Incarnus


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We are a global provider of next-generation, cloud-enabled solutions for all levels of healthcare from primary health practice to large-scale hospital networks.
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