Case Studies

Claim analytics

One of the best teams in the country on claims based analytics, we have experience in working with the entire National Claims Data, and additional external data sources to allow our customers take important business decisions based on our analysis. Some of the ways we have helped our clients are as follows

  • 1

    Physician Network and Referral Analysis:

    Hospital and ACO management need to understand who are referring to their physicians and what proportion of their clients are being leaked to competitors within their market area. We have developed Loyalty and Volume Score for physicians in the network within the defined market of a client organization and helped identify the most likely physician’s practices that can be acquired to improve business.

  • 2

    Entity Resolution:

    With increasing volume and velocity of health data, inference across networks and semantic relationships between the health organizations becomes a greater challenge leading to duplicate entities and linkage issue. We use a similarity quotient approach to generate a single entity key by standardizing the organization names and resolving different keys for the same organization using similarities of locations

  • 3

    Targeted Campaign for Hospitals:

    Propensity Models for largest hospital chains in the US to determine prospective consumers for health products related to specific service lines using claims data for patients and survey data for potential consumers in the market area. Models were developed for more than 65 different diseases with patient models having more than 75% response rate and consumer models with greater than 65% success rate.

  • 4

    Workers Compensation Claim Analytics:

    We help our clients identify claims that are likely to escalate based on the information available the day of the accident (0-day) and also at the 30 day level so that the insurer is able to allocate resources in proportion to the severity of the case.


UrjaSenseTM - Energy Management Solution in Industrial and Home Settings

UrjaSenseTM is an energy management and advisory software application for home owners. Its purpose is to provide real-time insights into energy consumption in the overall home and each appliance in the home using electrical energy. Thus, providing evidence based advisory to home owners where they can make changes to their energy consumption behavior and time of usage of electricity so as to minimize their current electricity cost. This application is built on infrastructure that currently resides on Nebbiolo’s Fog Node and thus can be ported to it when the production version of the UrjaSense is available.

Streaming Data Analytics:

We help you detect anomalies to prevent breakdown of your services by performing appropriate analytics on your streaming data at the edge itself so that you are immediately alerted when you need to take remedial action and know what action will be appropriate at that time. Our anomaly detection methods provide a ready solution generating alerts at different severity levels so that effective action can be taken by the production managers. We also predict the future performance of different machine parameters based on past data.


Meta-Analysis to determine Most Effective Treatments and Dosages by Combining Results from Different Studies

When Comparing Different Competing Treatments at different dosages it is often not possible to do this in only one study because of ethical, monetary and other constraints. The task here is used to combine results from several studies which meet certain criteria and compares some but not all of these treatments at some of these dosages and make meaningful interpretations for all of the treatments at all of these dosages.


We compared treatments for our client using Models and links like Conditional Binomial Likelihood, with probit link, random and fixed effects model, binomial likelihood, logit link, random & fxed effect models and created network diagrams and rankograms with best probability calculation. For this purpose we did pairwise meta-analysis and indirect treatment comparison. Both frequentist and Bayesian framework comparisons were made and patient characteristics were also analyzed.


The cost of new clinical trials to test all possible combinations of treatments and dosages were avoided by our clients and our models were used to determine the effective dosages of treatments and identify most effective treatment and dosages for different levels of diseases.


PROBLEM: Developing an Interactive Alert System through the Case Manager Dashboard to Identify and Respond to Patients Requiring Immediate Attention

Diabetes Patients at a reputed hospital were monitored through the IntelliH Remote Health Monitoring System. Case Managers handling many patients needed to focus on the patients that required immediate attention and an algorithm needed to be formulated to identify such patients with priority levels for this purpose.
SOLUTION: Anytime the patients experienced Blood Glucose excursions and other vital parameters satisfied certain dynamic rule based conditions identified by their respective Physicians, alerts were sent to the Care Managers so that they could take appropriate action.

BENEFIT: Patients have commented on how timely intervention on the part of their Care Managers helped them avert emergency situations.

Development Environment: Visual Studio 2013, .Net Framework 4.5, Microsoft SQL Server 2008 R2 ; Coding Tools: Asp.Net, C#/VB.Net language, LINQ, Web API. Database: Microsoft SQL Server 2008 R2; External Libraries Used: Clickatell SMS Service, Google GCM (Google Cloud Messaging), .iOS .p12 Certificate

Data Quality

Quality Assessment, Cleansing, and Reporting of Medical Devices and Health Sensor

Data (QualitySense)

As a data analytics company, AnalyticsPlus has extensive experience in health care data quality assessment and cleansing. While humanly collected data has issues related to missing, incorrect data entries of different varieties, data duplication, and many others , all kinds of data in health whether sourced from EMR, claims, or patient surveys require significant quality assessment and cleansing before they can be used for analysis, reporting and visualization. The problem is compounded when multiple sources and types of data are merged together to meet the statistical or machine—learning based analytics or predictive modeling. As the number of health sensor and medical devices becoming available to patients are increasing exponentially, the need for data quality assessment, cleansing and validation is also increasing.

We are developing a “clearing house” of 100s of health sensor data through a systematic quality assessment, cleansing, and reporting platform, called QualitySense, that can be integrated with the Electronic Medical Record of health care facilities. Thus, all the data coming from these personal-sensors can be vetted and a seal of “AnalyticsPlus Approval” can be designated for those devices. Unlike one time FDA approval process, QualitySense system will be processing data coming from these devices on a real time basis and validating them or flagging them by comparing within device variability, calibration data, and inter-device variability. Thus, the hospital management and their care providers have a constant visibility and can see which devices/brands are more reliable than others and encourage their patients to use those accordingly. A layered or sequential pipeline approach to data quality assessment and cleansing has been developed using streaming data analytics infrastructure.

QualitySense will be our proprietary data quality tool to be used universally. It will enable cleansing and monitoring of data quality on an ongoing basis, helping to turn input data into trusted information. This Application will use appropriate business rules at data entry, during continuous streaming, and at the end point, and scale to cleanse millions of rows of static or dynamic data to provide trusted information for Data Analysis.

Anomaly Detection and Predictive Analytics

PROBLEM: Detection of Anomalies and Predicting Future Robot Arm Movements on Streaming Data

We were engaged by Nebbiolo to assist with a problem from a large Automobile manufacturer with Robots on the shop floor. Robots stream vast amounts high velocity data that needs to be analyzed in real time to trap anomalies. For example, in the event of an undetected battery power loss on a robot leads to disruption in the shop floor. Data tends to be noisy that made anomaly detection a challenge. A history of anomalous events was not available to facilitate detection.

OUR SOLUTION: Analytics Plus captured and analyzed sensor data from Kuka Robots using sensors deployed by Nebbiolo. We used unsupervised data cleaning algorithms based on clustering (K-means/Dbscan) to detect anomalies and eliminate sensor error or missing data. Prediction algorithms like HoltWinters, EWMA, ARIMA were used to predict future movement of data under normal conditions and deviations from the norm was used to predict anomalies. An App was built on the Nebbiolo platform allowing users to use/ set up pipelines to analyze data from sensors.
BENEFIT: The solution developed on test lab robots was able to capture genuine anomalies in real time and predict future robot arm movements based on past data. This predicts anomalies in advance and thus allows predictive maintenance and root cause analysis to be done on shop floors.

Tools Used: Influxdb, Kapacitor, Grafana, Python.