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Managing Snowflake For Cost Effective Performance
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April 2021
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Overview
Before any drug or treatment received regulatory approval, it goes through rigorous testing and development. While determining the efficacy of a drug is paramount, patient safety cannot be sacrificed to achieve it. Patient safety is a crucial part of clinical trials and has traditionally been the responsibility of medical monitors. Saama Analytics’s Life Science Analytics Cloud (LSAC) Drug Efficacy and Patient Safety analytics capability allows the monitoring of patient adverse events, lab data, vitals, and concomitant drug interactions.
When you look at multiple sites and disparate data systems, it becomes clear that critical information about the way a particular group of patients is responding can become lost or obscured in the mass of data.
LSAC, as an AI-powered solution, has the potential to automate any data process and leverage it to derive critical insights that may be the difference between successfully executing patient safety protocols and suddenly encountering a highrisk situation during a trial.
Benefits
A crucial part of setting up a clinical trial is establishing various controlled groups to determine a drug’s efficacy. The process is quite sensitive and has many builtin safety practices. An AI-powered solution can help ensure that patient safety data is more accurate and has the ability to warn medical monitors of potential adverse events or risk.
While medical monitors evaluate all processes and ensure patient safety, they could use some help in sifting through data and identifying patterns not immediately visible to the human eye. An AI-powered solution can perform a deep dive into any data system, harmonize it to adhere to the Industry standards – Study Data Tabulation Model (SDTM) – and then extract insights quickly and accurately. These insights may have been otherwise unidentifiable, and even if a group of analysts had been able to draw the same conclusions, they would not have been able to do so as quickly.
LSAC can ensure protocol adherence, keep data records standardized and free of error, monitor inclusion/exclusion criteria, keep a constant check on all regulations, and generate near real-time analysis of data. With real-time insights at your fingertips, it is possible to have a comprehensive view of how a drug or treatment is performing across patient pools and sites on a global level. Since clinical data is standardized near-real time using a metadata-based approach, it allows sponsors to standardize the clinical data management, review, and monitoring process. This approach enables the submission pathway to be faster and more efficient.
LSAC also offers a virtual assistant interface that provides access to results from the Drug Efficacy and Patient Safety Analytics capability. As a result, activities such as the review of clinical data, troubleshooting sites with a high number of adverse events, and performing patient-level safety analysis, become more accurate and efficient.
Work Process
LSAC allows users to monitor patient adverse events and identify the riskier sites with respect to adverse events and patient count.
Patient lab details can be analyzed over the course of the trial to ensure maximum patient safety.
Such a comparison and analysis can only help improve the assessment of the drug’s efficacy and further contribute to patient safety by making the testing of compounds even safer than previously possible. LSAC’s deep learning capability can ingest all types of structured and unstructured data to deliver such a sophisticated level of analysis.
LSAC can harmonize and standardize any type or volume of data, offer real-time key analysis of patient safety and drug efficacy, and allow the review of clinical data with the help of an interactive voice assistant. The AI technology that works with your data, also powers the voice assistant, allowing it to contextually respond to your queries and deliver predictive analysis derived from medical monitoring data.
Overview
Before any drug or treatment received regulatory approval, it goes through rigorous testing and development. While determining the efficacy of a drug is paramount, patient safety cannot be sacrificed to achieve it. Patient safety is a crucial part of clinical trials and has traditionally been the responsibility of medical monitors. Saama Analytics’s Life Science Analytics Cloud (LSAC) Drug Efficacy and Patient Safety analytics capability allows the monitoring of patient adverse events, lab data, vitals, and concomitant drug interactions.
When you look at multiple sites and disparate data systems, it becomes clear that critical information about the way a particular group of patients is responding can become lost or obscured in the mass of data.
LSAC, as an AI-powered solution, has the potential to automate any data process and leverage it to derive critical insights that may be the difference between successfully executing patient safety protocols and suddenly encountering a high-risk situation during a trial.
Benefits
A crucial part of setting up a clinical trial is establishing various controlled groups to determine a drug’s efficacy. The process is quite sensitive and has many built-in safety practices. An AI-powered solution can help ensure that patient safety data is more accurate and has the ability to warn medical monitors of potential adverse events or risk.
While medical monitors evaluate all processes and ensure patient safety, they could use some help in sifting through data and identifying patterns not immediately visible to the human eye. An AI-powered solution can perform a deep dive into any data system, harmonize it to adhere to the Industry standards – Study Data Tabulation Model (SDTM) – and then extract insights quickly and accurately. These insights may have been otherwise unidentifiable, and even if a group of analysts had been able to draw the same conclusions, they would not have been able to do so as quickly.
LSAC can ensure protocol adherence, keep data records standardized and free of error, monitor inclusion/exclusion criteria, keep a constant check on all regulations, and generate near real-time analysis of data. With real-time insights at your fingertips, it is possible to have a comprehensive view of how a drug or treatment is performing across patient pools and sites on a global level. Since clinical data is standardized near-real time using a metadata based approach, it allows sponsors to standardize the clinical data management, review, and monitoring process. This approach enables the submission pathway to be faster and more efficient.
LSAC also offers a virtual assistant interface that provides access to results from the Drug Efficacy and Patient Safety Analytics capability. As a result, activities such as the review of clinical data, troubleshooting sites with a high number of adverse events, and performing patient-level safety analysis, become more accurate and efficient.
Work Process
LSAC allows users to monitor patient adverse events and identify the riskier sites with respect to adverse events and patient count.
Patient lab details can be analyzed over the course of the trial to ensure maximum patient safety.
Such a comparison and analysis can only help improve the assessment of the drug’s efficacy and further contribute to patient safety by making the testing of compounds even safer than previously possible. LSAC’s deep learning capability can ingest all types of structured and unstructured data to deliver such a sophisticated level of analysis.
LSAC can harmonize and standardize any type or volume of data, offer real time key analysis of patient safety and drug efficacy, and allow the review of clinical data with the help of an interactive voice assistant. The AI technology that works with your data, also powers the voice assistant, allowing it to contextually respond to your queries and deliver predictive analysis derived from medical monitoring data.
Challenge
One of the largest affinity-based insurance carriers, with over $2.6 million active policies split over several disparate applications, was handicapped by the multitude of systems of record and countless data inconsistencies. The company was facing customer data acquisition, audit and compliance issues. There was a strong need to improve the customer experience, reduce the financial impact of discounts, cross-sell and up-sell more effectively.
Specific challenges
- Lack of a single, consolidated view of customer information
- Data privacy, audit and compliance issues
- Inability to segment customers, manage campaigns or engage customers seamlessly
- Lack of system integration across policy, billings and claims
- Inability to respond to customer event triggers timely
- Inability to translate findings into actionable customer experience programs
- Inconsistent customer satisfaction and customer retention correlation
- Ineffective cross-selling and up-selling
Solution
Saama Analytics devised a strategy and maturity roadmap with the carrier, and built a Customer 360 solution for a single, consolidated view of customers.
- Better tracking of critical KPIs than just mapping planned vs. actual stages
- Significant saving of resources and cost
- Ability to predict and correct anomalies
- Increased probability of keeping the study within budget and maintain timelines
- Advanced AI capability to enable better decision making
The Customer 360 solution was built on these components
- Master Data Management
- Data Quality services
- Enterprise Data services
- Customer portal
Solution highlights
- Devised strategy and maturity roadmap
- Integrated over twenty data sources
- Integrated agent portal, customer selfservice portal, policy administration, and CRM applications
- Established data governance and stewardship processes
The data was acquired from over twenty sources including policy, claims, affinity systems, and third parties. The data was standardized, synchronized and enriched through MDM and Data Quality services, and integrated with applications through enterprise data services using modern search technologies, SoA services and batch data services.
The Customer Portal was integrated with agency, claims and policy systems. It allows claims officers, underwriters and agents to view and drill down to detailed customer information and to make informed decisions. Demographics, policy and household information, history of customer interactions, and even customer scoring are all displayed on one screen, guiding the carrier’s conversations and interactions with customers.
Business Outcomes
- A single view of the customer and the record of truth
- Faster claims processing turnaround time
- Higher levels of privacy, security and regulatory compliance
- Improved customer service personnel productivity
- Omni-channel experience on customer interactions and events
- Higher Net Promoter Score (NPS) results
- Target segmentation for higher campaign effectiveness
- Quick answers to questions that used to consume weeks
Enabling a 360-degree customer view
The Challenge
A large Insurance carrier was facing a number of challenges with claims management and fraud detection.
First, the company had amassed a significant amount of claims data through the years. However, the data was difficult to consolidate and use. The information resided in five different legacy claims applications, and the insurer had no enterprise-wide view of claims.
Second, the insurer’s claims team was unable to perform any real time analysis. A claim is considered ‘the moment of truth’ for policyholders. Churn and dissatisfaction are directly correlated with an individual’s experience with how expediently their claim is processed and settled. It was, therefore, essential to optimize the claims process.
Third, there was a dire need to automate existing fraud models and build system that would provide effective fraud prevention techniques to mitigate premium leakage.
Specific Pain Points
- Manual process for claims investigation
- Inability to analyze claims quickly before payout
- Customer data spread across multiple legacy applications
- Inability to tie new data sources into investigations
- Data acquisition and integration challenges
- Lack of a user-friendly interface for searching and reporting
- Inability to analyze unstructured data (text), which constituted 75%
of available data - A frequent “gut-feel” approach to assessing claims and fraud
The Solution
The Insurer used Insurance Analytics Cloud, which is phased, agile, and iterative. With this method, the company was able to realize ROI in shorter cycles, rather than at the very end. A phased approach also ensured a smooth transition, and allowed for data accuracy validation on the go. A glass claims conversion pilot was performed and later built upon.
“Prior to this project, we were only able to refresh a small amount of claims data. Now we have more data, and we can see it in real time. We can see things as they are happening. we can’t act on it as fast as we are finding it.”
Enterprise Analytics Lead
Our Innovative method of data transformation and analytics
succeeded in accomplishing two key objectives in close
succession
Integrate claims data from multiple legacy system to a single, enterprise-wide system
Create a claims data warehouse for analytics, reporting and downstream application
Speed of Implementation
- Aggregated Enterprise datasets in eight weeks rather than the traditional six months
- Claims fraud operationalization in four weeks
Key Business Outcomes
The claims team at the insurer was ecstatic. What would have been a multi year project was implemented successfully in half the expected time. The key benefits were:
More accurate fraud detection, which leads to better customer experience and higher retention
Upto 20% improvement in settlement time and 15% reduction in fraudulent claims
Lifting of Validated Claims Fraud Flags from 3.7% to 8.1%
Potential for 2.2% points Loss Ratio improvements within twelve months
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Business Problem
Creating a winning deal for customers requires in-depth thought process and planning. Herein, the Brand Managers of our client found themselves in a tough spot, answering tough questions like…
“Should I run this promotion with retailer Walmart first or Costco?”
“Should I offer a higher discount on single units or design a multi-buy promotion?”
“Should I run the promotion in the 1st week or 2nd week of Feb and for how many weeks?”
“What if… I increase the budget by 10%? Will I get higher sales? How it will Impact my ROI?”
Questions kept coming, and the planning process increasingly became complex. And due to the sheer volume of products and tight deadlines, Brand managers simply tweaked their earlier strategy. They had shifted approximately 35% of the last year’s promotion plans with a minor change in discount.
The impact? Profitability suffered because the team wasn’t able to achieve optimum performance with their planning approach.
Issues
- Challenge in deciding the retail channel, offers, product category
- Challenge in deciding promotion timelines
- Challenge in arriving at the promotion budget based on target sales, demand, etc.
- Lack of indicative figures of returns on campaign investments
The SpendO Way
SPENDO stepped in with a solution to transform promotion planning with AI. Our platform helped the company to –
- Learn from historical data patterns identified and communicated by AI
- Simulate millions of promotion scenarios
- Optimize every single promotion for better results
- Prescribe a winning deal
Earlier, the team could manually run few What-IF scenarios before selecting a promotion deal which they were not sure would give back optimal results. SPENDO ran 1000 different What-if scenarios for every single promotion, on the click of a button.
All the brand manager had to do was provide:
- The product category,
- The quarter they are trying to promote the brand,
- The retailers targeted for this promotion
SpendO provided optimized end to end promotional plan with details like which retailer, which product, starting the week, ending week, how much should be invested as Trade Spend and what will be the outcome if this promotion plan was to be executed.
Brand Managers could also compare the optimized plan with last year to see YOY improvement in results. Accordingly, they now had the knowledge to increase/decrease the budget for specific products and measure the outcomes.
Measuring Impact
- High performing promotions were achieved using the optimized plan prescribed by SPENDO.
- The brand managers realized lower spend and higher turnover with AI prescriptive models.
- They could perform what-if analysis using AI ensuring the final outcome was the best deal possible.
- Brand managers were now planning promotions with more confidence.
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COVID-19 NOTICE: Saama Analytics continues to help the pharmaceutical industry stand tall in these unprecedented times.
We're taking precautions to ensure the health and safety of our employees and partners. We remain operational remotely so new medical treatments can be delivered to patients sooner. |