August 31st 2015

Wise Practitioner – Predictive Analytics Interview Series: Dr. Satyam Priyadarshy at Halliburton

Q: In your work with predictive analytics, what behavior do your models predict?

A: In the upstream oil and gas industry, there is need for predictive analytics at various phases of oil well lifecycle.  Predictive analytics plays a critical role in detecting events that could lead to increased cost of operations. Predictive analytics is also important to predict Black Swan events as well.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Upstream oil and gas is also referred as exploration and production (E&P) industry and focuses on health and safety across its operations. Predictive analytics plays a significant role in keeping this safety indicator score high for the organization and provides insights for intelligence based risk management.

Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: The operational cost depends on the drilling efficiency.  Drilling efficiency depends on rate of penetration (ROP). ROP is defined as advancement per unit time while the drill bit is on bottom and drilling. ROP is considered one of the most critical performance qualifiers. ROP depends on many factors like the weight on bit, the rotating speed, the lithology, the formation drillability, etc.  Predictive models that include these factors help provide lift in ROP which can also help improve significantly return on investment for drilling operations.

Q: What surprising discovery have you unearthed in your data?

A: In the E&P domain there are two important metrics – Non-productive time (NPT) and Invisible Loss Time (ILT). NPT occurs throughout the different phases of drilling, production and reservoir management.  Predictive modeling helped us in determining the root causes of NPT and ILT.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: Predictive analytics is used in many industries successfully with much ease. However, the oil and gas industry has one of the most complex operations of any industry. Many challenges and opportunities exist in this industry that can benefit from predictive and cognitive analytics on E&P big data.

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Don’t miss Dr. Priyadarshy’s conference presentation, Challenges in Leveraging Predictive Analytics for Big Data in Oil and Gas on Monday, September 28, 2015 at 2:40 to 3:25 pm at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

 

 

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August 27th 2015

Wise Practitioner – Predictive Analytics Interview Series: Werner Britz at RCS Group

Q:  In your work with predictive analytics, what behavior do your models predict?

A:  The predictive model developed and part of business operations are Application and Behavior models predicting risk. There are a number of different Behavior Risk Scorecards in play depending on the portfolio and delinquency level. Forward Roll Behavior Risk Scorecards are actively used in collections strategies. At customer solicitation we utilize Response Scorecards to cut cost and to market more effectively to the correct customers.

Q:  How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A:  Differentiating better between good and bad prospective customers is where the real value op predictive analytics starts. Predictive models form the cornerstone of the front-end Risk Assessment Solution and play a major part in determining the product offering. Taking the correct action on booked customers is made possible by predicting how the customer is going to behave in the future.

Q:  Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A:  Redevelopment of Loans Application Scorecards has made it possible to either increase the acceptance rate for the same bad rate or accept the same volumes but with a much lower bad rate. The business decision would depend greatly on which solution in the above mentioned range would increase advances and drive down charge-offs.

Q:  What surprising discovery have you unearthed in your data?

A:  Since Predictive modelling is the process of classify homogenous groups together and treating/offering them the same, the decomposition of these homogenous groups together with a slightly different risk/product modelling variable could further enhance product offering and competitive edge.

Q:  Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A:  Double digit lift in Recovery rate made possible by engaging predictive analytics and optimization theory.

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Don’t miss Werner Britz’s conference presentation, Recoveries: External Debt Collection Optimization on Monday, September 28, 2015 from 2:40pm – 3:25pm at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

 

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August 19th 2015

Wise Practitioner – Predictive Analytics Interview Series: Benjamin Uminsky, Los Angeles County

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of his upcoming conference presentation, Mining the Voter File, at Predictive Analytics World for Government, Oct 13-16, 2015, we asked Benjamin Uminsky, Executive Benjamin UminskyAssistant, Data Scientist, Los Angeles County Registrar Recorder/County Clerk’s Office, a few questions about his work in predictive analytics.

Q: How would you characterize your agency’s current and/or planned use of predictive analytics?  What is one specific way in which predictive analytics actively drives decisions in your agency?

A: The Los Angeles County Registrar Recorder/County Clerk is already using time series forecasting to anticipate revenue changes from our real property recorder operations for future fiscal quarters and years. We are also committed to developing an applied predictive model to help our poll worker recruiters better identify likely volunteers that will show up on Election Day and serve as poll workers. Our individual recruiters have good intuitions about who to contact and are also able to get commitments from poll workers, however, a predictive model will be able to see the entire data set of responses from potential poll workers that no recruiter could see or analyze at an individual level. The model will serve to be a new tool in helping our poll worker recruiters sift through their lengthy lists of potential poll workers and help them find the right people to call for Election Day.

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: Moving from a non-data driven management culture has been a significant challenge, one that is an ongoing journey for our Department. Part of the problem is a lack of trust that managers have in the statistical methods. The lack of trust is ultimately associated with an unfamiliarity in the methods and a fundamental lack of understanding as to how the methods work. Another large problem is moving managers out of their comfort zones. We are asking so much more of our managers when they make data-driven business. It is not enough anymore for a manager to justify a business decision with little more than intuition, limited observation of their operations, and narrow input from subordinate managers/supervisors.

Q: Can you discuss any near term goals you have for improving your agency’s use of predictive analytics?

A: We want to continue building bridges with our management teams and help them become more familiar with what a predictive analytic can do for them, without overwhelming them with the conceptual aspects of the predictive modeling process best left to the data scientists. Part of building trust with our management teams is in finding ways to communicate how a predictive model works, using intuitive explanations and reassuring managers that the predictive models are not being developed in order to replace their own judgement and intuitions but to complement them. Our Department is currently developing a crash course in data driven decision making. We hope that this training, intended for our managers, will help acclimate them and make them more comfortable with a shift to a new data driven decision making culture.

Q: Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.

A: Our poll worker recruitment prediction model will hopefully save the Department the added costs of temp hiring and overtime spending. However, these estimations are only theoretical as we have not yet had an election that we can test the model on.

Q: What tools are you currently using to perform data analytics?

A: Our Department is now a R shop and we are able to engage in a full suite of data analytic techniques using R (web scraping, data tidying and cleaning, exploratory data analysis, data visualization, data mining, applied predictive modeling, etc.).  Importantly, our Department has collaborated with a number of academic institutions to further enhance our Data Analytics program. The academic institutions that we have engaged with are accustomed to doing statistical learning projects using R and Python. The fact that we use R as well makes sharing and learning of new data analytic techniques much easier with these academic institutions.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: I think the biggest take away from my talk is how we are putting our voter and election data to good use by using both supervised and unsupervised statistical learning techniques. For those that attend the talk, they will see what kind of data our Department collects and how it can be both transformed and mined so as to extract important and interesting business insights that can enhance our service delivery.

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Don’t miss Benjamin’s conference presentation, Mining the Voter File on Wednesday, October 14, 2015 from 11:20 am to 12:05 pm at Predictive Analytics World for Government. Click here to register to attend.

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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August 17th 2015

Wise Practitioner – Predictive Analytics Interview Series: Jessica Taylor of St. Joseph Healthcare

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of her upcoming conference co-presentation at Predictive Analytics World for Jessica TaylorHealthcare Boston, Sept 27-Oct 1, 2015, we asked Jessica Taylor, Care Manager at St. Joseph Healthcare, a few questions about incorporating predictive analytics into healthcare. Catch a glimpse of her co-presentation, Improving Care Coordination and Reducing Readmissions Using Real Time Predictive Analytics, and see what’s in store for the second annual PAW Healthcare conference in Boston.

Q: In your work with predictive analytics, what area of healthcare are you focused on?

A: I am currently focused on reducing costs, improving quality of care, improving patient experience, and improving transitions of care.

Q: What outcomes do your models predict?

A: The model predicts the risks for Emergency Room Visits, Admissions to the Hospital, readmissions, and decline in health status.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions or impacts operations?

A: The ability to be proactive instead of reactive with our patient’s care has been integral to our team. The analytic tool allows my team to focus on the patients at risk, allocate our time and resources more efficiently, and decisively. We are able to create work lists that identify patients who are in the community or currently in patient who may be at risk or need support.  As the tool is predictive we can connect with those at risk before an event or exacerbation occurs. This all translates into higher quality of care for our patients, delivered at the time when they need it most.

Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: The ability to have timely accurate information at our finger tips, in tandem with patient lists, allows my team to work more efficiently and increase our productivity. We no longer find ourselves duplicating task, and working to the level of our licensure. This allows us to connect with more patients, provide more support, and improve more outcomes.

Q: What surprising discovery have you unearthed in your data?

A: With the implementation of the predicative tool we have reduced our Emergency Room visits, both initial and returns. This was a direct result of having the ability to see such robust current information, allowing us to identify opportunities for education with our patients.

Q: What areas of healthcare do you think have seen the greatest advances or ROI from the use of predictive analytics?

A: Population management, coordination of care, transitions of care.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: How the implementation of predictive analytics can improve coordination of care, improving quality of care, reduce costs, and improve patient satisfaction.

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Don’t miss Jessica’s co-presentation, Improving Care Coordination and Reducing Readmissions Using Real Time Predictive Analytics, at PAW Healthcare on Tuesday, September 29, 2015 from 10:05 to 10:50 am. Click here to register for attendance.

By: Jeff Deal, Conference Chair, Predictive Analytics World Healthcare

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August 14th 2015

Wise Practitioner – Predictive Analytics Interview Series: COL William Saxon, Department of the Army

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of his upcoming conference presentation, From Wisdom to Insight: Driving William_SaxonStrategic Decision Making with Predictive Analytics, at Predictive Analytics World for Government, Oct 13-16, 2015, we asked COL William "Bobby" Saxon, Chief, Force Management Enterprise Division, Force Management Directorate, Office of the Deputy Chief of Staff Department of the Army, a few questions about his work in predictive analytics.

Q: How would you characterize your agency's current and/or planned use of predictive analytics?  What is one specific way in which predictive analytics actively drives decisions in your agency?

A: The US Army is actively pursuing predictive analytic solutions in multiple areas. Most notably we are looking at soldier health and welfare specifically with regard to suicide prevention. We are encouraged by our early work and see potential for identifying groups of soldiers at the highest risk to allow us to intervene early to assist the soldier. Our concern for soldier privacy is high which challenges us to balance the risk with the rewards in this area. Other areas that predictive analytics may be valuable are with maintenance and readiness. Both areas directly impact our ability to defend the nation. Predictive analytics may help us reduce our maintenance downtime and see ourselves better with regards to readiness.

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: The Army has come a long way in the last decade with capturing, managing, sharing, and utilizing data to assist decision makers. Multiple efforts driven by the Secretary of the Army and the Army CIO/G-6 have increased data quality and identification. We still have a ways to go but our environment is much more data friendly today that just a few years ago.

Q: Can you discuss any near term goals you have for improving your agency's use of predictive analytics?

A: Several organizations within the Army are actively pursuing predictive analytic solutions. Efforts revolve around suicide prevention, maintenance, readiness, and manning. Our hope is to see these efforts mature to allow us to reap benefits not commonly found with basic analytics. If so, these successes should encourage others across the Army to delve into predictive analytics.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: Consuming large amounts of previously tightly held data is challenging. I will speak to the governance and political challenges associated with building a data system for business intelligence, analytics and eventually predictive and prescriptive analytics.

Tasked with providing the Army with access to visual, actionable analytics, COL Bobby Saxon led the redesign of the Enterprise Management Decision Support (EMDS) system to provide a strategic view of readiness for Army Leaders. The system gives brings together disparate data to provide one access point with valuable analytic displays. But this is only the beginning. With the vast amount of historical data the system brings together, the ability to forecast readiness and provide predictive models for future readiness is essential. During this session, COL Saxon will describe the ways he worked across the army to create EMDS, and the way forward for predictive analytics, as well as discussing the “art of the possible” that drives his vision for the program.

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Don't miss COL Saxon’s conference presentation, From Wisdom to Insight: Driving Strategic Decision Making with Predictive Analytics on October 13, 2015 from 11:20 am to 12:05 pm at Predictive Analytics World for Government. Click here to register to attend.

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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August 12th 2015

Wise Practitioner – Predictive Analytics Interview Series: Patty Larsen, Co-Director, National Insider Threat Task Force

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Don’t miss Patty’s panel discussion, Insider Threat Panel on October 13, 2015 from 10:30 to 11:15am, at Predictive Analytics World for Government. Click here to register to attend.

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

 
 
 

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August 10th 2015

Wise Practitioner – Predictive Analytics Interview Series: Bin Mu at MetLife

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Establishing Bin_MuValue: The “Making Impact Through Analytics” Framework, at Predictive Analytics World Boston, Sept 27-Oct 1, 2015, we asked Bin Mu, Vice President, Business Analytics at MetLife, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: We use predict analytics to power the business functions across insurance operation.  For example, when it comes to Channel, we use marketing mix, channel optimization to drive volume; for Acquisition, we have propensity models to uplift response, retention and cross-sell; for underwriting, we use predictive model for risk adjustment; and for operation, we have models to address fraud, lapse, and claim complexity.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: At MetLife, we always look at incremental, or ROI, as a way to quantify the value of the predictive analytics.  In order to that, we have to ensure our business partners are “onboard” with the insights and use that to drive decisions.  We apply the Insights That Matter framework in our operation, and it has proved to be instrumental to the success of both sides.

Q:  Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A:  I would love to, but our legal does not allow me to disclose any numbers.  However, we have seen the uplifts from our test group results at or above the industry average.

Q:  What surprising discovery have you unearthed in your data?

A: This is the most favorite part of my job – unearth surprising insights.  One correlation we have found is in how weather can impact dental claims. There tends to be a higher claim volume during a bad weather season.  I guess when people cannot go on vacation, they go to visit their dentists!  LOL.

Q:  Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: I want to share with fellow data scientists and predictive analytics practitioners a secret sauce of how to make real impact to the business through the insights they discovered using predictive analytics.  As we all have experienced, sometimes we are frustrated with our business partners because they either do not use our analysis to make smart decisions, or they do not use it the right way.  What I will share with the audience will address that pain, as well as putting predictive analytics into a healthy growth cycle. 

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Don't miss Bin’s conference presentation, Establishing Value: The “Making Impact Through Analytics” Framework, on Monday, September 28, 2015 at 3:55 to 4:40 pm, at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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August 7th 2015

Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Picking the Right Modeling Technique for the Problem, at Predictive Analytics World Boston, Sept 27-Oct 1, 2015, we asked Michael Berry, Analytics Michael_BerryDirector at TripAdvisor for Business, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: At TripAdvisor for Business, one of our most important products is subscription-based. We price our subscriptions based on the value our product will deliver to hoteliers in the form of increased direct bookings on their web sites. This means predicting their future traffic, click-through rates, conversion rates, room rates, average length of stay, and so on. Beyond that, I worry about all the usual things subscription-based businesses worry about: What is the probability that a subscriber will renew? What actions of ours can increase that probability? Which non-subscribers are the best prospects? What actions on our part will lead to increased owner engagement?

Q:  How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: I’ve already mentioned pricing.  Another area is sales efficiency.  There are over 900,000 hotels listed on TripAdvisor and our salespeople can’t reach all of them. We use predictive models to pick which properties to call.

Q:  Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: No. In a public forum like this, I generally show graphs with no numbers on the axes. Of course internally we measure things like the increase in expected value of sales leads so we know how valuable our work is.

Q:  What surprising discovery have you unearthed in your data?

A: Here’s one that surprised me a bit when I first started looking at hotel ratings data: The average bubble rating of all reviews is higher than the average bubble rating of all hotels.  Both are pretty high since people tend to like the places they picked, but the difference is noticeable.  How can that be?  Well, some properties have enormous numbers of reviews. Think The Bellagio in Las Vegas.  These properties tend to be traveler favorites so their thousands of reviews bring up the average review score. But the Bellagio is still just one hotel, so it doesn’t affect the average hotel score any more than a Motel 6 on a truck route somewhere.

Q:  Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: There is no one best type of predictive model; you need to pick your tools to match the problem you are trying to solve.

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Don't miss Michael’s conference presentation, Picking the Right Modeling Technique for the Problem on Monday, September 28, 2015 at 10:30 to 11:15am at Predictive Analytics World Boston. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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August 5th 2015

Wise Practitioner – Predictive Analytics Interview Series: Catherine Templeton, PAWGOV Keynote Speaker

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of her upcoming keynote conference presentation, Reforming Government: Where is the Catherine_TempletonPublic Servant to Start? at Predictive Analytics World for Government, Oct 13-16, 2015, we asked Catherine Templeton, Former Director of the South Carolina Department of Health and Environmental Control and Former South Carolina Secretary of Labor, a few questions about her work in predictive analytics.

Q: How would you characterize your state's current and/or planned use of predictive analytics?  What is one specific way in which predictive analytics actively drives decisions in your agency?

A: In our tax, Medicaid, workforce, and social services agencies, we are able to identify fraudulent returns and those situations that our investigators should address first. In our health and social services agencies, we can move our inspection resources to the health facilities and child safety situations that need the most oversight.  In our environmental agency, we can model potential hazardous contamination. 

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: The first challenge is the lack of historical data governance creating unreliable data – so think “trash in – trash out.”   Almost as challenging is the lack of sufficient personnel and expertise to implement the solutions because our public servants in IT are usually employed to keep the systems running, not mine insights for business decisions.

Q:  Can you discuss any near term goals you have for improving your state's use of predictive analytics?

A: Our state is currently implementing analytics for prescription drug monitoring, health inspections, environmental permitting, income tax return fraud, and certificates of need for public health.  Additionally, agencies are exploring the use of predictive analytics to stop fraudulent payouts for Medicaid provider and participant programs at the Attorney General’s office and the Department of Health and Human Services, at our lottery commission, and with our Department of Social Services SNAP, TANF, and child safety programs.

Q: Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.

A: Using analytics in any of these programs creates operational efficiencies that avoid wasted personnel time and budgetary costs.  For fraud identification, obviously, we can stop improper payments before we pay criminals or have to expend the resources to pay and chase the recovery.  The other initiatives are aimed at putting our resources where they bring us the most value in return, whether that is curbing controlled substance abuse or moving a child into the proper support program.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: Not stopping fraud or measuring the success of our government programs is not going to be politically or fiscally tolerated by the citizens we serve.  It is difficult to go through the implementation of predictive analytics, but, having experienced failure and success, I can identify our common hurdles and share the solutions we employed to clear them.

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Don't miss Catherine’s keynote conference presentation, Reforming Government: Where is the Public Servant to Start? on Wednesday, October 14, 2015 from 8:45 to 9:30 am at Predictive Analytics World for Government. Click here to register to attend.

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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August 3rd 2015

Wise Practitioner – Predictive Analytics Interview Series: William Wood of St. Joseph Healthcare

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of his upcoming conference co-presentation at Predictive Analytics World for Healthcare Boston, Sept 27-Oct 1, 2015, we asked William Wood, VP, Medical Affairs at St. Joseph Healthcare, a few questions about incorporating predictive analytics into healthcare. Catch a glimpse of his co-presentation, Improving Care Coordination and Reducing Readmissions Using Real Time Predictive Analytics, and see what’s in store for the second annual PAW Healthcare conference in Boston.

Q: In your work with predictive analytics, what area of healthcare are you focused on?

A: We are focused on managing at risk patients to provide better care coordination to reduce unnecessary utilization.  This includes reducing unnecessary inpatient readmissions and emergency room visits for patients managed by our primary care physicians.  We also use the analytics to identify patients with high mortality that might benefit from services like palliative care.  We also use the analytic tool to identify uninsured patients who are accessing our emergency department and primary care practices so we may connect them with the healthcare exchange, MaineCare (Medicaid), and community resources.  We are able to focus on specific chronic diseases, identify gaps in care, and opportunities for interventions.

Q: What outcomes do your models predict?

A: There are two basic types of clinical risk models we use, population based risk models, and event based risk models.  All the models are updating nightly providing near real time risk for all of our patients under management.

This population risk models are person based and are used by population health managers to understand each patient or member’s risk of an event in the future 12 month period.  This includes the following models:

  • Emergency Department (ED) Visit Risk
  • Inpatient (IP) Admission Risk
  • Predicted Future Cost
  • Risk of a Stroke
  • Risk of Diabetes
  • Risk of an AMI
  • Risk of Mortality

The event based risk models are encounter based and are triggered by an inpatient admission or an emergency department visit. These risk models are applied upon admission showing the likelihood of a 30 day readmission for inpatients and a 30 day return to the emergency department for emergency patients.

  • IP 30 Day Readmission
  • ED 30 Return Visit

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions or impacts operations?

A:  We believe we are better managing inpatient readmissions and appropriate emergency room utilization by proactively targeting the at risk population for proactive care management.  Predictive risk scores help us use our limited care resources more effectively by targeting the higher risk populations.  The tools have also helped us design a care management model that covers the care continuum.  We are able to focus our efforts on the high risk population, working more efficiently to allocate our resources to those that are in most need, and at highest risk.

Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: The implementation of computerized predictive risk models immediately eliminated the manual work effort of our nurses assessing risk during discharge planning.  This was a savings of 1000s of nurse hours that are now spent on providing the interventions to patients.  For readmissions and emergency room utilization the recent trends are showing a decrease which we expect to translate into hard ROI from reduced penalties.  We are embarking on ACO type population risk contracts which would provide the financial incentives and ROI for better population management, but that is longer term.

Q: What surprising discovery have you unearthed in your data?

A: We are identifying at-risk patients that were not obvious to the clinicians previously.  Although many patients are well known to care management, we routinely identify “unknown” patients at risk for mortality or MI/CVA/DM through the tool.  We have improved our follow-up for patients as they have transitioned from other healthcare organizations/skilled facilities to primary care. We now have the ability to view patients who are currently in a facility along with their potential risks. This allows us to better plan for their needs upon discharge and to see when they have left the facility. We are not dependent upon a call to alert us from the facility. We are able to be proactive and not reactive in care. This not only increases our productivity but decreases the risk of patients experiencing negative outcomes and readmissions during that critical 72 hours after discharge.

Q: What areas of healthcare do you think have seen the greatest advances or ROI from the use of predictive analytics?

A: We are currently and primarily still a fee for service hospital, which is financially incented to manage volume and throughput based on what happened to a patient retrospectively.  As we participate in ACO and population financial risk contracts along with bundle payment models, we have implemented the risk based tools to start managing patients prospectively. 

The biggest benefit to our organization has been the shift in thinking to better understand what will happen to a patient after they leave our 4 walls.  We have also been better able to identify gaps in care as we refine the care management model. 

We are starting to see early results from this mind set change in reduced readmissions and ED visits.  As the financial incentives continue to change, we will see continued investment in staffing, training and systems to better manage patients proactively which inevitably requires predictive risk analytics. 

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: Predictive analytics is only a part, an important one, but still only one part of the improvement cycle.  An organization needs to invest in the adoption of risk tools to best understand how to apply them.  This includes workflow integration and staff training.  The risk tools gave us better information that we didn’t have previously, and we needed to then understand what to do to a patient given their risk profile.  The predictive math doesn’t provide the second step, and we needed to invest in the development of care interventions tied to patient risk profiles and then train our staff accordingly.  The adoption, workflow integration, and training are a necessary part of the total cost of ownership to take advantage of predictive risk scores.

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Don't miss Williams’s co-presentation, Improving Care Coordination and Reducing Readmissions Using Real Time Predictive Analytics, at PAW Healthcare on Tuesday, September 29, 2015 from 10:05 to 10:50 am. Click here to register for attendance.

By: Jeff Deal, Conference Chair, Predictive Analytics World Healthcare

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