Forecasting

Big Data Analytics Paves Way for a New Era in Healthcare

Medical professionals, hospitals and related healthcare organizations are facing challenges to reduce costs, provide coordinated patient care, standardize healthcare quality and deliver effective patient outcomes. Standard medical practice is moving from relatively ad-hoc and subjective decision making to evidence-based healthcare. A McKinsey study identifies a set of converging trends in the healthcare industry to a tipping point where Big Data and Analytics will play a major role. The trends are:

Demand for better data owing to cost-reduction pressures
Availability of relevant data at scale that includes
Clinical data in the form of EMRs and information exchanges
Non-healthcare consumer data
Technical capability
Government enabling and catalyzing market change

As US healthcare providers have dramatically increased the usage of Electronic Health Record system, according to data shared by US Department of Health and Human Services, “More than half of eligible professionals and 80 percent of eligible hospitals have adopted these systems, which are critical to modernizing our health care system.” This digitization drive is leading to generation of large volumes of data. Additional sources adding on to healthcare data include:

Development of new technologies such as capturing devices, sensors, and mobile applications
Extraction of genomic information has become much cheaper and quicker
People have become very active on social media channels
Interactions with various healthcare organizations through digital forms are increasing
Enormous amounts of medical knowledge/discoveries are being accumulated

In 2012, worldwide digital healthcare data was estimated to be equal to 500 petabytes and is expected to reach 25,000 petabytes in 2020.

Building analytics competency can help healthcare organizations harness “big data” to create actionable insights, set their future vision, improve outcomes and reduce time to value.  Leading healthcare organizations use analytics to differentiate, see the future and drive revenue growth. In a series of interviews with healthcare executives conducted by IBM Institute for Business Value, health care professionals feel that three business objectives can be addressed by Analytics in the Healthcare industry:

Improve clinical effectiveness and patient satisfaction
Improve operational effectiveness
Improve financial and administrative performance

How are Health Care Organizations Realizing these Benefits?

Improved Quality of Patient Care: As Healthcare Quality is one of the primary concerns, Analytics can assist in maintaining high quality of patient care by analyzing health outcomes data for different services to pinpoint lags in providing effective treatments consistently across a patient population or geographic area.

GE Healthcare leverages SAS technology to analyze patient health datasets and look for specific patterns and trends that help hospitals prevent adverse medical events. The SAS software mines patient-related data and provides critical insights, best practices, and benchmarking to enable clinicians to make informed decisions aimed at reducing medical errors and improving the quality of care. GE’s Patient Safety Organization provides its members a single common medical event-reporting platform, with comprehensive data analytics and advisory support to identify the root causes of risk, and help hospitals make lasting safety improvements.

Baptist Health’s CHF reduction initiative is a successful example of combining data analysis with new approaches to care delivery to improve quality and reduce costs. Data was merged from multiple sources across Baptist Health to present a full picture of the causes of CHF (Congestive Heart Failure) re-admissions. This data was analyzed to identify at-risk patients, determine resource utilization rates, and assess progress on a set of quality benchmarks. Dashboards offered providers the tools needed to use this data at the point of care, and education affirmed new roles and responsibilities. As a result Baptist Health was able to achieve its goals and reduce re-admissions for CHF patients at relatively low costs.

Improved Revenue Cycle Management: Revenue cycle management (RCM) refers to the process of managing claims, payments, and revenue generation and relies heavily on a combination of claims data, clinical data, and analytics technology. Analytic tools can help healthcare organizations determine patient eligibility, validate coverage, authorize services, assess payment risk, manage submissions, and track performance.

In a recent SAS podcast, Graham Hughes, the chief medical officer of analytics provider SAS, talks about the difference analytics can make in examining the cost and quality of health care in the United States. Hughes discusses a new analytics program SAS has launched to collect and compare health care payer claims data. By collecting this information in a data warehouse, the software allows users, including state agencies, health care providers, researchers, and eventually consumers, to analyze and compare health care cost data within a county or state. This solution can go a long way in eradicating opaque medical bills and establish transparent pricing policy in healthcare.

Better Resource Utilization: Analytics can build effective processes which result in removing system bottlenecks and reducing wastage. By appropriately estimating patient volumes, length of stay, and/or waiting times, inventory control systems and supply chain management processes can be effectively redesigned. Real-time data interpretation helps tremendously in developing efficient and optimized workflows.

Fraud and Abuse Prevention: Fraud refers to a calculated misrepresentation of facts aimed at convincing payors to process a false claim for financial gain while Abuse refers to neglect of accepted business or medical practices resulting in higher reimbursements. Cost trending and forecasting, care utilization analysis, and actuarial and financial analysis are commonly used analytic applications for preventing such cases.

Population Health Management: Healthcare providers carry the responsibility to educate people, spread awareness about lifestyle changes leading to disease prevention as well as its treatment. Their timely action in reaching people before, during, and after they need specific medical attention can save many lives. Analytics plays a key role here by assisting healthcare organizations in recognizing populations consuming the most resources or at greatest risk for hospital readmissions, enabling them to target high-risk groups to reduce costs and improve outcomes. Real-time data insights can help identify trends in disease prevalence, compare the effectiveness of different treatment options, and derive best practices.

The Louisiana Department of Health and Hospitals, for example, recently teamed up with geographic information systems (GIS) software vendor ESRI to map epidemiological issues, such as babies with low birth weights. Using ESRI’s GIS mapping software, the LDHH plugged in 354 points of data for every live birth in Louisiana. Sophisticated algorithms identified clusters among locations and then generated a map based on this data. For example, by crunching low birth weight records with geospatial data, the LDHH discovered correlations between low birth weight rates and crime-riddled neighborhoods. By flagging neighborhoods with low birth weights, preventative healthcare measures can reduce the number of high-risk births, thereby cutting healthcare costs.

According to the CDC, 26 million Americans currently have asthma that costs $3,300 per person annually in treatment costs. Asthmapolis, one of a new generation of digital health startups, has designed snap-on, Bluetooth-enabled sensors that track how often people are using their inhalers (along with location and time-of-day), along with analytics and mobile apps to help them visualize and understand their triggers and trends while receiving personalized feedback. In turn, the data collected by the solution enables doctors to identify patients who are at risk or need more help controlling its symptoms. This allows them to potentially prevent attacks before they happen, saving them the cost of hospitalization or a trip to the emergency room. In fact, Asthmapolis’ early studies found that this access to real-time data was able to reduce the number of people with uncontrolled asthma (or those not regularly using inhalers) by 50 percent.

Despite the fact that there is a lot of data that the hospitals already have (through their EHR systems), there are important pieces of data that the hospital system does not have. e.g., they do not know if the patient is a smoker or not (unless the patient fills that in their form), the size of their households, their consumption of various luxury, health and vice-goods etc. HealthVue, a Raleigh, NC based start-up is bringing together data from consumer sources, geo-spatial data, CMS data and combine it with EHR data to deliver a comprehensive and timely view of the populations.

Big Data initiatives can transform the healthcare industry to make it more coordinated and streamlined and be readily available at the right time, saving millions of lives and making high-quality patient care the new norm.

Analytics Forecasting Healthcare Insights

A Single Demand Forecast (Part 4): What Should Retailers Ask For?

After reading the preceding posts, you probably agree that a single forecast for all retail business processes is not the right thing to ask for.

But then how do you address the original issues that prompted the discussion:

Each business function is working with a different forecast in mind. Can’t all of us work off a single number?

The technology and personnel investments required to address these differing forecasting needs are mind boggling. Can we consolidate them?

These are valid issues that should be addressed. Our recommendations:

Establish a Sales & Operations Planning process to achieve alignment

Create a central forecasting methodology group that serves different organizations

Seek a data and technology infrastructure that serves multiple forecasting needs

Forecasting Insights Retail Supply Chain

Bottom-Up Forecasting: Be Wary of the Sophists

It is common for some retail guru’s/ practitioners to speak to the virtues of forecasting bottom-up as the best way to get a good forecast as a universal truth . Let’s examine the mathematical fallacies of this argument with an example.

Scenario: You are interested in creating a forecast for how much a store is going to sell in a given month. You want to use this as one of the inputs for the district management team. Your arch-nemesis (Dr.Z) and you are given the task of creating the forecast, the person with the best forecast is going to be promoted. The store sells only three items. You have 12 months of data (shown below). The race is on.

Dr. Z is a believer in bottom-up forecasting and is going ahead and creating forecast models using the finest software he has. Although the item sales figures are rather volatile, he confident that he can find enough causal factors to explain a lot of this variation. So, he sends his ace data folks to collect all the causal data he can to figure out what is causing the ups and downs in the forecast. He’s off to a flying start. Dr. Z successfully develops a model, where he has a forecast error of 10% for each of the three items and is very thrilled with the accuracy he’s able to get with the bottom-up forecasts (results below).

You just came back from vacation, and you learn about the fancy work that Dr. Z and his team have been doing. You have only one day left before you are expected to make the presentation. What do you do? You add up the sales for the items to look at the pattern of sales at the store level, as that is what you have been asked to forecast (shown below):

It’s evident that forecasting at total store level for this store is relatively simple and you may not need to do a lot of work. So, you submit that the forecast for this store is $100/ month.

From the example above, it’s evident that forecasting at the lowest level of the data (you have) is not always going to yield the best result. It may yield a good result, but is always a good idea to try multiple methods (top-down, bottom-up or a middle-out approach) to determine which method (or combination of methods) will yield the best result.

This is not just a clever example, but the nature of most processes in business (and nature). The lower level is always more noisy and harder to predict than the higher level (as noise across different time series cancels out like in the example above).

Forecasting Insights Retail Supply Chain

A Single Demand Forecast (Part 3): Be Careful What You Ask For!

A point I made earlier is worth repeating. The premise that I am challenging is a single forecast for all business processes, NOT the premise of a single forecast for a single purpose across the supply chain (which I think is a good idea, worth pursuing). The lack of clarity on this issue is what I am seeking to clarify here.

There are two problems with a single forecast that supports all business processes:
a)      A single demand forecast for all business processes is sub-optimal
b)      It is not possible to create such a single demand forecast (note that (a) trumps (b))

A single forecast for all business processes sub-optimal
When a business process requires a forecast, it has a certain expectation of forecast granularity and forecast horizon.

Forecast granularity: At what level of the business do you need the forecasts (to take the operational decision)? For most purposes in retail, the granularity is presented in terms of Merchandise – Location – Time.
Forecast Horizon: How far out do you want the forecast for, and how long do you need the forecast for. This is determined by the time it takes to act on a forecast, e.g., if you need to order an item from a supplier who has to make it, you generally need to send them a forecast way in advance in comparison to an item that the supplier keeps in stock.

Some examples in the table below:

Let’s consider the extreme examples here: Strategic Planning and Labor Scheduling. It is intuitive for managers to recognize that it is better to forecast at a higher level for strategic planning purposes rather than to roll-up hourly-item-store level forecasts to arrive at the forecast for the next four years. Intuitively we know that, while forecasting for the next 4 years, you need to consider the trends in your overall business, competition and macro-economic factors as opposed to your current merchandise mix, or last month’s hourly sales patterns. On the other hand, when you are doing labor scheduling for the next two weeks, it is critical for you to understand hourly sales patterns not only of similar time periods from past years but also of last week.

Beyond the fact that it intuitively does not make sense, it is mathematically sub-optimal. For more on this topic, read my previous post on this topic: Bottom-Up Forecasting: Be Wary of the Sophists.

It is not possible to create such a single demand forecast
Once again in our strategic planning and labor scheduling example, if the approach to a single demand forecast is roll-up the bottom up forecast. One needs to know the merchandise mix for the next four years at the store level. It is not possible. In many cases, you don’t know the precise merchandise mix that will be in the stores six months from now. So, it is not possible to create a strategic plan based on a roll-up of hourly forecasts. As a more practical example, it is not possible to create a merchandise financial forecast for the next 6 months based on hourly forecasts for the next six months.

In summary, the idea that a single forecast at a store-sku-week level will serve all business planning purposes is mis-conception. It intuitively does not make sense, mathematically incorrect and ultimately impractical.

Now that I have described what you should not ask for, I am sure you are thirsting to read what the solution is. We cover this in Part 4: What should retailers really ask for?

Forecasting Insights Retail Supply Chain

A Single Demand Forecast (Part 2): Why do Retailers ask for it?

Forecasting is a necessary evil in the retail industry (or for that manner in any industry). In the past two decades, retailers have realized that there is significant value in making science an integral part of their decision making.

Starting with Computer Assisted Ordering(CAO), retailers have continued to gain benefits from applying scientific approaches to processes such as: Replenishment, Allocation, Pricing, Promotions, Markdowns, Assortments, Size mix, Labor scheduling and Financial Planning. Forecasting is a critical component of all these areas.

Without exception, in all the areas mentioned above, a good forecast is atleast 60% of the answer. Hence, all these solutions come up with a forecast, and recommend how to control the operational levers (e.g., staffing) to meet such a demand forecast.

In many cases, making individual processes smarter, has provided rewards and the money is in the bank. Now comes the next series of questions:

Why do we need so many forecasts? If all of these processes are creating forecasts, is it not correct to assume that there’s one answer that’s better than all others?
Can we consolidate the efforts into something more meaningful (like a single forecast)? Forecasting requires skilled individuals who are quantitatively adept and have a solid understanding of the business. So, the inevitable question:
Should we be buying a forecasting system and put all these business processes on top of that technology? Good forecasting requires a lot of infrastructure – good granular sales and inventory data, capture of causal information, good master data for products and stores etc., In addition, large volumes of data need to be crunched in short weekend windows. All these  require large (and on-going) investments in hardware.

It is easy to lead to the conclusion, of course, we need a single forecasting system that produces one answer and the entire business runs on it. We all wish for a simple world, but Retail is not meant to be that easy, it wouldn’t be fun, would it?

Read my point of view on why these are the right questions, but a single forecast for all processes is not the answer in the next installment of this blog: A single demand forecast (Part 3) – Are you really sure you want one?

Forecasting Insights Retail Supply Chain

A Single Demand Forecast (Part 1) – Debunking the Myths

Retail executives are struck by the number of seemingly conflicting forecasting processes that their organizations are investing in. This series of 4 blogs examines the problem and recommends a solution, and it is not what you think.

Retail executives are struck by the number of seemingly conflicting forecasting processes that their organizations are investing in. In response to concern, there’s a whole industry that has emerged that touts the benefits of a “single demand forecast” and how they either provide a methodology or a software solution that delivers exactly to this expectation.

Many retail executives have asked me the same question. Unfortunately, the answer is not that simple.

Pause for a minute, and think – A single demand forecast – for what?
(a) For a single business process (e.g., replenishment) across the entire supply chain (to avoid the bull-whip effect); or
(b) For all processes (e.g., labor scheduling, replenishment, strategic planning etc)

If you answered, (a), you are an enlightened soul, hope you have a lot of power to make it happen in your organization (as multiple forecasts across the supply chain for the same thing does cause a lot of inefficiency). But you should note that you still have the problem of multiple forecasts for different purposes and may want advice on what to do about it.

If you answered (b), my writing here is specifically intended for you – to clarify the issues and hopefully convince you that you should be more specific in what you really want, otherwise, you might regret the outcome.

I don’t think there is a single demand forecast that will serve all the needs of a particular retailer (or for that matter any organization). If you really want it, I am sure someone can provide you one. Will it help you better sense and respond to customer demand in a profitable manner, most likely not. It’s not a technological weakness, it’s the wrong thing to ask for.

In this series of blogs, I will provide my point of view on this issue. The series will cover:

Part 2: Why do retailers ask for this? i.e., what problem are they seeking to solve
Part 3: Be careful what you ask for? i.e., how a retailer could actually lose their ability to respond to the market by seeking a single demand forecast
Part 4: What should retailers really ask for? Alternative thinking that actually addresses the problem and makes your organization more responsive to consumer demand

Forecasting Insights Retail Supply Chain
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