Welcome to the MedMetrics Blog

The MedMetrics blog provides comments and insights regarding the world of Workers’ Compensation, principally, issues that are medically-related. The blog offers viewpoints regarding issues affecting the industry written by persons who have long experience in the industry. Our intent is to offer additional fabric, perspective, and hopefully, inspiration to our readers.

Search The MedMetrics Blog

Monday, February 24, 2014

Don't Expect Analytics Alone to Change Outcomes

by Karen Wolfe

Research suggests 40% of major business decisions are based not on facts, but on the manager’s gut.[1] Most of those gut-based decisions are not life-endangering. However, many of them can directly impact the organization’s viability.

In Workers’ Compensation, critical decisions are made not only by managers, but by front-line workers. Claims adjusters make course-swerving decisions every day. How accurate and timely are the decisions they make? What about the decisions made to ignore information and avoid taking action?

How are outcomes traced back to the decisions made and not made? What accountability is built into the process? What kind of decision support is available? Are any of the decisions based on objective data?

Business intelligence
Business intelligence, derived from analytics (data analysis) can inform business decisions throughout the organization. More importantly, the analytics must be infused into operations to lead to action. Only analytics that are linked to operations can consistently and positively provide decision support that create positive outcomes.

“If you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes—the methods by which work gets done and value gets created.”[2]  

Sequestered knowledge
Both garden variety analytics and highly sophisticated predictive modeling are common now in many organizations. But few apply the analytics to their operational process effectively to make a significant impact on outcomes and profitability. Unfortunately, the best practice exceptions are not often found in Workers’ Compensation. Applied analytics in Workers’ Compensation, and particularly those relating to the medical aspect of claims, is rare.

In Workers’ Compensation, analytics are most often sequestered in the executive suite. Analytic results are shared at board meetings and are lavishly portrayed at marketing shindigs or annual reports. They are represented in colorful graphics while decision-makers ponder their meaning. Nevertheless, just executing and reviewing analytics has little impact on decisions made by middle managers and front line workers.

Analytics must be linked to operations to make them actionable.

Dashboards are not actionable
Dashboards have become a fashionable way to display analytic results, but they don’t link the analytics to operations. They do not change behavior. They are designed to portray conditions in the organization across a broad swath of indicators in one view.

An example is a hospital where a dashboard displays vital operational statistics including admissions and discharges for the period, average lengths of stay, and acuity rates. Dashboards are interesting and informative of business activity. The remaining question is, who should do what to incorporate the knowledge? What should be done operationally to effect the indicators going forward?

Basically, dashboards are for viewing only, and unless the organization has designed response procedures for assigned persons, the impact is negligible. Dashboards have no direct relationship with operations and no mechanism for tracking responses to the information. Changes in process are anecdotal only.

Corporate communications, regardless of how sophisticated, do not effectively translate analytic knowledge into action on the front line.

Actionable analytics
For analytics to be actionable they must be linked to, and fused into operations automatically. Front line workers must be led by the information process to take appropriate action.

The best way to do this in Workers’ Compensation is to electronically monitor the data, execute the analytics in real time, and initiate the desired actions among workers by means of an automated electronic message. This approach hurdles the communication log jam found with immediate, specific information sent directly to the person who can best act on it. 

Infusing analytics into operations requires a computerized system specifically designed to monitor and analyze all transactions and to automatically send alerts, thereby communicating the results of current analytics to the appropriate persons.

When the computer system identifies a high risk situation in a claim, the appropriate person is automatically notified electronically. At the same time, the system should also keep an audit trail noting all claims identified, the reason, and to whom the alert was sent. The end-to-end process will infuse analytics into the process, render the process more efficient, and establish accountability.

When a person is alerted of a high risk claim, action is expected. Some organizations have formal procedures for the actions required under a specific set of circumstances. Such actions are documented so that outcomes can be traced back to the claim conditions, initiatives taken, and the persons involved. The results are exponentially improved while the data gathered in the process enhances organizational performance intelligence.

Analytics by themselves cannot and will not change outcomes. But analytics linked to operations through specifically designed systems will effect both the process and outcome. Only actionable analytics create value.

Karen Wolfe is the founder and president of MedMetrics®, LLC, an Internet-based Workers’ Compensation analytics company. MedMetrics offers online medical management apps that link analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

[1] Davenport, T. Harris, J., and Morison, R. Analytics at Work, Smarter Decisions, Better Results. Harvard Business School Publishing Corporation. 2010.
[2] Ibid

Monday, February 10, 2014

Why Poor Data Quality is Not an IT Problem

by Karen Wolfe

Everyone knows the old adage about data: “garbage in – garbage out”. Now, however, the meaning of the phrase is magnified because the volume, quality, and impact of data has reached unprecedented levels. With increasing importance and reliance on analytics and predictive initiatives, as well as the promise of metadata analysis, the importance of quality data is paramount.

This is not intended as a doomsday message. It’s more of a not-so-gentle nudge to change business practices regarding data management because doing otherwise will lead to significant financial disadvantages. Unfortunately, the people who have the power to change, frequently think the problem belongs elsewhere.

Not an IT problem!
The misconception is if it’s data, it must be an IT problem. However, only management has the power to change data quality, not IT. It is a management decision and responsibility to hold people and organizations accountable for data quality. The following is an excerpt from and email I received recently from our IT describing one client’s data. Unfortunately, the problem it describes is common.

“There is a field for NPI number in the data feeds but it is not often populated.  When it is populated we can definitely use that information to derive the specialty and possibly to determine the individual provider rather than the practice or facility.”

This example highlights a widespread problem in Workers’ Compensation data. Even though a field is available to capture a specific data element, in this case, NPI (National Provider Identification) number, it is not populated. This number is derived from medical bills and the reason for the omission should be thoroughly investigated.

The information trail
The first place to look is upstream in the information trail, to the submitting provider or entity. Standard billing forms such as the HCFA 1500 contain a field for NPI, but it may not be filled. Second along the information hand-off line is the bill review company. Is the NPI number being captured from the bill?

If the provider is submitting the NPI, is the bill review company capturing it? Then, if the bill review company is capturing the NPI, is it included in the data set transmitted to the payer? Once the source of the problem is discovered, management must require the necessary process changes.

Management intervention
If the submitting provider is not including the data needed, in this case the NPI number, the best management intervention is refusing to pay incomplete bills. Likewise, if the bill review company or system is not capturing the data or is not passing it on to the payer, management must demand the data needed.

Seemingly trivial data omissions can lead to multiple other problems. Another common data problem is the submitting provider or entity entering a facility, group, or practice name while excluding that of the individual treating physician. Management should insist upon using the individual treating physician name and NPI number rather than the entity name only. Systems should capture all three pieces of information.

Bad data comes in many forms beyond missing data in existing fields. Other kinds of bad data include erroneous data and duplicate records in the data. Regardless of the form and source of bad data, the challenges on the horizon are significant. The simple fact is, benefits from analytics to gain cost advantages are not accessible to those with poor data quality.  

Management owns data quality
Accurate and complete data is the only affordable and practical resource on the horizon to advance to the next levels of medical management and measureable cost control. Only management can insure data quality.

Karen Wolfe is the founder and President of MedMetrics, LLC, an Internet-based Workers’ Compensation analytics company. MedMetrics offers online apps that intensify medical management, including Provider Performance Analysis, Predictive Intelligence Profiles with Alerts, ICD-9 Predictive Scores, and Ask-the-Data Query Library. MedMetrics will import and analyze your data to identify omissions and opportunities for data quality improvement. karenwolfe@medmetrics.org