Tag Archives: data monitoring

A How-To on Nurse Case Management

Nurse case management (NCM) has a powerful impact on workers’ compensation claim cost and outcome. Positive results of nurse involvement have long been anecdotally accepted, but widespread evidence of nurse impact has not emerged, and objective proof of value is still missing. Several factors account for this.

Inconsistent Referrals

For one thing, NCMs are usually considered an adjunct to the claims process, called upon in sticky situations. Too often, referrals to nurses is a last resort rather than an integral and standardized part of claim management. When claims adjusters have the sole responsibility to refer to NCMs, it can be subjective, uneven and therefore unmeasurable.

Besides receiving referrals for sundry issues at different points in the course of the claim, nurses have not clearly articulated their case management interventions. Claims adjusters sometimes misunderstand the nurses’ approach. However, consistent referrals and standardized procedures can bring about major change.

Consistent referrals

Referrals to NCM should be made based on specific medical conditions in claims such as comorbidity like diabetes or problematic injuries like low back strains that tend to morph into complexity and high cost. Specific risky situations found in claims data should automatically trigger NCM notification.

A recent article published in Business Insurance, “Nurses a linchpin in reducing workers’ comp costs,” points out how Liberty Mutual has developed a tool that notifies claims adjusters of cases that would most benefit from a nurse’s involvement. Decision burdens for claims adjusters are eliminated. Referrals to NCM are automatic based on specific high-risk situations found in the claim. Inconsistency disappears, and several benefits evolve from this approach.

Process standardization

An operational process can be dissected and categorized, thereby gaining better understanding of its components and relative importance. Review the data to determine which medical conditions in claims result in longer disability, lower rates of return to work and, of course, higher costs. Select the conditions in claims that should activate an NCM referral.

An example is a mental health diagnosis appearing in the data well into the claim process. A mental health diagnosis appearing during the claim for a physical injury such as a low back strain is a strong indicator of trouble. The injured worker is not progressing toward recovery. However, the only way to know this diagnosis has occurred in a claim is to electronically monitor claims on a continuous basis.

Data monitoring

To identify problematic medical situations in claims and intervene early enough to affect outcome, the data should be monitored continually. Clearly, this is an electronic, not a human function. When the data in a claim matches a select indicator, an automatic notice is sent to the appropriate person.

Standardized procedures

Catching high-risk conditions in claims is just the first step. NCM procedures must be established to guide responses to each situation triggered. Standardized procedures should describe what the NCM should evaluate and advise possible interventions. Such processes not only explain the NCM contribution, they assist in documentation and are the basis for defining value.

Measuring value

NCM has been under-appreciated in the industry because measuring apples-to-apples cost benefit has been impractical. When claims adjusters decide about referring to NCMs and individual nurses create their own methodology, variables are endless and little is measurable.

In contrast to the subjective approach, specific conditions in claims found through continuous data monitoring can automatically trigger a referral to the NCM. In response, the nurse is guided by the standard procedures of the organization. When referrals are based on specific conditions in claims and response procedures are delineated, outcomes can be analyzed and objectively scored.

Unleash The Power Of Real-Time Data Monitoring For Managed Care

Data monitoring means applying technology and analytics to gain real-time intelligence and decision support in claims management. Moreover, data monitoring is the way to link analytics to operations, thereby making them actionable.

Nearly everyone in Workers’ Comp is trying their hand at analytics now. The problem is that organizations that have implemented analytics do not seem to know what to do with them. Analytics are great for reporting activity and corporate status to boards of directors and shareholders. Analytics uncover cost drivers and are impressive when graphically presented in annual reports. But analytics must be taken to the next level to actually impact costs or improve claim outcomes. To be truly effective, analytics must be linked to operations, thereby making them actionable. One way to achieve that is through concurrent data monitoring.

Unified And Concurrent Data Platform
Claim data must be gathered from all relevant sources and integrated in a single platform. In Workers’ Compensation, organizations still find their data spread across multiple silos. Bill review, pharmacy, claims adjudication, and medical case management are all important deposits of data. However, claims cannot be adequately analyzed unless the full scope of data is gathered, integrated, and available for comprehensive assessment concurrently.

Some say bill review data alone is adequate for medical analysis. Yet, bill review data cannot reveal work status or return to work, indemnity costs, or final disability rating. These data, derived from the claims adjudication system, must be considered in combination with billing data in order to draw reasonable conclusions. Claims should be evaluated holistically, not in fragments by assorted participants.

Computerized Data Monitoring
Manual data monitoring is humanly challenging, if not impossible. The detail in claims, including many events over a period of months and years, cannot be absorbed or retained even by the most astute claims adjusters. Moreover, new information is added to claims continually that must be combined with historic information within a claim. Only a well-designed computerized system can handle the job.

For data monitoring to be valuable, it must be consistent and never random. A computerized data monitoring system can scrutinize all the data once it is integrated at no greater effort or cost than to monitor just a few items. It is a matter of thoroughness in system design and development. The computer can do it all, and do it accurately and consistently.

Computer-Aided Medical Management
Computerized medical data management is always on and always alert. Rules-based conditions and data combinations in the broad scope of a claim trigger automatic alerts to the appropriate persons. The burden of watching every claim and remembering its historic facts, then combining the history with current conditions is impossible without computerized assistance. When claims adjusters and medical case managers receive alerts, they focus on claims that need the most attention, thereby optimizing efficiency and outcomes.

Standardized Processes
Traditional claims management and medical management processes rely on individual ingenuity, knowledge, and skill. As a result, processes are rarely standardized within an organization. Individual performance and outcomes are rarely measured. However, with computerized, real-time data monitoring the organization’s quality output is monitored, individual performance is audited, and results are reportable.

The Early Discovery Advantage
Importantly, computerized data monitoring insures early concurrent discovery of calamitous conditions in claims such as creeping claim severity, repeated opioid prescriptions, and comorbidities, to name a few. Uncovering such situations as they occur can save millions and prevent disastrous outcomes.

Analytics must be made actionable by leveraging the technology and pushing it into operations through computerized concurrent data monitoring. Data becomes a work-in-process tool, thereby achieving measureable efficiency and cost savings.