How do you ensure data quality and security when using Utilization Management databases?

Utilization Management (UM) is the process of evaluating and optimizing the use of health care resources and services. UM databases are essential tools for storing, analyzing, and reporting UM data, such as prior authorization requests, clinical criteria, outcomes, and costs. However, UM databases also pose challenges for data quality and security, as they may contain sensitive, incomplete, or inaccurate information. How do you ensure data quality and security when using UM databases? Here are some best practices to follow.

1 Define data standards

Before collecting and entering UM data, you need to define and document the data standards that will guide your UM database design and maintenance. Data standards are the rules and specifications that ensure the consistency, accuracy, and completeness of your data. For example, data standards may include data definitions, formats, sources, validation rules, and quality indicators. Data standards help you avoid data errors, duplication, and confusion, and enable you to compare and integrate data from different sources and systems.

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Maryanne Malter

Faculty @ University of Arizona |
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Most organizations develop standards and guidelines for UM based on key performance indicators based on industry standards and adherence to regulatory guidelines. Standards for educating RNs on clinical documentation, utilization review, and physician engagement are crucial. Data analysis involves an organization’s comparison from the Program for Evaluating Payment Patterns Electronic Report (PEPPER). PEPPER provides provider-specific Medicare data statistics for discharges/services vulnerable to improper payments. Specific organizations to improve RN education in case management and utilization review stems from certification from the American Case Management Association (ACMA) and Certified Case Manager (CCM).

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2 Implement data security measures

Data security is the protection of your UM data from unauthorized access, use, modification, or disclosure. Data security measures are the policies and procedures that safeguard your data from internal and external threats, such as hackers, malware, or human errors. For example, data security measures may include encryption, authentication, authorization, backup, and audit. Data security measures help you comply with regulatory and ethical requirements, such as HIPAA and HITECH, and prevent data breaches, losses, or damages.

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3 Perform data quality checks

Data quality checks are the processes of verifying and correcting your UM data to ensure that they meet your data standards and expectations. Data quality checks are performed at different stages of your UM database lifecycle, such as data collection, entry, update, and analysis. For example, data quality checks may include data cleansing, verification, reconciliation, and review. Data quality checks help you identify and resolve data issues, such as missing, invalid, or inconsistent data, and improve the reliability and usability of your data.

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Paul (Pablo) Arias EJD, MIS, BSN, RN, CMAC

Founder and CEO- Case Management, Denial Prevention, Revenue Cycle, Regulatory Compliance, Utilization Review, BPCI, Transitional Care
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Data needs to be verified and also have a source of truth so that is comes from one single repository that can be duplicated time over time with accurate results so that credibility in the data is not lost. Comparison to outside organizations for benchmarking of outcomes will inform you of how well you are performing as well as how well your data is working and if it is being accurately being captured. As mentioned below in the tools section if you use one of the national companies mentioned or any other UR tool they will have reports that you can compare your data against many others in their database and it will provide feedback on quality.

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4 Use data quality tools

Data quality tools are the software applications or features that help you automate and streamline your data quality checks. Data quality tools can perform various functions, such as data profiling, matching, deduplication, enrichment, and transformation. Data quality tools can help you save time and resources, reduce human errors, and enhance data quality outcomes. For example, you can use data quality tools to generate data quality reports, dashboards, or alerts that highlight your data quality status and issues.

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Maryanne Malter

Faculty @ University of Arizona |
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Within acute care settings, software applications such as Interqual and Milliman provide RN case managers and utilization review the tools to determine medical necessity, and the correct admission status such as inpatient or observation. Admission status is based upon the clinical documentation and discussions with providers the RN has to determine the plan of care, diagnosis, and discharge planning.

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5 Train data users

Data users are the people who access, use, or manage your UM data for various purposes, such as UM decision making, reporting, or research. Data users need to be trained on how to use your UM database and its data quality and security features effectively and responsibly. Data users need to understand the data standards, security measures, quality checks, and tools that apply to your UM database, and follow the best practices and guidelines for data entry, retrieval, analysis, and sharing. Data users also need to report any data quality or security problems or incidents to the appropriate authority.

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6 Update data regularly

Data update is the process of adding, modifying, or deleting your UM data to reflect the changes in your UM activities, policies, or environment. Data update is necessary to keep your UM database current, relevant, and accurate. Data update should be done periodically or as needed, depending on the frequency and nature of your UM data changes. Data update should also follow your data standards, security measures, quality checks, and tools to ensure that your UM database remains consistent and reliable.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you ensure data quality and security when using Utilization Management databases?

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1 Define data standards

2 Implement data security measures

3 Perform data quality checks

4 Use data quality tools

5 Train data users

6 Update data regularly

7 Here’s what else to consider

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