Data analytics investments rely on strong information governance
Since the release of AHIMA’s information governance (IG) framework for implementing IG programs, the healthcare industry has recognized IG as a strategic asset for ensuring data integrity. As organizations advance their programs, measuring results is the next important step in the IG continuum.
What should organizations be seeing as they roll out their programs? How have other industries measured IG success? And what role does IG play in enterprise data analytics strategies? This article addresses each of these key healthcare IT questions.
Rooted in Finance
Information governance is not unique to healthcare. The banking industry began implementing IG programs a decade ago. According to a 2006 study, financial institutions aimed to ensure proper safeguards and measures to protect sensitive information, ensure transparency and accountability, achieve compliance, improve operational efficiencies and minimize risk. Over the years, information governance has become increasingly important in the banking industry. Financial executives have emphasized positive outcomes and the ongoing benefits of reduced costs, better data quality and improved performance.
While there are similarities in the goals and priorities across industries, IG in healthcare is more complex — involving quality of patient care, protection of health information, prevention of medical errors and population health. Stringent regulatory requirements and compliance standards such as HIPAA present unique challenges as organizations attempt to protect, share and leverage meaningful information.
We’re also beginning to hear more healthcare IT executives talking about data governance, establishing consistent data definitions, cleaning up data so that analytics projects can be successful. Strong analytics and business intelligence are difficult to achieve in healthcare due to disjointed, incorrect and unreliable data. This topic was addressed during several sessions at HFMA’s 2016 National Institute conference held June 26-29.
Strong Data Analytics Require IG
According to Pam Arlotto, president and CEO or Maestro Strategies and former president of the HIMSS, building a strong data governance process is a key factor for hospital leaders to consider before investing in a data analytics system. This includes specifying decision-making processes and unifying data definitions across the health system.
Data analytics and business intelligence investments also must have high-quality, accurate data to improve clinical outcomes, meet regulatory requirements, increase business efficiency, reduce costs, and more. However, Effective data governance and IG programs mitigate these issues through strategic efforts to ensure data integrity and accuracy.
Finally, the importance of information governance for ensuring data integrity is also directly tied to both clinical and financial performance. From a financial perspective, one of most pressing questions for healthcare executives is: Does IG save my organization?
Data Quality Impacts Cost Savings: How IG Can Help
At CIOX Health, we recently conducted a case study focused on the quality of documentation released from our clients’ covered entities and the impact on cost savings. The process involved extensive inquiry and data analysis in collaboration with key players — security, compliance, privacy, operations, HIM, IT and executive leadership. We asked the following questions:
- What are the core quality errors?
- Where are these errors occurring?
- What are the root causes?
- What measures are needed for improvement — training, staffing, technology?
- How do we ensure transparency of information?
Transparency was top priority — showing the hard numbers to convey the cost of poor quality and gain buy-in for our IG model. The costs cited were related to operational inefficiencies, management involvement with quality errors, potential loss of business, patient satisfaction, and staff turnover (recruitment, onboarding, IT security changeover). According to a national survey, the average total turnover rate for healthcare employers in 2015 was 19.2% — meaning substantial costs and increased privacy and security risks.
Our team decided to take an incremental approach, targeting a specific quality error occurring with a single client. A detailed onsite audit revealed several factors that were hindering the overall quality of staff performance. Process improvement resulted in a reduction in staff turnover along with an increase in employee confidence and performance, client satisfaction, and patient satisfaction. Timely delivery of accurate information also improved population health efforts—reducing duplication of patient services, and providing prompt diagnoses and treatment plans.
From both clinical and financial perspectives, the cost savings and other benefits supported the case for IG. Addressing one significant quality error had a ripple effect across our organization. The next step is to advance enterprise IG, using data analysis within a multidisciplinary approach to evaluate progress in more complex areas on a global level.
Measuring Success and Moving Forward
Though case studies in the healthcare industry that quantify savings and verify additional outcomes are not readily available, they are slowly emerging. AHIMA is conducting pilot tests in various settings to create case studies, identify success factors, and validate the IG model. In addition, their current resources include a set of tools and services designed to evaluate IG progress and move your program forward.
For healthcare organizations that have implemented IG programs, how do you know you’re on the right path? Here are questions to consider as you measure success:
- What improvements are you seeing in specific areas? For example: operational efficiencies; cost savings — what does IG save you; reduced patient complaints; reduced unauthorized disclosures’ improved population health — specific improvements; fewer breaches — improved data protection; disaster recovery time; data analysis; fewer admission errors; and fewer medical errors.
- Do you have stats that reflect improvement?
- Are you analyzing breach data to improve ROI?
- Are your data elements consistent across systems?
- What process improvements are in place?
- What are the challenges as you move forward?
- Have you conducted case studies to support your efforts?
HIM professionals and executive leaders must work together to ensure understanding of the clinical, financial, regulatory, and technology aspects of healthcare. Enterprise information governance and management are necessary to meet current and evolving requirements of an increasingly complex and constantly changing healthcare environment. Data integrity is more important than ever.
Seven Data Quality Issues to Know
A recent report on the importance of establishing a clean database for analytics and integration efforts identified seven specific data quality issues across industries including the following.
- Hard-to-detect duplicate records. About 10% of names and addresses in an average database are duplicates. Identifying duplicates and merging/purging them are critical components to improving data accuracy.
- Inability to consolidate records from a group of duplicates. If you have duplicate records in varying degrees of accuracy, how can you determine which record is the most relevant? How do you collapse the information into one unified, accurate record?
- Data entry errors in address elements. Data entry errors, such as misspelled or missing contact information, present one of the main causes of poor data quality. According to The Data Warehousing Institute (TDWI), 26% of data quality problems are due to data entry errors.
- Not identifying data quality issues at the start. Know exactly what your system’s data quality issues are up front—before any data-driven initiatives are implemented. Detecting and fixing data problems before merging data saves time and money.
- Fragmented, inconsistent data. Customer data comes from multiple sources, through acquisitions or mergers, legacy systems, data migrations, and data entry. This can corrupt the integrity of your database and lead to additional costs, program inefficiencies, and erroneous views of information.
- Failed integration due to legacy data issues. Merging legacy systems of consolidating organizations or applications can result in poor quality—disorganized data stored in varying formats. Data must be complete, accurate, and properly formatted to ensure successful integration.
- Inability to verify identity. Verifying patient identity is critical to preventing fraud and breach of protected information. But connecting contact elements to gain meaningful insight can be a challenge. Make sure the mailing address, phone number, and email address for each patient are valid.