Effective data-driven clinical workflows require HIT customization
In my last article, I provided an overview of how healthcare organizations could optimize their HIT systems as to deliver a more accurate and reliable understanding of their clinical and financial performance. In this article, we will dive deeper in order to describe the specific tools and system changes that organizations will need to make to achieve those benefits.
To recap, the last article emphasized how systems integration and improved data capture could help provider organizations risk stratify and engage their patients and understand the cost of care. Integrating information systems and increased data aggregation, however, may present a new set of challenges, particularly in interpreting the data and making it actionable for providers at the point of care.
This is where HIT customization is a crucial aspect of optimizations. Although many IT vendors may describe their solutions as ready “out of the box,” this is often not the case and the built-in analytic tools cannot deliver the insight providers need, when they need it. Effective and efficient data-driven workflows require some additional resources spent, mostly time, to maximize an EHR investment.
Our firm recently witnessed firsthand how crucial it is to have the optimal tools for data analysis and management when we were helping provider organizations transition from the ICD-9-CM to ICD-10-CM coding set. Several of the organizations we assisted were using a crosswalk application in their EHR system that was, in some cases inaccurately translating the ICD-9-CM into ICD-10-CM codes, but the organization was largely unaware of these errors. We needed to deconstruct this crosswalk application’s database to see why it was, at one organization, correct only 60 percent of the time. The ICD-10-CM search function in several EHR systems was also noted to be difficult and time consuming for the providers to find the most specific ICD-10-CM diagnosis code. This often resulted in the capture of the unspecific ICD-10-CM codes creating inaccurate data. Inaccurate reporting of the ICD-10-CM diagnosis codes may not effectively communicate the type or severity of illness and ultimately affect patient care and reimbursement.
In some cases the cause for coding or analytic errors is because of poor data integrity at the organization. In the last article, I described the importance of capturing data and standardizing them across the organization to ensure strong integrity. To enable enterprise-wide capture and standardization, organizations need to implement an enterprise data warehouse (EDW) or ensure their existing EDW is effectively cleansing data collected from around the organization so accurate claims are submitted and safe care is delivered. In health systems where affiliated providers may be working in clinics in surrounding communities, we have seen differing coding and documentation standards at the disparate outpatient locations and inpatient facilities, which can lead to overbilling or under-billing, flawed enterprise-wide analysis, but, most importantly, patient safety risks.
For example, a survey of hospital CIOs and senior leadership found that one-fifth indicated that their organization had experienced an adverse event during the course of the year due to a patient information mismatch. Ensuring data integrity with a reliable and proven EDW is essential for systems integration.
Streamline EHR templates
Thanks to the Physician Quality Reporting System (PQRS), accountable care organizations and other pay-for-performance care-quality programs, many provider organizations are required to chart quality metrics in the patient’s medical record in addition to information about their conditions and treatments. This trend has led to a single encounter that should only have been one page, become three or four pages of highly repetitive text.
Some of this information is not physician written, either. EHRs are able to automatically generate text, or copy and paste text from a previous note, based on prompts from the provider. In 2014, this practice of “cloning” notes came under scrutiny by the Office of the Inspector General of the U.S. Department of Health and Human Services, which reported the activity may “mask true authorship of the medical record and distort information in the record to inflate health care claims.”
That is why after implementing or configuring an EDW to reliably standardize data, healthcare organizations should customize EHR templates so the quality data necessary for reporting and analysis is easily distinguishable from the other clinical information. The major EHR systems used in hospitals and practices, and many others, can be configured in this fashion.
Customizing EHR templates is just one aspect of being able to deliver data to providers when they need it at the point of care. Additional analytic technology needs to be implemented or configured so providers at or near the point of care can easily generate quality reports about a patient or a designated population. While the major EHR systems have built-in reporting tools, they often are not useful at the point of care because the results are difficult to quickly interpret, and therefore, not actionable.
Once these reporting tools are implemented and/or customized, providers will then need to be trained on proper usage. If the reporting module’s design is intuitive and providers are already knowledgeable about their EHR’s operation, then training time should be minimal.
As stated in my first article, the data-analytic technology for improved clinical and financial performance at an organization likely already exists in some form at your organization. Since EHR systems are often so complicated, provider organizations have not had the time or expertise to explore and optimize these tools. However, the time spent configuring IT for providers and revenue-cycle leaders will be well worth it as the entire industry transitions from payment based on volume of services to payment based on value.