by Michael Smith
A successful compliance audit depends on much more than selecting the right EMR software or a company policy requiring timely document completion. Strategic use of data analytics can expose compliance issues stemming from errors by clinicians or even by the auditors themselves. It can pinpoint specific areas where education is needed to prevent future errors.
Considering recent innovations in modern EMR software, data analytics presents a new opportunity for all healthcare providers – acute, Home Health, Hospice, SNF and clinics of all types – to analyze results and deficiencies in clinical documentation in preparation for a compliance audit.
All healthcare facilities are subject to these audits completed as a mandate for state compliance and internal monitoring. The prevailing current process in health care facilities is to use paper or a spreadsheet to track results, as crude a method as that is, mostly to monitor nothing more than completion. There is much more information that can be revealed with data analytics, especially in the areas of clinical documentation, auditor accuracy, and identifying education needs.
For each state and type of healthcare facility, a specific number of audits may be needed to maintain licenses, compliance, or survey readiness. This number may vary by state and facility. If, in some areas, 30 or more audits need to be completed to support a compliance program, why do most facilities still complete these on paper without aggregating the data?
If as many as 10 audits per month are being completed by a single facility or provider, several different samples of clinician documentation must be reviewed. When data analytics are deployed, audits can drill down to the specific documentation section that falls below standard. In the right hands, data can be used to tag specific clinician scores on specific tasks, looking for patterns over a month, a quarter, or a year’s time. Data like this can translate to information for performance evaluations and, on a short-term scale, to identify immediate training needs.
With documentation having emerged as a central focus to find rationale for payment denials, ZPIC extrapolations, RAC audits, etc. and, conversely, to prepare appeals, data analytics is a life-saver. A tool such as this will eliminate the need to complete paper chart audits and to manually translate the data to information and use it to devise immediate mitigation actions.
Alternatively, submitting audits to data analytics provides immediate information that can be used for performance improvement and accountability. With regulations constantly changing each year, and with mandates from insurance providers also changing, it is imperative to have as much data around the analysis of documentation in order to continue process improvement.
If a healthcare organization deploys the practice of internal audits at the local, regional, or multi-site level, the issue of audit credibility surfaces. Auditors themselves must be highly trained, must be experts in, the regulations whose compliance violations they are commissioned to detect. With data analytics, the accuracy of their audit decisions can be tracked. Some current compliance programs have a senior or regional personnel do an audit check to close the loop in this process. The added benefit of having data analytics cross-reference documentation on a clinician or facility that has been audited is extremely valuable to creating a solid compliance program.
Example: Healthcare Facility on 100% Review
A healthcare organization with both home health and outpatient lines of business was put on a full CMS audit that continued for two years. This remedy is not uncommon following CMS's stepped up focused review efforts. Despite steps taken to progressively educate staff on their documentation downfalls, progress toward improving compliance was still slow and essentially ineffective. By the 18-month time mark, the organization implemented a data analysis-powered audit system. Several key areas needing improvement were identified.
The organization had hired several new clinicians during the first 18 months of focused review. Some of them were not full-time clinicians, who were not given the same training as full-time staff. However, these part time and per-diem staff were used so much that it was their documentation, for assessments and follow-up visits, that CMS reviewers saw most often. By limiting training to full-time clinicians while extensively deploying part-timers, the organization could not get training benefits, which were significant, visible to CMS reviewers. This became apparent once the data highlighted the individuals with the most serious training needs.
In addition, management realized that very rapid turn-around was needed from analysis to recommendations. If feedback was not readily available for clinical leadership to act on, remediation would have taken many more months, during which CMS review would have continued and additional staff turnover would continue to burden the corrective process. Instead, thanks to insights gained from this system, the opposite occurred. Month over month improvement led to the closure of the review six months after data analytics was begun. Analysis of charts and documentation also allowed for coding issues to be analyzed, uncovering high risk audit practices and making education needs easier to see.
In only 6 months using data analytics, applying findings in detail down to each individual clinician, key documentation error patterns were identified that were not otherwise readily apparent. It was this system that precipitated removal from CMS review.
Applying data analytics to a documentation improvement program aimed at compliance is the next step in home health technology. Such tools not only make an audit program efficient, effective, highly accountable, and useful for clinical documentation improvement but can also be applied to other types of remediation plans. The aforementioned example is but one success story among many that demonstrate how data aggregation and analysis can reveal areas of improvement that would have been invisible using spreadsheets or the naked eye.
For more information on this concept, contact Michael Smith. email@example.com (702)-343-3110
©2018 by Rowan Consulting Associates, Inc., Colorado Springs, CO. All rights reserved. This article originally appeared in Tim Rowan's Home Care Technology Report. homecaretechreport.com One copy may be printed for personal use; further reproduction by permission only. firstname.lastname@example.org