Data Analytics

Brault’s technical team works with your hospital IT department to pull from the data sources necessary for integration into Brault’s Practice Solutions portal (with little impact on other hospital IT projects). This process is typically achievable within 60-90 days. And, depending on your hospital’s IT capabilities, data can either be extracted in batch intervals or in real-time.

Common integrations include: hospital EDIS, Press Ganey (and other satisfaction surveys), physician scheduling systems, and data from the Emergency Department Benchmarking Alliance (for comparison to national performance indicators from more than 1,200 participating emergency departments).

Brault clients are provided with monthly reports detailing (by physician) the volume and types of documentation deficiencies that are costing your Practice’s lost income. Chart samples are provided for each physician to detail his or her own documentation habits, with applicable suggestions for improvement.

There are more than 25 discrete causes of lost revenue that can be related to deficiencies, which our PAT system can help track – with related RVU and charge losses related to each deficiency.

MIPS tracking reports also show monthly and year-to-date performance scores related to each quality measure that’s applicable to your Practice. Action plans for documentation improvement are then developed from these reports, with specific training interventions that can result in additional income and increased MIPS scoring.

The benefit of custom analytics are many but one practical example is compensation modeling, which allows practice leaders to easily analyze cost trends, total group compensation, individual salaries and related information. Combining this level of operations and billing data into a single repository can even help you determine how practice styles across individual providers may impact your Practice’s overall billing performance.

These custom analytics can also help nurture the relationship between your Practice and your hospital C-suite by better illuminating the patient experience in the ER, observation and daily-care settings. This analytics-based modeling can help identify patterns in patient arrival, treatment and discharge times, which could reveal under-staffing issues that affect patient satisfaction, and by extension, Net Promoter Score (NPS), or NP ratios.