Data quality matters now more than ever and Clinical Documentation Improvement (CDI) is quickly becoming a top priority for leading Ontario hospitals. However, typical CDI initiatives are time and resource intensive. They can take years to properly implement leading to millions of dollars in lost funding in the interim.
Data Quality Assist (DQA) is a software tool that is installed on-site and immediately identifies the potential problems in coding and clinical documentation that directly affect HSFR funding. Correcting these cases such that the coding reflects the reality of the care provided helps ensure that patient acuity is accurately represented, leading to improved HBAM and QBP performance.
This approach ensures fair distribution of funding based on the patient services provided and enables better decision making, leading to improved quality of care.
Problem: A hospital has not accurately specified transfers to and from other inpatient facilities within their patient abstracts. This has had a significant impact on funding since inpatient transfers greatly alter HIG weight calculations for affected cases.
Background: Hospitals often audit cases by manually comparing abstracted data to various hospital information systems. In addition to consuming considerable resources, mistakes are often made.
Solution: DQA can integrate directly with external HIS systems (e.g. ADT, EDIS, surgical) to cross-reference key data fields against your coded data to ensure that important weight-influencing factors are not missed. In this case, data from the hospital ADT system was imported to detect transfers from other inpatient facilities.
Problem: Cases including mechanical ventilation have not been well-documented for a hospital, resulting in artificially low case weights.
Background: HBAM provides additional funding for cases with certain interventions (such as mechanical ventilation) as well other characteristics including transfers from other hospitals, ICU visits, home care referrals, and palliative deaths. Missing these factors can have significant impact on HIG weight, and therefore funding, for complex cases.
Solution: In this case, the appropriate clinical units and the Respiratory Therapy department were asked to provide a list of cases where ventilation occurred. This list was quickly imported into DQA where the incorrect cases were identified and subsequently corrected by Health Records.
Problem: A hospital has several inpatient cases where tracheostomies were not coded, either through incomplete documentation or incorrect coding. By not coding these procedures, the hospital is not receiving the appropriate case weight, negatively impacting their HBAM funding.
Background: Physicians often document interventions related to the care and use of tracheostomies without actually recording the procedure itself. Staff are often not aware that by missing certain interventions, they adversely affect funding.
Solution: DQA uses advanced proprietary machine learning algorithms to identify cases where tracheostomies are performed. Using this baseline data, a probability is assigned to cases where tracheostomies were likely done but not documented and/or coded. Using the resulting list of cases ordered by significance, the cases were reviewed and several data quality errors were corrected, leading to the correct HIG weight assignment.
Problem: A hospital had marked several inpatient visits as palliative care cases, however the HIG weights assigned to them were significantly lower than expected.
Background: Complex cases with many diagnoses and procedures may have some of the ICD/CCI codes truncated from the abstract record because of field restrictions in the CIHI DAD specifications.
Solution: DQA analyzes inpatient records directly from the abstract system and can detect when impactful diagnoses and procedures are being excluded from what is submitted to CIHI. DQA identified that the palliative diagnosis Z51.5 was not being submitted, which resulted in the cases being treated as non-palliative/typical. By adjusting the order of diagnoses on the cases, the palliative care was accurately captured and the full HIG weight was assigned.
Problem: The most responsible physician has documented the reason for admission as the most responsible diagnosis (MRDx), however a post-admit comorbidity (hospital acquired infection) accounted for the majority of the length of stay. The incorrect MRDx selection results in a significantly lower assigned HIG weight.
Background: The most responsible diagnosis is often the most important factor in determining the HIG weight for a case. The MRDx determines which Major Clinical Category (MCC) and Case Mix Group (CMG) a case falls under, which ultimately determines the HIG grouping. Correcting the HIG group for a case can have significant impact on the resulting HIG weight.
Solution: DQA examines every patient abstract and analyzes all interventions and diagnoses to determine where potential incorrect MRDx assignment may have occurred. DQA flagged this case for review by the physician, who determined that a correction was required. Furthermore, this case review helped the physician and coder better understand how the HIG weight methodology works and how hospital funding is impacted by certain documentation choices.
Hospitals that embark on dedicated HBAM and QBP initiatives often consume considerable time from Health Records, IT, and Decision Support resources, as well as busy clinicians, to search for data quality errors in coded patient abstracts. DQA includes a proprietary library of data quality checks that eliminates the need to manually search for cases to review. Hospital-specific data quality checks can also be added to reflect the unique nature of your hospital's clinical programs.
The software integrates directly with abstracting systems and other hospital information systems to automatically identify potential problem cases as they are coded. This reduces the error-prone yet common practice of a fiscal year-end push to search for records to correct as the submission deadline nears.