Problem lists are not new to healthcare, yet the industry has historically struggled to accurately capture this critical snapshot of patient problems and visit diagnoses.
While EHRs now provide an efficient way to gather problem list data, they don’t address the underlying challenge of clean data capture due to the wide variance in terminologies used across the industry. When compiled accurately and thoroughly, problem lists have the potential to immediately align multi-disciplinary treatment efforts as patients are triaged from unit to unit or provider to provider. It’s one of the primary reasons these lists were a focal point of Meaningful Use (MU) requirements to improve electronic exchange of critical patient data. Unfortunately, strategies to meet MU problem list requisites are often more about checking off a compliance box than ensuring information is concise and accurate. Thus, many IT and clinical executives face notable challenges in effectively governing this information today.
In truth, healthcare organizations must prioritize quality control of problem lists and visit diagnoses to avoid negative downstream impacts related to analytics, clinical decision support, and reimbursement. Clinicians need streamlined methods that speed selection of problem lists and visit diagnoses codes, while also improving accuracy. Otherwise, healthcare organizations run the risk of:
- Inability to accurately report quality measures with inaccurate or unspecified data
- Outdated or inaccurate diagnoses due to a lack of specificity
- Decrease in productivity with an increased number of coder queries
- Clinician EHR dissatisfaction
These impact your hierarchical condition categories (HCC) and risk adjustment factors (RAF).