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ERGONOMIC WORKSTATIONS


easily reviewed summary, even though much of that data was originally re- corded solely in the free-text dictations. Each problem, medication and allergy is tied to each underlying document in which it was found, so that the caregiver can quickly and easily verify the information and view it in clinical context. With end-to-end transcription, they


are building dashboards with graphs, lists and drill-down capabilities that drive strategic planning and continuous improvement eff orts. T ese eff orts are powered by the free-text transcription data. Dashboards show the clinical quality of care, clinician adherence to care guidelines and performance against meaningful-use metrics (such as how often patients with chronic obstructive pulmonary disease had their lung function evaluated with spirometry, which patients were missed and which doctors were most and least likely to document adherence to the guidelines). Dashboards also support population health and ACO activi- ties, such as identifying patients with chronic diseases and stratifying them by severity in order to assess risk and target interventions. Dashboards show the quality of documentation to support appropriate coding and billing (particularly in an ICD-10 era), such as identifying which clinicians most often fail to document the stage of a patient’s chronic kidney disease or the specifi c type of conges- tive heart failure. T ose dashboards also provide a list of visits for which excisional debridement was almost certainly performed, but the documen- tation only supports coding for simple debridement. Each such under-coded visit can represent thousands of dollars in lost revenue. Even over-coding can often be identifi ed, helping to reduce the risk of accusations of fraud in an RAC audit. Perhaps most importantly, dash- boards are surfacing that give real-


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time feedback to the clinician during dictation to drive improvements at the point of care. T is non-interruptive, automated feedback helps the clinician record the visit with the right details needed to support billing, coding, regulatory reporting and research. T is feedback also helps the clinician fol- low commonly accepted standards of care that improve quality and patient outcomes, such as identifying when a radiologist has documented a critical fi nding (e.g., a collapsed lung), but has not documented that the treating physician was notifi ed of the problem. Real-time feedback during dictation helps clinicians generate the best deci- sions and documentation at the point of care. T is enables downstream activi- ties to proceed with greater effi ciency and eff ectiveness. T e goal is to achieve increased revenue, reduced costs and, most importantly, better patient out- comes – all with less eff ort on the part of already overworked clinicians. To achieve this ideal end-to-end transcription state requires the right supporting architecture. T at includes capture and retention of all the relevant data into a fast and fl exible data store (preferably cloud-hosted to reduce costs and enhance manageability); real-time translation from free-text transcription directly into meaningful use-mandated codes that computers can understand, such as SNOMED-CT, RxNorm, and LOINC; additional metadata tagging to describe the context in which those codes were found, such as the certainty (e.g., positive, negative, maybe), tim- ing (past, present, future) and subject (patient or family member); and an infrastructure to meaningfully and fl exibly query the data and display it in dashboards, worklists and at the point of care. It’s been a long time in the mak- ing, but end-to-end transcription is fi nally here, and it’s starting to change everything we thought we knew about creating clinical documentation. HMT


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