QA in Clinical Documentation in the Current Healthcare Scenario
With EHR and speech recognition systems, clinical documentation quality has become more important than ever, since the documents are often shared among multiple providers. Both patients and providers depend on these documents and so these have to be free of errors. However, the question is whether Quality Assurance (QA) of documents is adequate under the current developments in healthcare documentation? According to the president of the Association for Healthcare Documentation Integrity (AHDI), QA process is there within healthcare organizations that have in-house departments and the transcription service organizations that provide transcription services to the hospitals. The QA standards are inconsistent as each company / department develops their own standards to define quality documentation.
Establishing an Effective QA Program The Association for Healthcare Documentation Integrity (AHDI) and the American Health Information Management Association (AHIMA) have already released a QA resource kit to assist this process, which includes standards for measurement, reporting, and documentation improvement. These organizations have also released a report on QA best practices for healthcare documentation. However, knowing the best practices alone is not enough for an effective QA process. There is no validated data that can help you determine how much QA actually needs to be done. As per the President of the ADHI, the organization is trying for a benchmarking survey of all healthcare organizations in the United States in order to determine how many of them have their own transcription departments, how many facilities handle QA on outsourced documents and how much QA is being performed on physician documentation. The organization hopes to learn about the types of programs and professional skills required and how to update certification programs in order to reflect the needs of providers. In the meantime, healthcare organizations can adopt the following practices for an effective QA. 
The QA process must start with the medical transcriptionists (in-house or hired). Make sure that the newcomers in your organization are meeting the quality standards and streamlining the process so that the questions of medical transcriptionists (MTs) can be routed and answered in the most
effective manner. There should be open communication with MTs, so that it is possible to address any dictation-related error that may cause a quality concern, at the point of transcription. There should be a concurrent review process for MT’s work as well.
Organizations should standardize their entire process to make the most out of the QA process and produce the best possible quality outcomes consistently. This will help to quickly address recurrent mistakes (making the same type of mistake each time).
In the case of outsourced documentation, healthcare practitioners should ensure
that
they
provide
complete
information
regarding
the
quality
standards required to their transcription company. Normally, there will be strong QA programs within transcription companies that enable them to provide error-free transcripts. So it is recommended that providers give enough information to the outsourcing medical transcription company and perform their own QA on outsourced documents.
Challenges of QA in Front-end Dictation In the case of back-end speech recognition system (SR), physicians are only required to dictate into a recorder. The transcriptionists are responsible for transcribing the data to create a document, or correcting the errors in documents that are created through the speech recognition process. The QA process here is not that challenging and can be done with a separate group of healthcare documentation specialists more effectively. This is not the case with front-end speech recognition system where the transcription process is carried out in real time. Physicians have the responsibility of correcting the errors while their dictations are translated into documents before their eyes. Quality checking is challenging due to the following reasons:
With back-end dictation, the transcript reaches the physicians after a review. Since the medical editor has already reviewed the documents, physicians require less time to edit the document and continue their job. However, in front-end dictation, physicians will require more time to review the documents as they are translated in real time and no medical editor is doing the review.
Physicians who are overburdened mostly overlook the errors since they may not have enough time to balance documentation and patient care.
Of course, most of the front-end speech recognition systems are sophisticated and can distinguish among accents, specialties and other information pertaining
to the provider’s profile.
However,
even
with
the
perfect
technology, if there is a noisy environment, if the provider finds it difficult to speak properly (for example, if he/she has a cold), or the provider’s profile is not adapting to the system, then the recognition would be less accurate and the generated documents would require more review. Physicians will lose a lot of time due to this. An ideal speech recognition system would be one that supports both front end and back end workflow. When there is a shortage of transcription resources or hectic activity, facilities can switch from back end to front end SR. For example, in a healthcare facility short reports can be reviewed by authors at the front end, while lengthier and detailed reports can be sent to transcription for review and correction.
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