Worklist Prioritization: The Critical Element in Today’s CDI Operations

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• The past few years have been transformative for
  healthcare in the truest sense. From the rise of
  virtual care to big techs crafting innovative
  care delivery models, the healthcare industry has
  been witnessing some interesting shifts.
• Prodded by new programs and practices, the shift
  from fee-for-service reimbursement to value-based
  care has resulted in improved outcomes for
  patients, providers, and payers alike.

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• Theres no denying the opportunity to enhance
  patient outcomes while reducing cost is driving
  the industry forward and instigating healthcare
  providers to modernize legacy practices.
• The push for improved performance and financial
  health, however, is also putting clinical
  documentation improvement (CDI) programs under
  intense pressure to ensure accurate documentation
  rightly that justifies medical necessity.

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• The situation has influenced healthcare providers
  to redesign their CDI programs by implementing
  new, methodologies and technologies that can help
  them prioritize workflows and focus closely on
  cases that have greatest quality and financial
  impact.
• Worklist prioritization is a critical objective
  for healthcare providers, especially at a time
  when the importance of CDI is growing fast.

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Prioritization Why is it Challenging Healthcare
Providers?

• Accurately capturing and documenting the patient
  conditions require dedicated clinical
  documentation improvement specialists (CDIS) with
  coding and clinical knowledge.
• Additionally, hospitals need to implement a
  process that enables detailed evaluation of
  patient reports on a regular basis.

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• With new documentation and clinical evidence
  being added every hour, regular evaluation of the
  data is required to ensure that the medical
  records are thorough and accurate.
• In order to achieve this goal, the majority of
  CDI programs will have to increase the number of
  employees involved. Unfortunately, most
  healthcare providers lack the necessary resources
  or budget to train a large pool of staff.

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• To enhance the existing staffs capability in
  identifying records that require improvement, CDI
  programs often implement several methods to
  classify a specific set of records to evaluate
  every day.
• There are programs that even attempt to
  prioritize worklists on the basis of clinical
  experience.

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• In this case, specialists end up guesstimating
  the records that are likely to have documentation
  integrity concerns.
• Their decisions are often based on factors such
  as length of stay (LOS), chief complaint, list of
  providers with the past record of poor
  documentation habits, and so on.

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How can Technology Create a Difference?

• A solution that accurately identifies the
  discrepancies between clinical evidence and
  documentation would effectively address
  documentation prioritization issues. Doing so,
  however, requires additional focus on separate
  clinical data to identify probable conditions. 
• While rule-based methods have already been
  attempted to simplify the process of extracting
  the right data, they often suffer from the
  false-positive/negative dilemma. Thankfully,
  cognitive technologies such as artificial
  intelligence provide CDI specialists a solution
  to this problem.

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• For instance, machine learning algorithms can
  analyze real-time patient data to accurately
  predict potential medical conditions. By
  identifying gaps between documentation and
  clinical evidence ML can create a catalog of
  prioritized patient records for CDI staff to
  evaluate. With this approach, CDI specialists can
  address some of the most pressing documentation
  concerns.
• Halifax Regional Medical Centers (HRMC)
  initiative to improve its financials with an
  AI-based Clinical Documentation Improvement
  software serves as an excellent example in this
  regard. 

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• Another critical issue was the encoder which
  wasnt ICD-10-ready. As a result, the teams were
  experiencing issues with encoder speed and
  responsiveness, unnecessary downtime and
  connectivity issues between the EHR and
  the encoder.
• In order to resolve the issues, Halifax
  collaborated with ezDI to deploy our full suite
  of NLP-technologies including total coding
  workflow automation with ezDIs
  integrated computer-assisted coding application
  (CAC).

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• To learn more about ezDIs AI-based mid-revenue
  cycle management solutions visit www.ezDI.com and
  see the live product demo of our Clinical
  Documentation, Coding, Compliance/Auditing,
  Quality Measures, Encoder, and Enterprise
  Analytics request a live demo.

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Contact us Visit Our Website-
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Address- 12806 Townepark Way, Louisville,
Kentucky 40243