Implementation of a Real-Time Documentation Assistance Tool: Automated Diagnosis (AutoDx)

Abstract

Background Clinical documentation improvement programs are utilized by most health care systems to enhance provider documentation. Suggestions are sent to providers in a variety of ways, and are commonly referred to as coding queries. Responding to these coding queries can require significant provider time and do not often align with workflows. To enhance provider documentation in a more consistent manner without creating undue burden, alternative strategies are required.

Objectives The aim of this study is to evaluate the impact of a real-time documentation assistance tool, named AutoDx, on the volume of coding queries and encounter-level outcome metrics, including case-mix index (CMI).

Methods The AutoDx tool was developed utilizing tools existing within the electronic health record, and is based on the generation of messages when clinical conditions are met. These messages appear within provider notes and required little to no interaction. Initial diagnoses included in the tool were electrolyte deficiencies, obesity, and malnutrition. The tool was piloted in a cohort of Hospital Medicine providers, then expanded to the Neuro Intensive Care Unit (NICU), with addition diagnoses being added.

Results The initial Hospital Medicine implementation evaluation included 590 encounters pre- and 531 post-implementation. The volume of coding queries decreased 57% (p < 0.0001) for the targeted diagnoses compared with 6% (p = 0.77) in other high-volume diagnoses. In the NICU cohort, 829 encounters pre-implementation were compared with 680 post. The proportion of AutoDx coding queries compared with all other coding queries decreased from 54.9 to 37.1% (p < 0.0001). During the same period, CMI demonstrated a significant increase post-implementation (4.00 vs. 4.55, p = 0.02).

Conclusion The real-time documentation assistance tool led to a significant decrease in coding queries for targeted diagnoses in two unique provider cohorts. This improvement was also associated with a significant increase in CMI during the implementation time period.

Keywords

clinical documentation - automated - AutoDx

Protection of Human and Animal Subjects

This project received a formal Determination of Quality Improvement status according to University of Chicago Medicine institutional policy. As such, this initiative was deemed not human subjects research and was therefore not reviewed by the Institutional Review Board.

Publication History

Received: 27 February 2024

Accepted: 02 May 2024

Accepted Manuscript online:
03 May 2024

Article published online:
26 June 2024

• References

• 1 Lorkowski J, Pokorski M. Medical records: a historical narrative. Biomedicines 2022; 10 (10) 2594
• 2 Division HIP. Individuals' Right under HIPAA to Access their Health Information 45 CFR § 164.524. HHS.gov. Published January 5, 2016. Accessed September 18, 2023 at: https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/access/index.html
• 3 Sanderson AL, Burns JP. Clinical documentation for intensivists: the impact of diagnosis documentation. Crit Care Med 2020; 48 (04) 579-587
• 4 Renée Brown L. The secret life of a clinical documentation improvement specialist. Nursing 2013; 43 (02) 10
• 5 3M 360 Encompass System | 3M Health Information Systems. Accessed September 18, 2023 at: https://www.3m.com/3M/en_US/health-information-systems-us/improve-revenue-cycle/360-encompass-system/
• 6 AHIMA. Guidelines for Achieving a Compliant Query Practice (2019 Update). Guidel Achiev Compliant Query Pract 2019 Update AHIMA Am Health Inf Manag Assoc. Published online February 5, 2019. Accessed October 20, 2023 at: http://bok.ahima.org/doc?oid=302673
• 7 Reyes C, Greenbaum A, Porto C, Russell JC. Implementation of a clinical documentation improvement curriculum improves quality metrics and hospital charges in an academic surgery department. J Am Coll Surg 2017; 224 (03) 301-309
• 8 Esper P, Walker S. Improving documentation of quality measures in the electronic health record. J Am Assoc Nurse Pract 2015; 27 (06) 308-312
• 9 Castaldi M, McNelis J. Introducing a clinical documentation specialist to improve coding and collectability on a surgical service. J Healthc Qual 2019; 41 (03) e21-e29
• 10 Patel A, Ali A, Lutfi F, Nwosu-Lheme A, Markham MJ. An interactive multimodality curriculum teaching medicine residents about oncologic documentation and billing. MedEdPORTAL 2018; 14: 10746
• 11 Paz KB, Halverstam C, Rzepecki AK, McLellan BN. A national survey of medical coding and billing training in United States Dermatology Residency Programs. J Drugs Dermatol 2018; 17 (06) 678-682
• 12 Kapa S, Beckman TJ, Cha SS. et al. A reliable billing method for internal medicine resident clinics: financial implications for an academic medical center. J Grad Med Educ 2010; 2 (02) 181-187
• 13 Hebal F, Nanney E, Stake C, Miller ML, Lales G, Barsness KA. Automated data extraction: merging clinical care with real-time cohort-specific research and quality improvement data. J Pediatr Surg 2017; 52 (01) 149-152
• 14 Furmaga J, Courtney DM, Lehmann CU. et al. Improving emergency department documentation with noninterruptive clinical decision support: An open-label, randomized clinical efficacy trial. Acad Emerg Med 2022; 29 (02) 228-230
• 15 Cerasale M, Nguyen K, Cui M, Cao K. Hospitalist builders: maximizing the physician potential to improve the EHR. In: Journal of Hospital Medicine. Accessed September 20, 2023 at: https://shmabstracts.org/abstract/hospitalist-builders-maximizing-the-physician-potential-to-improve-the-ehr/
• 16 D'Souza FR, Murray JP, Tummala S. et al. Implementation and assessment of a proning protocol for nonintubated patients with COVID-19. J Healthc Qual 2021; 43 (04) 195-203
• 17 Cerasale M, Kao CK. A summary of three years of optimizing documentation. Presented at: Epic Users' Group Meeting: Madison, WI; 2023
• 18 Parkash O, Jamil S, Chudzinski V. et al. Academy for Quality and Safety Improvement (AQSI) project to improve diagnosis and documentation of malnutrition in a community hospital. BMJ Open Qual 2023; 12 (04) e002415
• 19 Radmard S, Epstein SE, Roeder HJ. et al. Inpatient neurology consultations during the onset of the SARS-CoV-2 New York City pandemic: a single center case series. Front Neurol 2020; 11: 805
• 20 Chowdhury RN, Hasan AT, Ur Rahman Y, Khan SI, Hussain AR, Ahsan S. Pattern of neurological disease seen among patients admitted in tertiary care hospital. BMC Res Notes 2014; 7 (01) 202
• 21 Bacellar A, Pedreira BB, Costa G, Assis T. Frequency, associated features, and burden of neurological disorders in older adult inpatients in Brazil: a retrospective cross-sectional study. BMC Health Serv Res 2017; 17 (01) 504
• 22 Budd J. Burnout related to electronic health record use in primary care. J Prim Care Community Health 2023; 14: 21 501319231166921
• 23 Florig ST, Corby S, Rosson NT. et al. Chart completion time of attending physicians while using medical scribes. AMIA Annu Symp Proc 2022; 2021: 457-465