Automated problem list generation and physicians perspective from a pilot study

Devarakonda, Murthy V.; Mehta, Neil; Tsou, Ching Huei; Liang, Jennifer J.; Nowacki, Amy S.; Jelovsek, J. Eric

doi:10.1016/j.ijmedinf.2017.05.015

Cited by 33 publications

(24 citation statements)

References 7 publications

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“… 12 , 17 , 18 From an interventional perspective, clinical decision support tools, automated alerts, and machine learning may play a role in improving problem list completeness across a wide range of diseases. 19–21 …”

Section: Introductionmentioning

confidence: 99%

Characterizing outpatient problem list completeness and duplications in the electronic health record

Wang

Wright

2020

Journal of the American Medical Informatics Association

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Abstract Objective The study sought to characterize rates of problem list completeness and duplications in common chronic diseases and to identify any relationships that they may have with respect to disease type, demographics, and disease severity. Materials and Methods We performed a retrospective analysis of electronic health record data from Partners HealthCare. We selected 8 common chronic diseases and identified patients with each of those diseases. We then analyzed each patient’s problem list for completeness and duplications and also collected information regarding demographics and disease severity. Rates of completeness and duplications were calculated for each disease and compared according to disease type, demographics, and disease severity. Results A total of 327 695 unique patients and 383 404 problem list entries were identified. Problem list completeness varied from 72.9% in hypertension to 93.5% in asthma, whereas problem list duplications varied from 4.8% in hypertension to 28.2% in diabetes. There was a variable relationship between demographic factors and rates of completeness and duplication. Rates of completeness were positively correlated with disease severity for most diseases. Rates of duplication were consistently positively correlated with disease severity. Conclusions Incompleteness and duplications are both important issues in problem lists. These issues vary widely across different diseases and can also be impacted by patient demographics and disease severity. Further studies are needed to investigate the effect of individual user behaviors and organizational policies on problem list utilization, which will aid the development of interventions that improve the utility of problem lists.

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Section: Introductionmentioning

confidence: 99%

Characterizing outpatient problem list completeness and duplications in the electronic health record

Wang

Wright

2020

Journal of the American Medical Informatics Association

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“…Such solutions include note templates, such as that provided by Epic SmartPhrases (Epic Systems Corporation, Verona, Wisconsin, Unites States), 30,31 strategic menu design, 32,33 and order sets. 34 Automationcentric innovations also exist, such as automated problem list generation 35 and follow-up determination after radiographic testing, 36 as well as clinical decision support, alerts for risk stratification 37 and drug-disease interactions. 38 A study by Arndt et al of ambulatory family practitioners in the United States showed that clinicians devote nearly 50% of their total daily time in their EHR performing clinical documentation and chart review, with chart review comprising nearly 75% of EHR tasks related to direct medical care.…”

Section: Discussionmentioning

confidence: 99%

Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center

et al. 2019

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Background Neurologists perform a significant amount of consultative work. Aggregative electronic health record (EHR) dashboards may help to reduce consultation turnaround time (TAT) which may reflect time spent interfacing with the EHR. Objectives This study was aimed to measure the difference in TAT before and after the implementation of a neurological dashboard. Methods We retrospectively studied a neurological dashboard in a read-only, web-based, clinical data review platform at an academic medical center that was separate from our institutional EHR. Using our EHR, we identified all distinct initial neurological consultations at our institution that were completed in the 5 months before, 5 months after, and 12 months after the dashboard go-live in December 2017. Using log data, we determined total dashboard users, unique page hits, patient-chart accesses, and user departments at 5 months after go-live. We calculated TAT as the difference in time between the placement of the consultation order and completion of the consultation note in the EHR. Results By April 30th in 2018, we identified 269 unique users, 684 dashboard page hits (median hits/user 1.0, interquartile range [IQR] = 1.0), and 510 unique patient-chart accesses. In 5 months before the go-live, 1,434 neurology consultations were completed with a median TAT of 2.0 hours (IQR = 2.5) which was significantly longer than during 5 months after the go-live, with 1,672 neurology consultations completed with a median TAT of 1.8 hours (IQR = 2.2; p = 0.001). Over the following 7 months, 2,160 consultations were completed and median TAT remained unchanged at 1.8 hours (IQR = 2.5). Conclusion At a large academic institution, we found a significant decrease in inpatient consult TAT 5 and 12 months after the implementation of a neurological dashboard. Further study is necessary to investigate the cognitive and operational effects of aggregative dashboards in neurology and to optimize their use.

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“…Several studies have found that the electronic problem list is often incomplete. 6 7 Some studies have explored making the problem list more complete by using natural language processing, 14 importing claims data, 15 or implementing rules based on structured data or decision support tools. 16 17 …”

Section: Discussionmentioning

confidence: 99%

Two algorithms for the reorganisation of the problem list by organ system

Hier

Pearson

2019

BMJ Health Care Inform

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ObjectiveLong problem lists can be challenging to use. Reorganisation of the problem list by organ system is a strategy for making long problem lists more manageable.MethodsIn a small-town primary care setting, we examined 4950 unique problem lists over 5 years (24 033 total problems and 2170 unique problems) from our electronic health record. All problems were mapped to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) and SNOMED CT codes. We developed two different algorithms for reorganising the problem list by organ system based on either the ICD-10-CM or the SNOMED CT code.ResultsThe mean problem list length was 4.9±4.6 problems. The two reorganisation algorithms allocated problems to one of 15 different categories (12 aligning with organ systems). 26.2% of problems were assigned to a more general category of ‘signs and symptoms’ that did not correspond to a single organ system. The two algorithms were concordant in allocation by organ system for 90% of the unique problems. Since ICD-10-CM is a monohierarchic classification system, problems coded by ICD-10-CM were assigned to a single category. Since SNOMED CT is a polyhierarchical ontology, 19.4% of problems coded by SNOMED CT were assigned to multiple categories.ConclusionReorganisation of the problem list by organ system is feasible using algorithms based on either ICD-10-CM or SNOMED CT codes, and the two algorithms are highly concordant.

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Automated problem list generation and physicians perspective from a pilot study

Abstract: Cognitive computing systems like this Watson system hold the potential for accurate, problem-list-centered summarization of patient records, potentially leading to increased efficiency, better clinical decision support, and improved quality of patient care.

Cited by 33 publications

References 7 publications

Characterizing outpatient problem list completeness and duplications in the electronic health record

Characterizing outpatient problem list completeness and duplications in the electronic health record

Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center

Two algorithms for the reorganisation of the problem list by organ system

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