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Automated Inference of Patient Problems from Medications using NDF-RT and
the SNOMED-CT CORE Problem List Subset
Jacob A. McCoy,             MS 1,     Allison B. McCoy,            PhD2,3,      Adam Wright,             PhD 4,     Dean F. Sittig,        PhD2,3
1 The University of Texas Medical School at Houston, 2 School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth),
3 UT Houston-Memorial Hermann Center for Healthcare Quality and Safety, Houston, TX, 4 Brigham and Women’s Hospital, Harvard Medical School, Boston, MA




 Objective                                                       Inference Knowledge Base                                                                                 Relevancy Rating for Inferred Problems
 To apply clinical indication relationships from NDF-RT and      For each medication, we extracted indicated problems
 usage frequency from the SNOMED-CT CORE problem                 from NDF-RT, mapped to SNOMED-CT and RxNorm,                                                  All
                                                                                                                                                                           1st       2nd       3rd        4th       5th       6th       7th       8th       9th       10th
 list subset to infer patient problems as an automated           limiting problems to those in the CORE Subset.                                              Ratings
 method for summarizing large, complex patient records.
                                                                                                                               All Potential Problems              847      191       166        136       117        81        57        42        32        18         7
                                                                 Evaluation of Inferred Problems
 Background                                                      We randomly selected 50 patients with an ambulatory visit     Median Rating                          4          2         4         4          4         5         5         5         5         5      4
 NDF-RT provides a formal content model to describe              during July 1, 2010 through December 31, 2010 and at
 medications and definitional relationships, such as drug        least one active medication. For each medication, we           Relevant                        26.6% 58.1% 25.3% 26.5% 15.4% 13.6%                           5.3%       0%       6.3%       0% 28.6%
 indication. RxNorm provides normalized drug names and           reviewed the patient’s chart to determine if any of the ten
 links to drug vocabularies. The CORE Problem List Subset        most frequent CORE problems inferred existed in the
 of SNOMED-CT includes the most commonly used terms              patient’s active problem list or if the term was an exact      Neither                         10.4%      7.3% 13.3% 11.0% 15.4%                   8.6% 10.5%          9.5%      3.1%      5.6%        0%
 and usage frequency from seven institutions                     match.
                                                                                                                                Not Relevant                    62.9% 34.6% 36.7% 15.4% 48.7% 77.8% 84.2% 90.5% 90.6% 94.4% 62.9%

                                                Inference Knowledge Base
                                                                                                                                        We rated each inferred problem on its relevance to the medication’s actual indication using a 5-point Likert scale .
                                                                                                                                      (1=Definitely Relevant, 2=Slightly Relevant, 3=Neither Relevant nor Irrelevant, 4=Slightly Irrelevant, 5=Not Relevant)
   Medication List
       Entry
                                                                                                                                 Results                                                                  Conclusion
                                                                                                                                 We evaluated 191 medications with 847 inferred problems                  Utilization of NDF-RT medication indications with CORE
                                                                                                                                 (4.4 per medication). Of these, 118 (61.8%) inferred                     problem frequencies performed reasonable well and may
                                   NDF-RT            may_treat           NDF-RT                   SNOMED-CT                      problem lists contained an entry also in the patient’s active            facilitate problem-oriented patient record summarization,.
         RxCUI
                                 Preparation                             Disease                   Concept                       problem list, and 62 (32.5%) contained exact matches.                    Some improvements are necessary for optimal problem-
                                                                                                                                 Of the 73 medications without an inferred problem in the                 medication matching.
                                                                                                                                 patient’s active problem list, 45 (62%) had an inferred
                                                                                                                                 problem rated as definitely or slightly relevant based on
                                                                                        SNOMED-CT CORE Problem
                                                                                                                                 the chart review, indicating that the problem should have
                                                                                          List Subset Frequency
                                                                                                                                 been entered on the active problem list.                                This project was supported in part by Grant No. 10510592 for
    Example:                                                                                                                     The first inferred problem was more often definitely or                 Patient-Centered Cognitive Support under the Strategic
    The top five problems and usage frequencies inferred from aspirin                                                            slightly relevant than lower rated problems. CORE                       Health IT Advanced Research Projects Program (SHARP)
    include degenerative arthritis (50.7%), gout (25.4%), febrile                                                                frequency more often corresponded with relevance to a                   from the Office of the National Coordinator for Health
                                                                                               Inferred Problem
    (16.8%), atrophic arthritis (14.4%), and rheumatic fever (1.0%).                                                             patient’s clinical scenarios for medications with fewer                 Information Technology and NCRR Grant 3UL1RR024148.
                                                                                                   List Entry
                                                                                                                                 potential indications, such as anti-inflammatory drugs.

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Automated Inference of Patient Problems from Medications using NDF-RT and the SNOMED-CT CORE Problem List Subset

  • 1. Automated Inference of Patient Problems from Medications using NDF-RT and the SNOMED-CT CORE Problem List Subset Jacob A. McCoy, MS 1, Allison B. McCoy, PhD2,3, Adam Wright, PhD 4, Dean F. Sittig, PhD2,3 1 The University of Texas Medical School at Houston, 2 School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), 3 UT Houston-Memorial Hermann Center for Healthcare Quality and Safety, Houston, TX, 4 Brigham and Women’s Hospital, Harvard Medical School, Boston, MA Objective Inference Knowledge Base Relevancy Rating for Inferred Problems To apply clinical indication relationships from NDF-RT and For each medication, we extracted indicated problems usage frequency from the SNOMED-CT CORE problem from NDF-RT, mapped to SNOMED-CT and RxNorm, All 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th list subset to infer patient problems as an automated limiting problems to those in the CORE Subset. Ratings method for summarizing large, complex patient records. All Potential Problems 847 191 166 136 117 81 57 42 32 18 7 Evaluation of Inferred Problems Background We randomly selected 50 patients with an ambulatory visit Median Rating 4 2 4 4 4 5 5 5 5 5 4 NDF-RT provides a formal content model to describe during July 1, 2010 through December 31, 2010 and at medications and definitional relationships, such as drug least one active medication. For each medication, we Relevant 26.6% 58.1% 25.3% 26.5% 15.4% 13.6% 5.3% 0% 6.3% 0% 28.6% indication. RxNorm provides normalized drug names and reviewed the patient’s chart to determine if any of the ten links to drug vocabularies. The CORE Problem List Subset most frequent CORE problems inferred existed in the of SNOMED-CT includes the most commonly used terms patient’s active problem list or if the term was an exact Neither 10.4% 7.3% 13.3% 11.0% 15.4% 8.6% 10.5% 9.5% 3.1% 5.6% 0% and usage frequency from seven institutions match. Not Relevant 62.9% 34.6% 36.7% 15.4% 48.7% 77.8% 84.2% 90.5% 90.6% 94.4% 62.9% Inference Knowledge Base We rated each inferred problem on its relevance to the medication’s actual indication using a 5-point Likert scale . (1=Definitely Relevant, 2=Slightly Relevant, 3=Neither Relevant nor Irrelevant, 4=Slightly Irrelevant, 5=Not Relevant) Medication List Entry Results Conclusion We evaluated 191 medications with 847 inferred problems Utilization of NDF-RT medication indications with CORE (4.4 per medication). Of these, 118 (61.8%) inferred problem frequencies performed reasonable well and may NDF-RT may_treat NDF-RT SNOMED-CT problem lists contained an entry also in the patient’s active facilitate problem-oriented patient record summarization,. RxCUI Preparation Disease Concept problem list, and 62 (32.5%) contained exact matches. Some improvements are necessary for optimal problem- Of the 73 medications without an inferred problem in the medication matching. patient’s active problem list, 45 (62%) had an inferred problem rated as definitely or slightly relevant based on SNOMED-CT CORE Problem the chart review, indicating that the problem should have List Subset Frequency been entered on the active problem list. This project was supported in part by Grant No. 10510592 for Example: The first inferred problem was more often definitely or Patient-Centered Cognitive Support under the Strategic The top five problems and usage frequencies inferred from aspirin slightly relevant than lower rated problems. CORE Health IT Advanced Research Projects Program (SHARP) include degenerative arthritis (50.7%), gout (25.4%), febrile frequency more often corresponded with relevance to a from the Office of the National Coordinator for Health Inferred Problem (16.8%), atrophic arthritis (14.4%), and rheumatic fever (1.0%). patient’s clinical scenarios for medications with fewer Information Technology and NCRR Grant 3UL1RR024148. List Entry potential indications, such as anti-inflammatory drugs.