scholarly journals 2. Keeping a Problem List by Alexander Perry

2021 ◽  
Vol 68 (05) ◽  
pp. 1
Author(s):  
Alexander Perry
Keyword(s):  
Problem List

Abstract 173: A Comparison of Administrative Claims Discharge Diagnosis with Electronic Medical Record Problem List in the Diagnosis of Acute Myocardial Infarction

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Michael D McCulloch ◽  
Tim Sobol ◽  
Joy Yuhas ◽  
Bill Ahern ◽  
Eric D Hixson ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Medical Record ◽  
Chart Review ◽  
Claims Data ◽  
Administrative Claims ◽  
Problem List ◽  
Principal Diagnosis ◽  
Administrative Claims Data ◽  
Patient Admissions

Background: Administrative claims data are commonly used for measurement of mortality and readmissions in Acute Myocardial Infarction (AMI). With advent of the Electronic Medical Record (EMR), the electronic problem list offers new ways to capture diagnosis data. However, no data comparing the accuracy of administrative claims data and the EMR problem list exists. Methods: Two years of admissions at a single, quaternary medical center were analyzed to compare the presence of AMI diagnosis in administrative claims and EMR problem list data using a 2x2 matrix. To gain insights into this novel method, 25 patient admissions were randomly selected from each group to undergo physician chart review to adjudicate a clinical diagnosis of myocardial infarction based on the universal definition. Results: A total of 105,929 admissions from January 1, 2010 to December 31, 2011 were included. Where EMR problem list and administrative claims data were in agreement for or against AMI diagnosis they were highly accurate. Where administrative claims data, but not EMR problem list, reported AMI the most common explanation was true AMI with missing EMR problem list diagnoses (60%). Less common reasons for discordance in this category include: (1) administrative coding error (20%), (2) computer algorithm error (8%), (3) patient death before EMR problem list created (4%), (4) EMR problem list not used (4%) and (5) AMI diagnosis was removed from EMR problem list (4%). Where EMR problem list, but not administrative claims data, reported AMI the most common explanation was no AMI with historical diagnosis of AMI from a previous admission (60%). Less common reasons for discordance in this category include: (1) AMI present but not the principal diagnosis (32%), (2) administrative coding error (4%) and (3) erroneous EMR problem list entry (4%). Conclusion: Compared to administrative and chart review diagnoses, we found that using the EMR problem list to identify patient admissions with a principal diagnosis of AMI will overlook a subset of patients primarily due to inadequate clinical documentation. Additionally, the EMR problem list does not discriminate the admission principal diagnosis from the secondary diagnoses.

Medical Record Documentation of Head Injury

1993 ◽  
Vol 14 (8) ◽  
pp. 300-301
Keyword(s):  
Blood Pressure ◽  
Head Injury ◽  
Chief Complaint ◽  
Problem List ◽  
Past Medical History ◽  
Retinal Hemorrhages ◽  
Parietal Area ◽  
Regular Rhythm ◽  
Optic Discs ◽  
Medical Record Documentation

Name: Roger Horton1 Date of Birth: June 1, 19842 Drug Allergies: None known3 Immunizations: Complete4 Wednesday, June 9, 1993 1 PM CHIEF COMPLAINT: Fell and hit side and head. PRESENT ILLNESS: At 12:15 PM was swinging high on swing at schoolyard. Fell off at top of arc and landed on hard ground, striking right side of body and right side of head. Remembers landing and "seeing stars," then being surrounded by others. Does not think he lost consciousness. Walked into school, with help. PAST MEDICAL HISTORY (as recorded on problem list): Occasional wheezing, relieved by albuterol inhaler. Fracture, left radius, age 6. PHYSICAL EXAMINATION: Slim, pale boy, sitting quietly on table. Complains of headache; feels "sick to my stomach" and cold. Right shoulder and hip hurt; neck does not. Weight: 58 lb Temperature: 98.2°F, orally Pulse rate: 92 beats/min Respiratory rate: 20 breaths/min Blood pressure: 120/70 Head: Tender and mildly swollen over right parietal area. Eyes: Sclerae clear. Pupils equal in size and reactive. Extraocular movements full without nystagmus. Media clear. Optic discs sharp. Venous pulsations noted. No retinal hemorrhages. Acuity 20/26 in each eye. Ears: Clear tympanic membranes without blood. Nose: Clear; no discharge. Mouth/throat: Clear; no injury noted. Neck: Full movement without pain. Mild tenderness on right side. Chest: Breathing easily. Clear sounds. No tenderness of ribs. Heart: Good sounds in regular rhythm without murmurs.

scholarly journals An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT

2015 ◽  
Vol 22 (3) ◽  
pp. 649-658 ◽  
Author(s):  
Kin Wah Fung ◽  
Julia Xu
Keyword(s):  
Electronic Health Records ◽  
Growth Pattern ◽  
Problem List ◽  
Snomed Ct ◽  
Health Records ◽  
The Core ◽  
Core Subset ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine ◽  
Core Problem

Abstract Objective Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated. Methods Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship. Results On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger. Conclusion The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.

The Problem with the “Problem List” Name

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
P. Millares Martin
Keyword(s):  
Problem List

Introduction to the Problem-List Phase

2008 ◽  
pp. 151-162
Author(s):  
Michael W. Otto ◽  
Noreen A. Reilly-Harrington ◽  
Jane N. Kogan ◽  
Aude Henin ◽  
Robert O. Knauz ◽  
...  
Keyword(s):  
Problem List

Diagnostic Reasoning in the Pain-Focused Encounter

2019 ◽  
pp. 103-116
Author(s):  
Beth B. Hogans
Keyword(s):  
Differential Diagnosis ◽  
Negative Impact ◽  
Diagnostic Testing ◽  
Clinical History ◽  
Diagnostic Reasoning ◽  
Problem List ◽  
Patient Centered ◽  
Clinical Model ◽  
Error Disclosure ◽  
The Impact

Chapter 7 addresses the processes and pitfalls of evaluating, reasoning about, and attending to the needs of patients with pain. This chapter builds on Chapter 6, which addressed clinical assessment, explaining in detail the process of extracting and abstracting information from the pain narrative (clinical history or interview) to lay the foundation for a problem list and differential diagnosis. The problem list and differential diagnosis are described and contrasted so that clinicians will be comfortable with both. A clinical model explains the need for patient-centered approaches to be omnipresent but balanced with an appropriate disease-centered knowledge base that is likewise informed by understanding the patient’s healthcare-related values and motivations. A balanced approach is emphasized. The process of planning for diagnostic testing, including imaging, laboratory testing, provocative maneuvers, and targeted referrals, is described. The last section of the chapter addresses the impact and nature of cognitive and affective biases that can mitigate the effectiveness of diagnostic reasoning. A coordinated strategy to limit the negative impact of diagnostic reasoning biases is presented in a memorable way. Finally, the ethics of errors and error disclosure are discussed as well as the process of error disclosure.

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