scholarly journals Tumor Registry Versus Physician Medical Record Review: A Direct Comparison of Patients With Pancreatic Neuroendocrine Tumors

2011 ◽  
Vol 7 (2) ◽  
pp. 111-116 ◽  
Author(s):  
Elisabet E. Manasanch ◽  
Jillian K. Smith ◽  
Andreea Bodnari ◽  
Jeannine McKinney ◽  
Catherine Gray ◽  
...  

Academic tumor registry analysis indicates many patients with pancreatic neuroendocrine tumors are not identified when compared with physician medical record review. Reasons include registry time lag and case-finding methodologies.

2019 ◽  
Vol 28 (2) ◽  
pp. 256-263 ◽  
Author(s):  
Nancy A. Brandenburg ◽  
Syd Phillips ◽  
Karen E. Wells ◽  
Kimberley J. Woodcroft ◽  
Kandace L. Amend ◽  
...  

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Catherine L. Satterwhite ◽  
Onchee Yu ◽  
Marsha A. Raebel ◽  
Stuart Berman ◽  
Penelope P. Howards ◽  
...  

ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15–44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15–25 years as predictors. Algorithm sensitivity (GH=96.4%;KPCO=90.3%) and PPV (GH=86.9%;KPCO=84.5%) were high, but specificity was poor (GH=45.9%;KPCO=37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification.


2019 ◽  
Vol 10 (4) ◽  
pp. 674-687 ◽  
Author(s):  
Muhammad Wasif Saif ◽  
Rohan Parikh ◽  
David Ray ◽  
James A. Kaye ◽  
Samantha K. Kurosky ◽  
...  

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 6600-6600
Author(s):  
E. E. Manasanch ◽  
T. P. McDade ◽  
A. Bodnari ◽  
J. McKinney ◽  
M. E. Sullivan ◽  
...  

6600 Background: Researchers have underutilized single-institution tumor registry (TR) data, instead using researcher medical record review and/or personal, departmental or institutional databases to identify patients with particular malignancies. However, TR data is becoming increasingly prominent on a national level through broader use of the National Cancer Database (NCDB). We selected mPNETs as example to compare the accuracy of TR identification of these tumors with physician medical record review (MD Review). Methods: For MD Review, the health information services department of a single academic medical center was queried for all patients with pancreatic ICD9 codes (157.0–157.9; 211.6–211.7) January 2000-August 2008. A single physician reviewer analyzed computerized and paper medical records and identified mPNET cases. For TR data, mPNET patients were identified using the TR database with assistance of TR staff by two separate strategies. From January 2000-December 2006, patients were identified by using diagnosis codes from manual review of admission, discharge, clinic and pathology reports. From January 2007-August 2008 the TR used an automated case finding program (CAL by C/NET, California, USA) that downloaded cases with terms and codes related to malignancy. All MD Review- and TR-identified mPNET cases were reviewed by a second investigator blinded to identification strategy to assure consistency of mPNET definitions. Results: Using MD Review, 1194 pts with pancreatic ICD9 codes were identified. After MD Review, 42 mPNET patients were identified and confirmed. In comparison, TR identified 17 patients, of whom 5 were not identified by MD Review. Of the 47 patients identified by either strategy, TR identified 17/47 (32.6%) patients, whereas MD Review identified 42/47 (89.4%). TR identification rate in time periods 1 and 2 were 30% and 40%, respectively. Conclusions: Analysis of an academic tumor registry demonstrates that a substantial proportion of mPNET cases are missed when compared to ICD-9 identification and physician medical record review. Since MD review is imperfect, the TR may be even less effective at identifying PNETs than our data suggest. This may be applicable to other tumor registries and TR-based national studies. No significant financial relationships to disclose.


2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i9-i12
Author(s):  
Anna Hansen ◽  
Dana Quesinberry ◽  
Peter Akpunonu ◽  
Julia Martin ◽  
Svetla Slavova

IntroductionThe purpose of this study was to estimate the positive predictive value (PPV) of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for injury, poisoning, physical or sexual assault complicating pregnancy, childbirth and the puerperium (PCP) to capture injury encounters within both hospital and emergency department claims data.MethodsA medical record review was conducted on a sample (n=157) of inpatient and emergency department claims from one Kentucky healthcare system from 2015 to 2017, with any diagnosis in the ICD-10-CM range O9A.2-O9A.4. Study clinicians reviewed medical records for the sampled cases and used an abstraction form to collect information on documented presence of injury and PCP complications. The study estimated the PPVs and the 95% CIs of O9A.2-O9A.4 codes for (1) capturing injuries and (2) capturing injuries complicating PCP.ResultsThe estimated PPV for the codes O9A.2-O9A.4 to identify injury in the full sample was 79.6% (95% CI 73.3% to 85.9%) and the PPV for capturing injuries complicating PCP was 72.0% (95% CI 65.0% to 79.0%). The estimated PPV for an inpatient principal diagnosis O9A.2-O9A.4 to capture injuries was 90.7% (95% CI 82.0% to 99.4%) and the PPV for capturing injuries complicating PCP was 88.4% (95% CI 78.4% to 98.4%). The estimated PPV for any mention of O9A.2-O9A.4 in emergency department data to capture injuries was 95.2% (95% CI 90.6% to 99.9%) and the PPV for capturing injuries complicating PCP was 81.0% (95% CI 72.4% to 89.5%).DiscussionThe O9A.2-O9A.4 codes captured high percentage true injury cases among pregnant and puerperal women.


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