Automatisierte Berechnung und Visualisierung von Komorbiditätsindizes für den Tumorboard-Entscheid

2020 ◽  
Vol 41 (08) ◽  
pp. 536-541
Author(s):  
Theresa Wald ◽  
Klemens Birnbaum ◽  
Susanne Wiegand ◽  
Andreas Dietz ◽  
Veit Zebralla ◽  
...  

Zusammenfassung Einleitung Komorbidität beeinflusst die für die kurative Therapie von Kopf-Hals-Karzinomen (HNC) verfügbaren Optionen. Das manuelle Zusammentragen der Nebenerkrankungen vor der Anmeldung im interdisziplinären Tumorboard (TB) ist zeitintensiv und oft unvollständig. Eine automatisierte Erfassung von nach ICD-10 kodierten Komorbiditätsdaten und deren Darstellung könnte die therapeutische Entscheidungsfindung im TB verbessern sowie bestehenden Informationsbedarf aufzeigen. Material und Methoden Die ICD-10-Codes unserer Patienten wurden aus 4 Datenbanken (hospital-information-system (HIS*-MED), der klinikinternen Tumordatenbank, OncoFlow® und OncoFunction®) extrahiert. Nach der Datensatzverknüpfung mittels der Python-Programmbibliotheken Pandas und Record Linkage wurden die ICD-10-Codes bezüglich des Charlson-Scores gewichtet und für die Implementierung in OncoFlow visualisiert. Die Kodierqualität wurde am Beispiel Diabetes an einer 1:1 gematchten Stichprobe von 240 Patienten überprüft. Ergebnisse 29 073 ICD-10-Codes von 2087 Patienten mit HNC wurden extrahiert. Die Anmeldung eines Patienten im TB triggert die sofortige automatische Erfassung und Visualisierung der Daten als Piktogramm in OncoFlow. Dies ermöglicht die schnelle Erfassung und Bewertung der Komorbidität sowie erforderlicher Diagnostik zur Komplettierung der Daten. Die klinikinterne Validationsstudie ergab eine Präzision der durch Datenimport verfügbaren Information zu Diabetes von 95,0 %. Diskussion Patienten mit HNC weisen häufig für die Therapieentscheidung relevante Nebenerkrankungen auf. Die automatisierte Erfassung der Komorbidität aus administrativen Daten und deren intuitive Darstellung ist ressourcen- und kostengünstig möglich. Voraussetzung ist eine präzise, vollständige Verschlüsselung der Krankheitsdiagnosen.

2019 ◽  
Vol 99 (01) ◽  
pp. 31-36
Author(s):  
Theresa Wald ◽  
Klemens Birnbaum ◽  
Susanne Wiegand ◽  
Andreas Dietz ◽  
Veit Zebralla ◽  
...  

Zusammenfassung Einleitung Komorbidität beeinflusst die für die kurative Therapie von Kopf-Hals-Karzinomen (HNC) verfügbaren Optionen. Das manuelle Zusammentragen der Nebenerkrankungen vor der Anmeldung im interdisziplinären Tumorboard (TB) ist zeitintensiv und oft unvollständig. Eine automatisierte Erfassung von nach ICD-10 kodierten Komorbiditätsdaten und deren Darstellung könnte die therapeutische Entscheidungsfindung im TB verbessern sowie bestehenden Informationsbedarf aufzeigen. Material und Methoden Die ICD-10-Codes unserer Patienten wurden aus 4 Datenbanken (hospital-information-system (HIS*-MED), der klinikinternen Tumordatenbank, OncoFlow® und OncoFunction®) extrahiert. Nach der Datensatzverknüpfung mittels der Python-Programmbibliotheken Pandas und Record Linkage wurden die ICD-10-Codes bezüglich des Charlson-Scores gewichtet und für die Implementierung in OncoFlow visualisiert. Die Kodierqualität wurde am Beispiel Diabetes an einer 1:1 gematchten Stichprobe von 240 Patienten überprüft. Ergebnisse 29 073 ICD-10-Codes von 2087 Patienten mit HNC wurden extrahiert. Die Anmeldung eines Patienten im TB triggert die sofortige automatische Erfassung und Visualisierung der Daten als Piktogramm in OncoFlow. Dies ermöglicht die schnelle Erfassung und Bewertung der Komorbidität sowie erforderlicher Diagnostik zur Komplettierung der Daten. Die klinikinterne Validationsstudie ergab eine Präzision der durch Datenimport verfügbaren Information zu Diabetes von 95,0 %. Diskussion Patienten mit HNC weisen häufig für die Therapieentscheidung relevante Nebenerkrankungen auf. Die automatisierte Erfassung der Komorbidität aus administrativen Daten und deren intuitive Darstellung ist ressourcen- und kostengünstig möglich. Voraussetzung ist eine präzise, vollständige Verschlüsselung der Krankheitsdiagnosen.


2014 ◽  
Vol 30 (10) ◽  
pp. 2039-2048 ◽  
Author(s):  
Rita de Cassia Braga Gonçalves ◽  
Sergio Miranda Freire

This study aimed to evaluate the use of hidden Markov models (HMM) for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient’s and mother’s names in the databases of the Mortality Information System (SIM), Information Subsystem for High Complexity Procedures (APAC), and Hospital Information System (AIH). A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.


2021 ◽  
Vol 24 ◽  
Author(s):  
Fernando Timoteo Fernandes ◽  
Diego Rodrigues Mendonça e Silva ◽  
Felipe Campos ◽  
Vilma Sousa Santana ◽  
Lucas Cuani ◽  
...  

ABSTRACT: Objective: To develop a linkage algorithm to match anonymous death records of cancer of the larynx (ICD-10 C32X), retrieved from the Mortality Information System (SIM) and the Hospital Information System of the Brazilian Unified National Health System (SIH-SUS) in Brazil. Methodology: Death records containing ICD-10 C32X codes were retrieved from SIM and SIH-SUS, limited to individuals aged 30 years and over, between 2002 and 2012, in the state of São Paulo. The databases were linked using a unique key identifier developed with sociodemographic data shared by both systems. Linkage performance was ascertained by applying the same procedure to similar non-anonymous databases. True pairs were those having the same identification variables. Results: A total of 14,311 eligible death records were found. Most records, 10,674 (74.6%), were exclusive to SIM. Only 1,853 (12.9%) deaths were registered in both systems, representing true pairs. A total of 1,784 (12.5%) cases of laryngeal cancer in the SIH-SUS database were tracked in SIM with different causes of death. The linkage failed to match 167 (9.4%) records due to inconsistencies in the key identifier. Conclusion: The authors found that linking anonymous data from mortality and hospital records is a feasible measure to track missing records and may improve cancer statistics.


Author(s):  
Polyana Mandacaru ◽  
Otaliba Libânio Morais Neto ◽  
Ana Lucia Andradde ◽  
Marli Souza ◽  
Fernanda Aguiar

IntroductionRoad traffic crashes (RTCs) represent an public health problem, accounting for 1.2 million deaths per year. However, these figures may be even greater if health databases are linked to police records. Objectives and ApproachMeasure the numbers of deaths and seriously injured victims and to estimate the percentage of corrections of the underlying cause of death, cause of hospitalization, and injury severity of the victims in five representative state capitals of the five macro-regions of Brazil. This was cross-sectional, study using the Hospital Information System, Mortality Information System and Police Road Traffic database. The RecLink III was used for the record linkage by identifying true pairs to calculate the correction percentage of the underlying cause of death, cause of hospitalization, and injury severity of the victims in the traffic records. ResultsChange in the diagnosis of hospitalization in the Hospital Information System with percentage of correction of the cause at 24.4% for Belo Horizonte, 96.9% for Campo Grande, 100.0% for Palmas, and 33.2% for Teresina. The correction of the underlying cause of death in the Mortality Information System were 29.9%, 11.9%, 4.2%, and 33.5% for Belo Horizonte, Campo Grande, Curitiba, and Teresina, respectively. Change in the classification of injury severity was observed with an overall percentage of 100.0% for Belo Horizonte and Teresina, 48.0% for Campo Grande, and 51.4% for Palmas. Conclusion/ImplicationsThe results showed the importance of the record linkage between health and police databases for the qualification of the RTCs and the victims in the five capital cities studied. This implies that deaths and serious injuries caused by RTCs are underestimated in the absence of integration of databases that record


2016 ◽  
Vol 2 (1) ◽  
pp. 20-29
Author(s):  
Ayanthi Saranga Jayawardena ◽  
S.C. Wickramasinghe ◽  
S.R.U. Wimalaratne

AbstractObjectives:To describe the use of Electronic Hospital Information System(EHIS) by the staff, to assess the competency of them to handle the EHIS and to assess the computer literacy among health care workers at the Out Patient’s Department(OPD) in District General Hospital(DGH) Trincomalee.Study design:A cross sectional descriptive study. A competency assessment test and a self administered questionnaire were used. Participants: All the staff members operating the EHIS at the OPD in DGH Trincomalee. Results: Regarding the general use of the EHIS medical officers (100%) used the EHIS to write prescriptions,(>70%)to get the patient’s socio-demographic details, enter patient’s history to retrieve previous medical records, to obtain what drugs available and what drugs out of stock at the outdoor pharmacy, for notification of diseases and used less frequently to get the laboratory reports (50-70%). The system was used for 17 tasks out of 20 tasks and most unused tasks were write the diagnosis according to the ICD-10. Nurses and attendents used the system less than half of the tasks for which the system was functional. The pharmacists use of the system was optimal. Overall respondents’ competency of using the system were high (>80%). Conclusions: Majority of staff members had low level of computer literacy. Majority of them used the system successfully. Recommendations: To strengthen the training program,combat several constraints and upgrade the system, provide digital X-ray imaging and download them to CDs and improved to write the diagnosis according to the ICD-10.Key words: Electronic Hospital Information System, Multi Disease Surveillance, Computer Literacy. 


1974 ◽  
Vol 13 (03) ◽  
pp. 125-140 ◽  
Author(s):  
Ch. Mellner ◽  
H. Selajstder ◽  
J. Wolodakski

The paper gives a report on the Karolinska Hospital Information System in three parts.In part I, the information problems in health care delivery are discussed and the approach to systems design at the Karolinska Hospital is reported, contrasted, with the traditional approach.In part II, the data base and the data processing system, named T1—J 5, are described.In part III, the applications of the data base and the data processing system are illustrated by a broad description of the contents and rise of the patient data base at the Karolinska Hospital.


1987 ◽  
Vol 26 (04) ◽  
pp. 189-194
Author(s):  
S. S. El-Gamal

SummaryModern information technology offers new opportunities for the storage and manipulation of hospital information. A computer-based hospital information system, dedicated to urology and nephrology, was designed and developed in our center. It involves in principle the employment of a program that allows the analysis of non-restricted, non-codified texts for the retrieval and processing of clinical data and its operation by non-computer-specialized hospital staff.This Hospital Information System now plays a vital role in the efficient provision of a good quality service and is used in daily routine and research work in this hospital. This paper describes this specialized Hospital Information System.


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