HPV DNA testing with cytology triage in cervical cancer screening: Influence of revealing HPV infection status

BACKGROUND: Knowledge of cervical human papillomavirus (HPV) status might influence a cytotechnician's assessment of cellular abnormalities. The authors compared original cytotechnicians' Papanicolaou (Pap) readings for which HPV status was concealed with Pap rereads for which HPV status was revealed separately for 3 screening populations. METHODS: Previously collected cervical Pap smears and clinical data were obtained from the Canadian Cervical Cancer Screening Trial (study A), the Democratic Republic of Congo Community-Based Screening Study (study B), and the Brazilian Investigation into Nutrition and Cervical Cancer Prevention (study C). Smears were reread with knowledge of HPV status for all HPV-positive women as well as a sample of HPV-negative women. Diagnostic performance of Pap cytology was compared between original readings and rereads. RESULTS: A total of 1767 Pap tests were reread. Among 915 rereads for HPV-positive women, the contrast between "revealed" and "concealed" Pap readings demonstrated revisions from negative to positive results for 109 women (cutoff was atypical squamous cells of undetermined significance or worse) and 124 women (cutoff was low-grade squamous intraepithelial lesions [LSIL] or worse). For a disease threshold of cervical intraepithelial neoplasia of grade 2 or worse, specificity significantly declined at the atypical squamous cells of undetermined significance cutoff for studies A (86.6% to 75.3%) and C (42.5% to 15.5%), and at the LSIL cutoff for study C (61.9% to 37.6%). Sensitivity remained nearly unchanged between readings, except in study C, in which reread performance was superior (91.3% vs 71.9% for the LSIL cutoff). CONCLUSIONS: A reduction in the diagnostic accuracy of Pap cytology was observed when revealing patients' cervical HPV status, possibly due to a heightened awareness of potential abnormalities, which led to more false-positive results. Cancer (Cancer Cytopathol) 2015. (c) 2015 American Cancer Society.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.