The biomarkers ATLAS: An audit on 1100 non-small cell lung cancer from an Italian knowledge-based database.

AIMS: To date, precision medicine has revolutionized the clinical management of Non-Small Cell Lung Cancer (NSCLC). International societies approved a rapidly improved mandatory testing biomarkers panel for the clinical stratification of NSCLC patients, but harmonized procedures are required to optimize the diagnostic workflow. In this context a knowledge-based database (Biomarkers ATLAS, https://biomarkersatlas.com/) was developed by a supervising group of expert pathologists and thoracic oncologists collecting updated clinical and molecular records from about 80 referral Italian institutions. Here, we audit molecular and clinical data from n = 1100 NSCLC patients collected from January 2019 to December 2020. METHODS: Clinical and molecular records from NSCLC patients were retrospectively collected from the two coordinating institutions (University of Turin and University of Naples). Molecular biomarkers (KRAS, EGFR, BRAF, ROS1, ALK, RET, NTRK, MET) and clinical data (sex, age, histological type, smoker status, PD-L1 expression, therapy) were collected and harmonized. RESULTS: Clinical and molecular data from 1100 (n = 552 mutated and n = 548 wild-type) NSCLC patients were systematized and annotated in the ATLAS knowledge-database. Molecular records from biomarkers testing were matched with main patients' clinical variables. CONCLUSIONS: Biomarkers ATLAS (https://biomarkersatlas.com/) represents a unique, easily managing, and reliable diagnostic tool aiming to integrate clinical records with molecular alterations of NSCLC patients in the real-word Italian scenario.

Mental Health Consultations in Immigration Detention: What Can We Learn From Clinical Records?

Objectives: Knowledge on mental health consultations in immigration detention and characteristics of people receiving consultations is scarce. Based on a sample of 230 adult men in immigration detention in Switzerland, we aimed to: (1) Quantify the proportion of persons receiving mental health consultations during detention; and (2) Identify socio-demographic and clinical characteristics associated with mental health consultations. Methods: Retrospective observational study with a cross-sectional design. Prevalence estimates, logistic regressions, and contingency tables were used to analyse the data. Results: A total of 30% of the sample received mental health consultations during detention. Time spent in immigration detention, mental health problems during detention, use of psychotropic medication, and self-harm were associated with mental health consultations. Although mental health consultations are provided to people with more severe mental health problems, 41% of persons with assessed mental health needs during the initial screening and 26% of those who self-harmed during detention did not receive mental health consultations. Conclusion: Mental health resources and screening procedures could be improved to ensure that mental health consultations are matched to clinical need in immigration detention settings.

Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words.

The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area of machine learning research. Large volumes of veterinary clinical narratives now exist curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, and the application of such techniques to these datasets is already (and will continue to) improve our understanding of disease and disease patterns within veterinary medicine. In part one of this two part article series, we discuss the importance of understanding the lexical structure of clinical records and discuss the use of basic tools for filtering records based on key words and more complex rule based pattern matching approaches. We discuss the strengths and weaknesses of these approaches highlighting the on-going potential value in using these "traditional" approaches but ultimately recognizing that these approaches constrain how effectively information retrieval can be automated. This sets the scene for the introduction of machine-learning methodologies and the plethora of opportunities for automation of information extraction these present which is discussed in part two of the series.

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction.

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients' time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient's clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient's time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

Learning from undercoded clinical records for automated International Classification of Diseases (ICD) coding.

OBJECTIVES: To develop an unbiased objective for learning automatic coding algorithms from clinical records annotated with only partial relevant International Classification of Diseases codes, as annotation noise in undercoded clinical records used as training data can mislead the learning process of deep neural networks. MATERIALS AND METHODS: We use Medical Information Mart for Intensive Care III as our dataset. We employ positive-unlabeled learning to achieve unbiased loss estimation, which is free of misleading training signal. We then utilize reweighting mechanism to compensate for the imbalance between positive and negative samples. To further close the performance gap caused by poor quality annotation, we integrate the supervision provided by the automatic annotation tool Medical Concept Annotation Toolkit which can ease the heavy burden of manual validation. RESULTS: Our benchmarking results show that positive-unlabeled learning with reweighting outperforms competitive baseline methods over a range of missing label ratios. Integrating supervision provided by annotation tool further boosted the performance. DISCUSSION: Considering the annotation noise and severe imbalance, unbiased loss estimation and reweighting mechanism are both important for learning from undercoded clinical records. Unbiased loss requires the estimation of false negative ratios and estimation through trained models is practical and competitive. CONCLUSIONS: The combination of positive-unlabeled learning with reweighting and supervision provided by the annotation tool is a promising solution to learn from undercoded clinical records.

Consistency analysis of perioperative self-reported pain scores and clinical records in patients with lung cancer / 中国胸心血管外科临床杂志

@#Objective     To analyse the consistency of perioperative self-reported pain scores of lung cancer patients with clinical records to provide a basis for optimal pain management. Methods    The patients with lung cancer who underwent surgical treatment in the Department of Thoracic Surgery, Sichuan Cancer Hospital from November 2017 to January 2020 were selected. They were divided into two groups based on the source of pain data. The self-report group used a questionnaire in which patients self-reported their pain scores, and the pain scores for the clinical record group were extracted from the electronic medical record system. Kappa test was used to compare the concordance of pain scores between the two groups preoperatively, on postoperative 1-6 days and on the day of discharge. McNemar's paired χ2 test was used to compare the differences in pain intensity levels between the two groups. Binary logistic multi-factor regression was used to analyse the factors influencing the concordance of severe pain (7-10 points) between the two groups. Results     Totally 354 patients were collected, including 191 males and 163 females, with an average age of 55.64± 10.34 years. The median postoperative hospital stay was 6 days. The consistency of pain scores between the two groups was poor (Kappa=–0.035 to 0.262, P<0.05), and the distribution of pain levels at each time point was inconsistent and statistically significant (P<0.001). The percentage of inconsistent severe pain assessment ranged from 0.28% to 35.56%, with the highest percentage of inconsistent severe pain assessment on postoperative day 1 (35.56%). Single-port thoracoscopic surgical access was an influencing factor for inconsistent assessment of severe pain on postoperative day 3 (OR=2.571, P=0.005). Conclusion     Self-reported perioperative pain scores of lung cancer patients are poorly aligned with clinical records. Clinical measures are needed to improve the accuracy of patient pain data reporting by choosing the correct assessment method, increasing education, and developing effective quality control measures.

MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records.

As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as "symptom-disease", "laboratory examination/imaging examination-disease", and "disease-treatment method". The continuous expansion of large electronic clinical records provides an opportunity to learn medical knowledge by machine learning. In this process, how to extract entities with a medical logic structure and how to make entity extraction more consistent with the logic of the text content in electronic clinical records are two issues that have become key in building a high-quality, medical knowledge graph. In this work, we describe a method for extracting medical entities using real Chinese clinical electronic clinical records. We define a computational architecture named MLEE to extract object-level entities with "object-attribute" dependencies. We conducted experiments based on randomly selected electronic clinical records of 1,000 patients from Shengjing Hospital of China Medical University to verify the effectiveness of the method.

Clinical Coders' Perspectives on Pressure Injury Coding in Acute Care Services in Victoria, Australia.

Pressure injuries (PIs) substantively impact quality of care during hospital stays, although only when they are severe or acquired as a result of the hospital stay are they reported as quality indicators. Globally, researchers have repeatedly highlighted the need to invest more in quality improvement, risk assessment, prevention, early detection, and care for PI to avoid the higher costs associated with treatment of PI. Coders' perspectives on quality assurance of the clinical coded PI data have never been investigated. This study aimed to explore challenges that hospital coders face in accurately coding and reporting PI data and subsequently, explore reasons why data sources may vary in their reporting of PI data. This article is based upon data collected as part of a multi-phase collaborative project to build capacity for optimizing PI prevention across Monash Partners health services. We have conducted 16 semi-structured phone interviews with clinical coders recruited from four participating health services located in Melbourne, Australia. One of the main findings was that hospital coders often lacked vital information in clinicians' records needed to code PI and report quality indicators accurately and highlighted the need for quality improvement processes for PI clinical documentation. Nursing documentation improvement is a vital component of the complex capacity building programs on PI prevention in acute care services and is relied on by coders. Coders reported the benefit of inter-professional collaborative workshops, where nurses and coders shared their perspectives. Collaborative workshops had the potential to improve coders' knowledge of PI classification and clinicians' understanding of what information should be included when documenting PI in the medical notes. Our findings identified three methods of quality assurance were important to coders to ensure accuracy of PI reporting: (1) training prior to initiation of coding activity and (2) continued education, and (3) audit and feedback communication about how to handle specific complex cases and complex documentation. From a behavioral perspective, most of the coders reported confidence in their own abilities and were open to changes in coding standards. Transitioning from paper-based to electronic records highlighted the need to improve training of both clinicians and coders.

Consideraciones legales sobre las notas de evolución en odontología / Legal considerations on medical charts in dentistry

El expediente clínico es considerado un documento de importancia médica y legal en donde se integran los datos necesarios para registrar el diagnóstico y los tratamientos realizados en cada paciente. Uno de los elementos más importantes dentro del expediente clínico son las notas de evolución, documentos con los que el odontólogo informa sobre el estado general del paciente y los tratamientos realizados cita tras cita. Existen legislaciones específicas en México que orientan al estomatólogo sobre los componentes mínimos necesarios que una nota de evolución debe tener; sin embargo, una de las omisiones más comunes de los odontólogos es que, por desconocimiento, no se dé la debida importancia a la elaboración de una adecuada nota de evolución, aumentando el riesgo de problemas legales. El objetivo del presente artículo es analizar la importancia de las notas de evolución dentro del expediente clínico, destacando su importancia clínica y legal (AU)

The clinical file is considered a document of medical and legal importance where the data necessary to record the diagnosis and the treatments performed on each patient are integrated. One of the most important elements within the clinical records are the medical charts, documents through which de dentist reports on the general condition of the patient and the treatments performed appointment after appointment. There are specific laws in Mexico that guide the stomatologist on the minimum necessary components that a medical chart must have, however, one of the most common omissions of dentist is that, due to ignorance, due importance is not given to the preparation of an adequate medical chart, increasing the risk of legal problems. The aim of this article is to analyze the importance of the evolution charts within the clinical records, highlighting their clinical and legal importance (AU)

Using WhatsApp as a Quick-Access Personal Logbook for Maintaining Clinical Records and Follow-Up of Orthopedic Patients.

Record keeping is an important aspect of orthopedic teaching, training, and practice for both documentation purpose and academic use. Physical logbooks may act as important documents but do not provide quick searchable access to the data they contain. Through this technique, we describe a simple method of creating personalized clinical records/logbook on WhatsApp through which individual records can be accessed with minimal basic key information in a span of a few seconds.

Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine.

BACKGROUND: In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future. METHODS: We developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. First, we determined the entity types included in this study through sample annotation. Then, we drafted a fine-grained annotation guideline by summarizing the characteristics of the dataset and referring to some existing guidelines. We iteratively updated the guidelines until the inter-annotator agreement (IAA) exceeded a Cohen's kappa value of 0.9. Comprehensive annotations were performed while keeping the IAA value above 0.9. RESULTS: We annotated the 10,197 clinical records in five rounds. Four entity categories involving 13 entity types were employed. The final fine-grained annotated entity corpus consists of 1104 entities and 67,799 tokens. The final IAAs are 0.936 on average (for three annotators), indicating that the fine-grained entity recognition corpus is of high quality. CONCLUSIONS: These results will provide a foundation for future research on corpus construction and named entity recognition tasks in the TCM clinical domain.

Charcot-Marie-Tooth disease: experience from a large Italian tertiary neuromuscular center.

INTRODUCTION: Charcot-Marie-Tooth (CMT) disease is the most common inherited neuromuscular disease. Thanks to the advances of the latest generation sequencing, more than 80 causative genes have been reported to date. METHODS: In this retrospective, observational study, we have analyzed clinical, electrophysiological, and genetic data of CMT patients in care at Neuromuscular Center of Messina University Hospital, Messina, Italy, for at least 22 years (from 1994 to 2016). Our center is the only reference center for genetic neuropathies in Sicily and in the southern part of Calabria. RESULTS: We reviewed the clinical records of 566 patients with the aim to evaluate how many patients received a genetic diagnosis and the distribution of the genetic subtypes. About 352/566 (62.19%) received a genetic diagnosis. The most frequent genetic diagnoses were CMT1A/PMP22 duplication (51.13%), followed by HNPP/PMP22 deletion (15.05%), CMT1B/MPZ mutation (10.22%), CMTX/GJB1 mutation (9.37%), and CMT2F/HSPB1 (4%). Other rare mutations included MFN2 mutation (n. 8 pts), BSCL2 mutation (n.8 pts), PMP22 point mutation (n.7 pts), GDAP1 mutation (n.4 pts), GARSmutation (n. 2 pts), TRPV4 mutation (n. 2 pts), LITAF mutation (n.1 pt), and NEFL mutation (n. 1 pt). CONCLUSIONS: Our study provides further data on frequency of CMT genes, subtypes in a wide Mediterranean area and contributes to help clinicians in addressing the genetic testing workup.

Prevalence, pain management and registration in Internal Medicine units. / Prevalencia, manejo y registro del dolor en unidades de Medicina Interna.

OBJECTIVE: To analyze the prevalence and management of pain episodes, their evaluation and recording in internal medicine hospitalization units in a third level public hospital of the regional health service of Castilla y León. METHOD: A descriptive cross-sectional study. The study population comprised patients hospitalized in internal medicine units. Pain prevalence was detected by the Brief Pain Inventory questionnaire. The management of pain episodes was analyzed as recorded in the clinical records. RESULTS: 83 patients were included, 73.5% of them reported pain and 67.2% did not know their analgesia regimen. More episodes of pain were identified in the women (P=.006) than in the men. The pharmacological administration was recorded in all cases; however, nurses recorded the episode in the clinical history of 29.5% of the patients. In no case, was the pain intensity or degree of relief recorded using the visual analogical scale. CONCLUSIONS: There is evidence of a high prevalence of pain in hospitalized patients and deficiencies in the management of pain episodes by nurses, both in evaluation and recording. This implies the need for pain control protocols and the implementation of evidence-based best practice guidelines to provide nurses with the means and support for adequate pain management.

Traditional Chinese medicine clinical records classification with BERT and domain specific corpora.

Traditional Chinese Medicine (TCM) has been developed for several thousand years and plays a significant role in health care for Chinese people. This paper studies the problem of classifying TCM clinical records into 5 main disease categories in TCM. We explored a number of state-of-the-art deep learning models and found that the recent Bidirectional Encoder Representations from Transformers can achieve better results than other deep learning models and other state-of-the-art methods. We further utilized an unlabeled clinical corpus to fine-tune the BERT language model before training the text classifier. The method only uses Chinese characters in clinical text as input without preprocessing or feature engineering. We evaluated deep learning models and traditional text classifiers on a benchmark data set. Our method achieves a state-of-the-art accuracy 89.39% ± 0.35%, Macro F1 score 88.64% ± 0.40% and Micro F1 score 89.39% ± 0.35%. We also visualized attention weights in our method, which can reveal indicative characters in clinical text.

Higher incidence of surgery-related complications in Wilms tumor nephrectomy from clinical records analysis compared with central database registration.

BACKGROUND/OBJECTIVES: Central database registrations are widely used tools for assessment of clinical results, but their reliability is subject to debate. The aim of this study is to evaluate the reliability of central database registration for Wilms tumor (WT) nephrectomy-related complications. DESIGN/METHODS: All Dutch patients undergoing WT nephrectomy according to the International Society of Paediatric Oncology (SIOP) 2001 protocol between 2001 and 2013 were evaluated. Results from the central database were analyzed and compared with data found via individual medical records analysis (gold standard). RESULTS: A total of 179 patients were included. Fourteen (7.8%) patients with a total of 17 complications were identified in the central database. The medical records revealed that 33 (18.4%) of patients had undergone a total of 41 complications (P < 0.001). Operative complications were similar between the groups (P = 0.157). Eleven short-term complications were noted in the central database versus 27 in the medical records (P = 0.059). Significantly more long-term complications, namely, adhesive small-bowel obstruction, were noted from the medical records compared with the central database (7 vs 1, respectively, P < 0.001). Postoperative chemotherapy was significantly delayed by on average 6 days (P < 0.0001) in patients with complications. No significant effect of complications on event-free survival, overall survival, or the relapse rate was recorded. CONCLUSION: Central database registrations underestimate the incidence of surgery-related complications after WT nephrectomy and need to be regarded with caution.

Calidad de las historias clínicas elaborados por internos de medicina - UMSA La Paz 2017 / Quality of clinical records prepared by the medical interns - UMSA La Paz 2017

INTRODUCCIÓN: La actividad del interno de medicina, considera como una práctica pre profesional, desarrollada en un contexto "real", significando que los internos efectúan su capacitación con pacientes verdaderos. La elaboración de la Historia Clínica (H.Cl.) por lo tanto deberá contener la mejor información del paciente. OBJETIVO: dirigida a identificar la calidad de la elaboración de las H.Cl. por los internos de medicina de la UMSA. MATERIAL Y MÉTODO: El diseñó corresponde a una investigación cuantitativa, observacional, longitudinal y analítica. Metodológicamente se efectuó el seguimiento a 8 internos durante el año 2015, revisando las H.Cl. que elaboraban durante sus diferentes pasantías en las especialidades de Medicina, Pediatría, Cirugía, Ginecología - Obstetricia, constituyendo un total de 64 expedientes clínicos, 8 por interno. Para la evaluación de la calidad de las H. Cl. se utilizó una plantilla con cinco tópicos, cuya validación se desprendió del procedimiento empleado en las auditorías internas utilizadas en los hospitales. RESULTADOS Y DISCUSIÓN: Solo 19 H.Cl. lograron la categoría de aceptables (29,7 por ciento), frente a las otras dos categorías 15 H.Cl. fueron catalogadas como insuficientes (23,4 por ciento) el resto de las historias clínicas 30, adolecían de varios defectos que se las califico como inaceptables (46,9 por ciento). SE puede señalar que solo tres de diez H.Cl. fueron elaboradas apropiadamente, adicionalmente cerca de la mitad del total de los documentos fueron apuntadas como inaceptables, reflejando la las H.Cl. fueron elaboradas de manera impropia, catalogadas como de mala calidad CONCLUSIONES: La evaluación sobre la calidad de las H.Cl. permitió identificary poner de manifiesto la presencia de una brecha marcada entre el propósito ideal buscado por el plan de estudios de la carrera y el producto final como parte del proceso de profesionalización.

INTRODUCTION: The medical intern activity, considers as a pre-professional practice, developed in a "real" context, meaning that the interns carry out their training with real patients. The elaboration of the Clinical Record (H.Cl.) should therefore contain the best patient information. OBJECTIVE: aimed at identifying the quality of the elaboration ofH. Cl. by the medical interns of UMSA. MATERIAL AND METHOD: The design corresponds to a quantitative, observational, longitudinal and analytical investigation. Methodologically, 8 interns were monitored during 2015, reviewing the H.Cl. that they elaborated during their different internships in the specialties of Medicine, Pediatrics, Surgery, Gynecology - Obstetrics, constituting a total of 64 clinical files, 8 per intern. For the evaluation of the quality of the H.Cl. A template with five topics was used, whose validation was detached from the procedure used in the internal auditing used in the hospitals. RESULTS AND DISCUSSION: Only 19 H.Cl. achieved the category of acceptable (29.7 percent), compared to the other two categories 15 H.Cl. were classified as insufficient (23.4 percent) the rest of the clinical histories 30, suffered from several defects that were classifiedas unacceptable (46.9 percent). It can be noted that only three out of ten H.Cl. were elaborated appropriately, additionally close to half of the total of the documents were pointed out as unacceptable, reflecting the H.Cl. were improperly elaborated, cataloged as of poor quality CONCLUSIONS: The evaluation on the quality ofH. Cl. allowed identifying and highlighting the presence of a marked gap between the ideal purpose sought by the major curriculum and the final product as part of the professionalization process.

Detecting the manipulation of digital clinical records in dental practice.

INTRODUCTION: Radiography provides many advantages in the diagnosis and management of dental conditions. However, dental X-ray images may be subject to manipulation with malicious intent using easily accessible computer software. METHODS: In this study, we sought to evaluate a dentist's ability to identify a manipulated dental X-ray images, when compared with the original, using a variant of the methodology described by Visser and Kruger. Sixty-six dentists were invited to participate and evaluate 20 intraoral dental X-ray images, 10 originals and 10 modified, manipulated using Adobe Photoshop to simulate fillings, root canal treatments, etc. RESULTS: Participating dentists were correct in identifying the manipulated image in 56% of cases, 6% higher than by chance and 10% more than in the study by Visser and Kruger. CONCLUSION: Malicious changes to dental X-ray images may go unnoticed even by experienced dentists. Professionals must be aware of the legal consequences of such changes. A system of detection/validation should be created for radiographic images.

De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.

The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks. Track 1 focused on de-identification of a new corpus of 1000 psychiatric intake records. This track tackled de-identification in two sub-tracks: Track 1.A was a "sight unseen" task, where nine teams ran existing de-identification systems, without any modifications or training, on 600 new records in order to gauge how well systems generalize to new data. The best-performing system for this track scored an F1 of 0.799. Track 1.B was a traditional Natural Language Processing (NLP) shared task on de-identification, where 15 teams had two months to train their systems on the new data, then test it on an unannotated test set. The best-performing system from this track scored an F1 of 0.914. The scores for Track 1.A show that unmodified existing systems do not generalize well to new data without the benefit of training data. The scores for Track 1.B are slightly lower than the 2014 de-identification shared task (which was almost identical to 2016 Track 1.B), indicating that these new psychiatric records pose a more difficult challenge to NLP systems. Overall, de-identification is still not a solved problem, though it is important to the future of clinical NLP.

Adverse Drug Reactions Related to Drug Administration in Hospitalized Patients.

INTRODUCTION: Drug treatment may be related to the development of adverse drug reactions (ADRs). AIM: In this paper, we evaluated the ADRs in patients admitted to Catanzaro Hospital. METHODS: After we obtained the approval by local Ethical Committee, we performed a retrospective study on clinical records from March 01, 2013 to April 30, 2015. The association between drug and ADR or between drug and drug-drug-interactions (DDIs) was evaluated using the Naranjo's probability scale and Drug Interaction Probability Scale (DIPS), respectively. RESULTS: During the study period, we analyzed 2870 clinical records containing a total of 11,138 prescriptions, and we documented the development of 770 ADRs. The time of hospitalization was significantly higher (P<0.05) in women with ADRs (12.6 ± 1.2 days) with respect to men (11.8± 0.83 days). Using the Naranjo score, we documented a probable association in 78% of these reactions, while DIPS revealed that about 22% of ADRs were related to DDIs. Patients with ADRs received 3052 prescriptions on 11,138 (27.4%) having a mean of 6.1±0.29 drugs that was significantly higher (P<0.01) with respect to patients not experiencing ADRs (mean of 3.4±0.13 drugs). About 19% of ADRs were not diagnosed and were treated as new diseases. CONCLUSION: Our results indicate that drug administration induces the development of ADRs also during the hospitalization, particularly in elderly women. Moreover, we also documented that ADRs in some patients are under-diagnosed, therefore, it is important to motivate healthcare to report the ADRs in order to optimize the patients' safety.

The effect of length, duration, and intensity of psychological therapy on CORE global distress scores.

OBJECTIVES: This study explores whether improvements, as measured by the CORE-OM/10, as a result of psychological therapy were related to length of treatment in weeks, number of treatment sessions, or treatment intensity, as well as any effect of diagnostic group. METHODS AND DESIGN: Pre- and post-therapy CORE-OM/10 scores were extracted from the clinical records of all secondary care adult psychological therapy team patients who undertook psychological therapy between 2010 and 2013 in one mental health trust. Of the 4,877 patients identified, 925 had complete records. Length of therapy was divided by the number of sessions to create 'treatment intensity' (sessions per week). Nonparametric analyses were used, initial score was controlled for, and diagnostic group was explored. RESULTS: No relationship was found between change in score and the number of sessions, therapy length, or treatment intensity; however, change in score was positively correlated with first-session score. Patients with higher initial scores had longer therapies; however, treatment intensity was similar for patients with lower pre-therapy distress. There were differences in treatment length (weeks) between diagnostic groups. Demographic differences were found between patients with and without complete records, prompting caution in terms of generalizability. CONCLUSIONS: These findings are consistent with the responsive regulation model (Barkham et al., 1996) which proposes that patients vary in their response to treatment, resulting in no associations between session numbers or treatment intensity and therapeutic gain with aggregated scores. Patients with higher CORE scores at the outset of psychological therapy had longer not more intensive therapy. There was variation in treatment intensity between diagnostic clusters. PRACTITIONER POINTS: Number of sessions, length of therapy (in weeks), and treatment intensity (the number of sessions per week between the first and last therapy sessions) were not related to therapeutic gains. These results fit with a responsive regulation model of therapy duration, suggesting an individualized approach to therapy cessation as opposed to therapy session limits as the number of sessions a patient experienced was not generally associated with outcome. We found that clients with a diagnosis of a behavioural syndrome (F50-59) had less 'intensive' therapy; they experienced the same number of sessions over a longer time frame. Despite this, there were no associations between diagnosis category and change in score.