Factors determining quality of clinical information recorded by physicians on toxicology consulting forms

1998 ◽  
Vol 95 ◽  
pp. 81
Author(s):  
A. Walubo
Keyword(s):  
Clinical Information

scholarly journals Implementing an Integrated Large-Scale Clinical Information System for ISSSTE’s Hospital Network in Mexico

2021 ◽  
Vol 3 (2) ◽  
pp. 444-453
Author(s):  
Arturo Cervantes Trejo ◽  
Sophie Domenge Treuille ◽  
Isaac Castañeda Alcántara
Keyword(s):  
Health Care ◽  
Information System ◽  
Health Care Services ◽  
Large Scale ◽  
Clinical Information System ◽  
Clinical Information ◽  
Public Hospitals ◽  
Care Services ◽  
Cost Reductions

AbstractThe Institute for Security and Social Services for State Workers (ISSSTE) is a large public provider of health care services that serve around 13.2 million Mexican government workers and their families. To attain process efficiencies, cost reductions, and improvement of the quality of diagnostic and imaging services, ISSSTE was set out in 2019 to create a digital filmless medical image and report management system. A large-scale clinical information system (CIS), including radiology information system (RIS), picture archiving and communication system (PACS), and clinical data warehouse (CDW) components, was implemented at ISSSTE’s network of forty secondary- and tertiary-level public hospitals, applying global HL-7 and Digital Imaging and Communications in Medicine (DICOM) standards. In just 5 months, 40 hospitals had their endoscopy, radiology, and pathology services functionally interconnected within a national CIS and RIS/PACS on secure private local area networks (LANs) and a secure national wide area network (WAN). More than 2 million yearly studies and reports are now in digital form in a CDW, securely stored and always available. Benefits include increased productivity, reduced turnaround times, reduced need for duplicate exams, and reduced costs. Functional IT solutions allow ISSSTE hospitals to leave behind the use of radiographic film and printed medical reports with important cost reductions, as well as social and environmental impacts, leading to direct improvement in the quality of health care services rendered.

scholarly journals Vision-related Quality of Life in Patients With Glaucoma: the Role of Illness Perceptions

2021 ◽  
Author(s):  
Qiqi Zhang ◽  
Wenzhe Zhou ◽  
Di Song ◽  
Yanqian Xie ◽  
Hao Lin ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Illness Perceptions ◽  
Linear Regression Analysis ◽  
Illness Perception ◽  
Clinical Information ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Illness Perception Questionnaire ◽  
Related Quality

Abstract Purpose: To explore the predictive effect of illness perceptions on vision-related quality of life (VRQoL) in Chinese glaucoma patients.Methods: In this cross-sectional study, 97 patients with glaucoma completed the Brief Illness Perception Questionnaire (BIPQ), the Glaucoma Quality of Life-15 (GQL-15), and a questionnaire with sociodemographic and clinical information. Correlation analysis and hierarchical linear regression analysis were performed.Results: The BIPQ total score was positively correlated with the scores of the total GQL-15 and its four dimensions. Chronic comorbidities, type of glaucoma, best-corrected visual acuity (BCVA), mean defect (MD) of visual field in the better eye, and identity in the BIPQ were the critical predictors of VRQoL. Illness perceptions independently accounted for 7.8% of the variance in the VRQoL of glaucoma patients.Conclusions: Patients with stronger illness perceptions who perceive themselves as having more glaucoma symptoms are likely to experience worse VRQoL. Illness perceptions in glaucoma patients deserve clinical attention, and further studies are needed to examine whether cognitive interventions targeting illness perceptions can improve VRQoL.

scholarly journals Communication and access to information in assessing the quality of multiprofessional care of patients

2010 ◽  
Vol 8 (3) ◽  
pp. 303-307
Author(s):  
Leny Vieira Cavalheiro ◽  
Paola Bruno de Araújo Andreoli ◽  
Nadia Sueli de Medeiros ◽  
Telma de Almeida Busch Mendes ◽  
Roselaine Oliveira ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Healthcare Professionals ◽  
Clinical Information ◽  
Relevant Information ◽  
Cross Sectional Study ◽  
Model Group ◽  
Cross Sectional ◽  
Multiprofessional Team ◽  
Under Performing

ABSTRACT Objective: To assess the quality of a multiprofessional healthcare model for in-hospital patients by means of two performance indicators (communication and knowledge about the case). Methods: A cross-sectional study assessed the knowledge that professionals had about the clinical information of patients and the use of communication strategies by the team. Healthcare professionals were interviewed during their work period. Seven occupational categories were interviewed. A total of 199 medical charts were randomly selected for interviews, and 312 professionals of different categories were interviewed. The sample comprised mostly nurses and physical therapists in the charts that were interviewed. Results: There were no statistically significant differences between the expected performing model group and the under-performing model group for sex, location and job. In the under-performing model group, a larger number of professionals correlated with less knowledge. Communication was improved when nurses had the relevant information about interdisciplinary care (97.4%), appropriate use of the Plan of Care form (97.0%), and formalized discussions with physicians (88.2%). In the expected performing model group, it was observed that the higher the number of healthcare professionals involved, the higher the communication levels. Conclusions: This model of care based on case knowledge and multiprofessional team communication performance indices allowed to assess quality of care. This assessment is measurable and there is the possibility of establishing the quality of care delivered.

The Nature of Insomnia

CNS Spectrums ◽  
2007 ◽  
Vol 12 (S10) ◽  
pp. 3-5 ◽  
Author(s):  
Thomas Roth
Keyword(s):  
Behavioral Therapy ◽  
Clinical Information ◽  
Negative Results ◽  
Behavioral Therapies ◽  
Insomnia Treatment ◽  
Treatment Use ◽  
Patients Experience ◽  
Pharmacologic Agents ◽  
Impaired Quality

AbstractInsomnia is a disorder characterized by chronic sleep disturbance associated with daytime disability or distress, such as memory impairment and fatigue, that occurs despite adequate opportunity for sleep. Insomnia may present as difficulty falling/staying asleep or as sleep that is nonrestorative. Studies show a strong correlation between insomnia and impaired quality of life. Pain conditions and depression are commonly associated with insomnia, either as secondary or comorbid conditions. In addition, a greater incidence of anxiety, alcohol and drug dependence, and cardiovascular disease is found in people with insomnia. Data indicate insomnia results from over-engaged arousal systems. Insomnia patients experience increased metabolic rate, body temperature, and heart rate, and elevated levels of norepinephrine and catecholamines. Pharmacologic options for the treatment of insomnia include benzodiazepine hypnotics, a selective melatonin receptor agonist, and sedating antidepressants. However, insomnia may be best treated with cognitive-behavioral therapy and instruction in good sleep hygiene, either alone or in concert with pharmacologic agents. Studies on the effects of insomnia treatment use variable methodologies or do not publish negative results, and there are currently no studies of treatment focusing on morbidity. Further research is necessary to better understand the effects of insomnia therapies on medical and psychiatric disorders.In this Clinical Information Supplement, Thomas Roth, PhD, describes the nature of insomnia and its pathophysiology. Next, Andrew D. Krystal, MD, MS, reviews morbidities associated with insomnia. Finally, Joseph A. Lieberman III, MD, MPH, provides an overview of therapeutics utilized in patients with insomnia, including behavioral therapies and pharmacologic options.

scholarly journals QOLP-10. PATIENT-REPORTED OUTCOMES DURING TREATMENT FOR GLIOBLASTOMA: SYMPTOM BURDEN AND ASSOCIATED IMPACT ON DAILY LIFE

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi199-vi199
Author(s):  
Meghan Hultman ◽  
Megan Tipps ◽  
Minda Liu ◽  
Nilanjana Banerji ◽  
Patricia Bruns ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Brain Tumor ◽  
Daily Life ◽  
Symptom Burden ◽  
Clinical Information ◽  
Symptom Experience ◽  
Surgical Interventions ◽  
Patient Reported ◽  
Late Treatment

Abstract INTRODUCTION Glioblastoma is the most common primary malignant brain tumor, with an annual incidence of 3–4 new cases per 100,000 people. Despite advances in chemotherapy, radiotherapy, and surgical interventions, prognosis remains poor. Aims of treatment for life-limiting diagnoses should extend beyond increasing survival time to include palliation of symptoms and promotion of quality of life. Here we describe patient-reported symptoms both early and late in the glioblastoma treatment trajectory, as well as their associated interference with daily life. METHODS Fifty patients with newly diagnosed glioblastoma were recruited between September 2015-March 2018. Demographic and clinical information was collected from medical records. Patients also completed the brain tumor-specific MD Anderson Symptom Inventory (MDASI-BT) approximately every 8 weeks from completion of chemoradiation through cessation of active disease treatment. Both individual and categories of related symptoms reported in the initial two (early treatment) and final two (late treatment) MDASI-BT assessments were examined for symptom frequency, severity, and relationships between symptom severity and interference with daily life. Patients still receiving treatment or who completed < 4 total MDASI-BT assessments were excluded from the analysis. RESULTS The most commonly reported symptoms were often, but not always, consistent with the symptoms rated as most severe. This was true for individual symptoms as well as categories of symptoms. The most common/severe symptoms also changed from early to late treatment. Overall symptom burden was positively correlated with symptom interference in daily life (r=0.67, p< 0.0001). Regression analysis identified different symptoms as predictors of interference in early as opposed to late treatment, and these predictors did not necessarily align with the most common/severe symptoms. CONCLUSION Symptom experience for patients with glioblastoma is complex and dynamic. Attention to symptom trajectories for patients with glioblastoma may direct approaches to assessment, early identification, and symptom management, thus promoting better quality of life.

Psychiatric Morbidity in HIV Patients

1998 ◽  
Vol 43 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Hani Raoul Khouzam ◽  
Nancy J Donnelly ◽  
Nabil F Ibrahim
Keyword(s):  
Quality Of Life ◽  
Psychiatric Illness ◽  
Literature Search ◽  
Psychiatric Morbidity ◽  
Clinical Information ◽  
Hiv And Aids ◽  
Psychiatric Evaluation ◽  
Diagnostic Code ◽  
Dsm Iv

Objective: To provide an accessible source of clinical information related to the diagnosis and treatment of psychiatric morbidity associated with HIV infection. Method: A selective MEDLINE literature search was used to identify 225 relevant articles, 67 of which were selected for inclusion based upon the presence of psychiatric morbidity. Psychiatric morbidity was defined by the presence of a DSM-IV diagnostic code reflecting psychiatric illness. Results: HIV-infected patients have high rates of psychiatric morbidity. Conclusion: The psychiatric evaluation and treatment of psychiatric morbidity improve the quality of life of HIV and AIDS patients.

The treatment of common mental disorders by a community team based in primary care: a cost-effectiveness study

1996 ◽  
Vol 26 (3) ◽  
pp. 487-492 ◽  
Author(s):  
D. Goldberg ◽  
G. Jackson ◽  
R. Gater ◽  
M. Campbell ◽  
N. Jennett
Keyword(s):  
Primary Care ◽  
Common Mental Disorders ◽  
Psychiatric Service ◽  
Severity Of Illness ◽  
Clinical Information ◽  
Cost Effective ◽  
Mental Health Team ◽  
Number Of Patients ◽  
Community Team

SynopsisThirty patients suffering from new episodes of depression or anxiety disorders seen by a hospital-based psychiatric service were matched for severity of illness with 30 patients seen by a community mental health team based upon primary care. These patients were drawn from a total of 108 such patients seen in the community and 57 seen by the hospital service. Clinical and social outcomes were similar in both groups, and neither was clearly superior in terms of quality of clinical information recorded. However, patients treated in the community were seen more quickly, had more continuity of care and were more satisfied with the service. Health services costs were less for those patients treated in the community, because patients were less likely to be admitted. With one a typical patient excluded, treatment by the community team is more cost effective. The greater number of patients seen by the primary-care-based service means that there is no overall cost saving to the NHS.

scholarly journals Technologies for Complex Intelligent Clinical Data Analysis

2016 ◽  
Vol 71 (2) ◽  
pp. 160-171 ◽  
Author(s):  
A. A. Baranov ◽  
L. S. Namazova-Baranova ◽  
I. V. Smirnov ◽  
D. A. Devyatkin ◽  
A. O. Shelmanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Chronic Diseases ◽  
Clinical Information ◽  
Structured Data ◽  
Unstructured Data ◽  
Intelligent Analysis ◽  
Pediatric Center

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient’s features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality.Aims: the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center.Materials and methods: Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as «negation» (indicates that the disease is absent), «no patient» (indicates that the disease refers to the patient’s family member, but not to the patient), «severity of illness», «disease course», «body region to which the disease refers». Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the method for determining the most informative patients’ features are also proposed.Results: Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records of patients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases.Conclusions: The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare. 

scholarly journals Neuropsychiatric Symptom Clusters and Quality of Life Among Patients With Stroke

2019 ◽  
Vol 42 (2) ◽  
pp. 1-11
Author(s):  
Vachira Posai ◽  
Ploenta Pipatsombat ◽  
Pramote Thangkratok
Keyword(s):  
Quality Of Life ◽  
Behavioral Problems ◽  
Neuropsychiatric Symptoms ◽  
Clinical Information ◽  
Average Frequency ◽  
Neuropsychiatric Symptom ◽  
Symptom Clusters ◽  
Mood Disturbance ◽  
Quality Of Life Scale

Background: There are relatively few reports of neuropsychiatric symptom clusters which affect the quality of life in patients with stroke.Objective: To study neuropsychiatric symptom clusters and the relationship between neuropsychiatric symptom clusters and quality of life among patients with stroke.Methods: This descriptive study conducted in 140 patients with stroke at Sanpasithiprasong Hospital, Ubon Ratchathani, Thailand; they were selected purposively. Data were collected through patient general and clinical information, the neuropsychiatric symptoms assessment, and the 12-item stroke-specific quality of life scale conducted from September 2018 to January 2019. Results: The results illustrated that 12 neuropsychiatric symptoms of patients with stroke embraced 3 symptom clusters were psychosis clusters, behavioral problems clusters, and mood disturbance clusters. (average frequency = 1.58, 2.37, 2.49; and average severity = 2.08, 2.73, 2.82, respectively). Among 3 symptom clusters, psychosis (r = 0.289; P < .01), behavioral problems (r = 0.719; P < .01), and mood disturbance (r = 0.716; P < .01) clusters were statistically significantly associated with all aspects of quality of life.Conclusions: This study showed that neuropsychiatric symptom, especially behavioral problems and mood disturbance clusters were correlated with quality of life among patients with stroke.

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