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Making an accurate assessment
Published in Helen Taylor, Ian Stuart-Hamilton, Assessing the Nursing and Care Needs of Older Adults, 2021
Thompson30 cites base rate neglect as a source of error in decision making, where the individual who is making the decision ignores statistical probabilities which could preclude that decision. He cites the example of a clinician using a diagnostic test that has a high false-positive result rate for a disease with a very low level of incidence. If the clinician was to take a positive test result as evidence that the patient has the disease, they would then be ignoring the base rate. He suggests that whilst there is some (albeit what he regards as methodologically limited) evidence that nurses do use base rate recognition as a basis for their clinical decisions, there may be a tendency to ignore statistical probability when making the decisions. The nurse may focus on what may be irrelevant information.
Psychometric Testing in Functional GI Disorders
Published in Kevin W. Olden, Handbook of Functional Gastrointestinal Disorders, 2020
Computer scoring is recommended, as differential item-weighting and scale overlap complicates scoring by hand. When MBHI sheets are sent for computer scoring, a printed profile is included as part of the interpretative report. The interpretative report provides clinicians with a theoretically and empirically based assessment drawn from an integration of the patient’s style of coping, areas of concern, and probable response to treatment. An electronic teleprocessing system is also available to expedite the machine scoring process. Raw scores are transformed into base rate scores rather than to standard scores. Base rate scores utilize estimated class prevalence data. Cutpoints of 75-84 describe “moderate” and 85 or above describe “severe” in each of the subscales of the MBHI. The cutpoints on the psychosomatic correlate scales of the MBHI are based on base rate scores of designated populations. For example, a base rate score of 85 or higher on scale QQ, which is life-threat reactivity, represents a cutpoint above which 11% of a group of patients who have been given diagnosis of illness with potential life-threatening consequences should score. They have no significance for patients who do not fall into this category.
Fraud
Published in Julie Dickinson, Anne Meyer, Karen J. Huff, Deborah A. Wipf, Elizabeth K. Zorn, Kathy G. Ferrell, Lisa Mancuso, Marjorie Berg Pugatch, Joanne Walker, Karen Wilkinson, Legal Nurse Consulting Principles and Practices, 2019
The various payment systems noted in Table 26.1 impact traditional factors such as geographic index, hospital wage index, adjusted case mix, patient characteristics, and other adjustments for extraordinary outlier costs. Each payment system will have unique attributes. For example, each skilled nursing facility has a base rate and computes with factors such as an adjustment for geographic factors, hospital wage index, adjusted for case mix, multiplied by a Resource Utilization Group (RUG) weight which impact the intensity of patient characteristics. The implication for the LNC is to focus on validation of patient characteristics that place a patient into a particular RUG group. An anomaly would be a paraplegic patient who is receiving gait training exercises. The LNC is looking for clinical attributes that cannot be validated or are insufficient in their documentation. Each payment basic formula has a unique attribute. The LNC should review the relevant reimbursement methodology listed in Table 26.1 and isolate the unique formula characteristic that manipulated results in an improper payment.
Prevalence of Low Scores on Executive Functions Tests in a Spanish-Speaking Pediatric Population from 10 Latin American Countries and Spain
Published in Developmental Neuropsychology, 2020
Itziar Benito-Sánchez, Isabel Gonzalez, Rafael E. Oliveras-Rentas, Rosario Ferrer-Cascales, Ivonne Romero-García, Juan Carlos Restrepo Botero, Ivan Darío Delgado-Mejía, Esperanza Vergara-Moragues, Diego Rivera, Juan Carlos Arango-Lasprilla
Until recently, the availability of tests to evaluate EF in children and adolescents with country-specific normative data were limited for most Spanish-speaking countries. After a multi-country study, normative data for 10 commonly used neuropsychological tests in children and adolescents are now available for Chile, Colombia, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, Puerto Rico, and Spain (Rivera et al., 2017a; Rivera & Arango-Lasprilla, 2017). Therefore, one of the major problems reported by many practitioners in providing neuropsychological services for Spanish Speakers (Arango-Lasprilla, Stevens, Morlett Paredes, Ardila, & Rivera, 2017c) was addressed. Moreover, using the present base rate analyses would provide even more objective information in interpreting test performance, reducing the likelihood of false positives misinterpretation (i.e. interpreting a low score as impairment to a cognitively unimpaired individual). To our knowledge, this is the first cross-country analysis of base rates in test scores for pediatric Spanish Speakers.
Developing Measurement-Based Care for Youth in an Outpatient Psychiatry Clinic: The Penn State Psychiatry Clinical Assessment and Rating Evaluation System for Youth (PCARES-Youth)
Published in Evidence-Based Practice in Child and Adolescent Mental Health, 2020
Daniel A. Waschbusch, Amanda Pearl, Dara E. Babinski, Jamal H. Essayli, Sujatha P. Koduvayur, Duanping Liao, Dahlia Mukherjee, Erika F. H. Saunders
Having selected measures, the next step was to test them in routine practice. To accomplish this we conducted a pilot project of PCARES-Youth. Below we discuss the assessment stages as implemented in the pilot project. We then illustrate the value of PCARES-Youth as a tool for learning not just about individual patients but also about the base rates and demographics in a specific clinic. It is important to understand base rates at specific clinics because they often deviate from general base rates found in the literature; knowing this can then inform the diagnostic process (Youngstrom & Duax, 2005). We illustrate the value of MBC for learning about the landscape of our specific clinic as a system by examining four exemplar questions: (1) What type of psychopathologies are exhibited by youth seeking mental health services in our clinic? (2) Are there age and/or sex differences in psychopathology among youth in our clinic? (3) What proportion of youth have multiple types of psychopathology? and (4) Are mother and father ratings of the same youth associated? These questions were addressed using the pre-visit, pilot data. Finally, we summarize the results of a survey of clinicians who participated in PCARES-Youth.
Validity of the CES-D for depression screening in military service members with a history of mild traumatic brain injury
Published in Brain Injury, 2019
J E Kennedy, M W Reid, L H Lu, D B Cooper
To apply present results to a clinical setting, the prevalence of depression in that setting needs to be estimated. When the estimated base rate is low, a higher CES-D cut score corresponding to higher specificity and lower sensitivity is generally preferred, in order to limit the false-positive rate. However, this must be balanced by maintaining an acceptable rate of correct identification of individuals with depression. If the base rate is too low, the test will invariably become insensitive. In this paper, we have presented the sensitivity and specificity for a range of cut points on the CES-D, to allow clinicians to determine the positive and negative predictive values of the CES-D in samples of military service members with a history of mTBI in settings with different estimated base rates. Figure 4, adapted from Youngstrom (29, p 214) illustrates the effect of changing base rates on the PPV and NPV, given a cut score of 18. Changing from the base rate in the current study of 55.7% to a base rate of 20% results in an increase from 79% to 95% correct identification of those without depression (NPV = .95). However, it also leads to a reduction from 86% correct identification of individuals with depression to 54% correct (PPV = .54).