How to Train to Discharge a Dermatology Outpatient: A Review.

BACKGROUND/AIMS: The decision to discharge is a critical and common outpatient consultation event. However, little guidance exists over how discharge decision-making can be taught. We aimed to provide educational recommendations concerning outpatient discharge decision-making. METHODS: Recommendations were drawn from prior interviews with 40 consultant dermatologists and 56 dermatology outpatients, and from the "traffic light" design discharge information checklist, developed using the Delphi technique. RESULTS: The key strategies to follow to appropriately manage the outpatient discharge process are: to warn patients in advance, to understand patients' agendas, to allow extra time for the discharge process, to prepare patients to self-manage, to provide a "safety net" and provide the GP with a clear management plan. Aspects to be considered include patient mobility, presence of carer, type of employment, diagnostic certainty, and use of the checklist or guidelines. Key training aspects include teaching structured thought processes when discharging, discharging according to context, developing communication and negotiation skills, avoiding decision biases and encouraging good interprofessional collaboration. Training should include the consideration of the possibility of discharge at each consultation. Novel training strategies have been developed on how to appropriately manage the outpatient discharge process, including involving and informing patients. These strategies focus on safe decision-making, being patient-centred and organizing an efficient health care service framework. CONCLUSION: Structured outpatient discharge training for dermatologists is now possible, based on information from detailed doctor- and patient-based qualitative studies.

Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression.

PURPOSE: The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. METHODS: Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. RESULTS: The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). CONCLUSIONS: This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.

Evaluating alignment between Canadian Common Drug Review reimbursement recommendations and provincial drug plan listing decisions: an exploratory study.

BACKGROUND: The CADTH Common Drug Review was established in 2002 to prepare national health technology assessment reports to guide listing decisions for 18 participating drug plans. The aim of this study was to compare the nonmandatory recommendations from the Common Drug Review in Canada with the listing decisions of provincial payers to determine alignment. METHODS: We identified the recommendations issued by the Common Drug Review from Jan. 1, 2009, to Jan. 1, 2015, and compared these with the listing decisions of 3 provincial public payers (Alberta, British Columbia and Ontario) that participate in the Common Drug Review and the recommendations from Quebec. RESULTS: We identified 174 medicine-indication pairs in CADTH Common Drug Review reports issued from Jan. 1, 2009, to Jan. 1, 2015; 110 of these met the inclusion criterion. Among the 110 medicine-indication pairs, listing decisions were available for 95 in Alberta, 102 in Quebec, 104 in Ontario and 106 in BC. There was moderate to substantial agreement between provincial listing decisions and Common Drug Review recommendations: 74.5% (κ = 0.47, 95% confidence interval [CI] 0.31-0.64) for Quebec, 78.8% (κ = 0.56, 95% CI 0.41-0.72) for Ontario, 78.9% (κ = 0.58, 95% CI 0.42-0.74) for Alberta and 81.1% (κ = 0.62, 95% CI 0.47-0.77) for BC. INTERPRETATION: Our study showed moderate to substantial agreement between Common Drug Review recommendations and provincial listing decisions. Future studies can build on this research by evaluating the concordance between Common Drug Review recommendations and listing decisions of all participating federal, provincial and territorial drug plans.

PFI-14©: A Rasch Analysis Refinement of the Psoriasis Family Index.

OBJECTIVE: We aimed to assess the psychometric properties of the Psoriasis Family Index (PFI), a disease-specific instrument, to measure the secondary impact of psoriasis on the quality of life (QoL) of family members and partners of psoriasis patients. METHODS: PFI data were collected from 150 accompanying family members of psoriasis patients attending the dermatology department of a secondary referral hospital. Rasch analysis was used to examine various psychometric properties of the PFI, including dimensionality, response category functioning, fit statistics, scale reliability and validity, item targeting and differential item functioning (DIF). RESULTS: The 15-item PFI with 4 response options showed good overall fit to the Rasch model. The response category almost entirely followed the Linacre criteria, demonstrating its optimal functionality. All items except one had good fit to the Rasch model. The mean fit residual for the items was -0.085 ± 1.184 (range -1.54 to 2.7). Strict unidimensionality was achieved following removal of the misfitting item 5. The revised 14-item PFI maintained strong reliability (person separation index = 0.87). The scale showed minor mistargeting (mean difference between person and item location = 0.95 logits). None of the items displayed DIF across gender or relationship type. CONCLUSIONS: The PFI is the first disease-specific measure to quantify the QoL of family members of psoriasis patients. Following the application of Rasch analysis we now recommend the use of a 14-item version of the PFI: question No. 5 from the 15-item version has been deleted. This study demonstrates the promising psychometric performance of the PFI and confirms its potential as a useful outcome measure in future clinical research.

The impact of patients' chronic disease on family quality of life: an experience from 26 specialties.

BACKGROUND: Previous studies have assessed family quality of life in individual disease areas and specialties. The aim of this study was to investigate the impact of disease on family members of patients over a wide range of specialties and identify key impact areas. This information is essential in order to reveal the extent of this impact and to allow strategies to be developed to support the family members of patients with chronic disease. METHODS: Semi-structured interviews were carried out with 133 family members of mostly chronically ill patients from 26 medical specialties. Family members were invited to discuss all areas of their lives that had been affected by having an unwell relative. Thematic analysis was carried out using NVivo9® software. RESULTS: Most family members were female (61%), the partner or spouse of the patient (56%), or the parent (22%). Their mean age was 56.1 years (range: 21-85 years) and the mean duration of the patient's disease was 8.9 years (range: 1 month to 60 years). Ten key themes of family quality of life were identified from interviews. The median number of themes reported by family members was six (range: 1-10). The key themes included: emotional impact (mentioned by 92% of subjects), daily activities (91%), family relationships (69%), sleep and health (67%), holidays (62%), involvement in medical care and support given to family members (61%), work and study (52%), financial impact (51%), social life (37%), and time planning (14%). Relationships between the themes were identified. CONCLUSION: This large scale multi-specialty study has demonstrated the significant, yet similar, impact that illness can have on the quality of life of patients' family members. Family quality of life is a previously neglected area of health care which needs to be addressed in order to provide appropriate support for the patient and the family unit.

Do patient-reported outcomes have a role in the management of patients with cystic fibrosis?

BACKGROUND: Health-related quality of life (HRQoL) is a rapidly growing area of expertise and the most commonly used patient-reported outcome (PRO). The impact of cystic fibrosis (CF) on HRQoL is liable to be great, making CF patients ideal candidates for the application of HRQoL instruments. The aims of this study were to assess the affect of CF on HRQoL, to ascertain the reliability and validity of the United Kingdom Sickness Impact Profile (UKSIP) and the Cystic Fibrosis Quality of Life Questionnaire (CFQoL) in the adult CF population, and to examine their role in the management of patients. METHODS: Seventy participants were recruited from the All Wales Adult Cystic Fibrosis Centre at Llandough Hospital, UK. There were two stages to the study: self-report of the UKSIP and CFQoL; and completion of the same two questionnaires 7-10 days later. RESULTS: The areas of HRQoL most impaired by CF were employment and concerns regarding the future. The UKSIP and CFQoL showed high internal consistency (rα = 0.89-0.93) and test-retest reliability (r(s) = 0.57-0.94, p < 0.005) in the CF population. Validity was variable with the UKSIP showing discrimination across socio-demographic factors, whilst the CFQoL showed increased sensitivity to clinical variables. Many parameters influenced patient-reported HRQoL, with the greatest correlations seen with the Borg score (p < 0.005). The use of a HRQoL instrument in CF annual reviews is recommended to provide holistic patient care. The results of this study underpin the value of HRQoL as a patient-reported outcome measure in the management of adult CF.

Translating the science of quality of life into practice: What do dermatology life quality index scores mean?

This study's aim was to determine the relationship between Dermatology Life Quality Index (DLQI) scores and a Global Question (GQ) concerning patients' views of the overall impairment of their skin-related quality of life (QoL), and to express this relationship by identifying bands of DLQI scores equivalent to each GQ descriptor. A DLQI questionnaire and the GQ were mailed to 3834 adult general dermatology outpatients. There were 1993 (52%) responses: male 841; female 1152. Mean DLQI score = 4.86 (range 0-30, standard deviation (SD) = 5.83). Mean GQ score = 1.22 (range 0-4, SD = 1.20). The mean, mode, and median of the GQ scores for each DLQI score were used to devise several sets of bands of DLQI scores, and kappa coefficients of agreement calculated. The set proposed for adoption is: DLQI scores 0-1 = no effect on patient's life (GQ = 0, n = 754); DLQI scores 2-5 = small effect on patient's life (GQ = 1, n = 611); DLQI scores 6-10 = moderate effect on patient's life (GQ = 2, n = 327); DLQI scores 11-20 = very large effect on patient's life (GQ = 3, n = 242); DLQI scores 21-30 = extremely large effect on patient's life (GQ = 4, n = 59); kappa coefficient 0.489. Banding of the DLQI will aid the clinical interpretation of an individual's DLQI score and allow DLQI scores to inform clinical decisions.