Quality Management in Health Care最新文献

Background and objectives: To address health care spending growth, coordinated care, and patient-centered primary care, most states in the United States have adopted value-based care coordination programs such as patient-centered medical homes (PCMHs). The objective of this study was to understand the relationship between having access to PCMHs and emergency department (ED) utilization for high cost/need children with autism and children with mental health disorders (MHDs).

Methods: This cross-sectional study included 87 723 children between ages 3 and 17 years in the 2016-2018 National Survey for Children's Health. Multivariate-adjusted logistic regression analyses were used to assess the association between ED and PCMH utilization for children with autism, with MHDs without autism, and others without autism or MHDs. Marginal predictions were used to examine whether PCMH utilization was moderated by health conditions.

Results: The results showed that children with a PCMH had a 16% reduction in the odds to visit the ED (adjusted odds ratio [aOR] = 0.84; confidence interval [CI], 0.77-0.92; P < .001). When compared with the reference group of children without autism and without MHDs, children with MHDs but without autism had 93% higher odds to visit the ED (aOR = 1.93; CI, 1.75-2.13; P < .001) and children with autism had 35% higher odds to visit the ED (aOR = 1.35; CI, 1.04-1.75; P = .023). Marginal effects results suggested that PCMHs reduced the odds of ED visits the most for children with MHDs without autism and reduced the predicted ED visits from 30.1% to 23.7% ( P < .001).

Conclusions: Primary care quality improvement through access to a PCMH reduced ED visits for children, but the effect varied by autism and MHD conditions. Future PCMH efforts should continue to support children with autism and address unmet needs for children with MHDs with a focus on needed care coordination, family-centered care, and referrals.

Background and objectives: The quality of health care in hospital at home (HaH) has been measured in different countries using simple indicators and clinical results that only contribute to some dimensions of the quality of health care. We sought to generate indicators to comprehensively evaluate the quality of health care provided to HaH users through the e-Delphi technique.

Methods: The e-Delphi technique was performed with the participation of 17 HaH experts. The methodological strategy applied in this study was divided into the following 3 phases: a preparatory phase; consultation phase; and consensus phase. Three rounds of consultations were conducted with experts. In round 1, they were asked to identify which aspects of HaH they believed should be evaluated using an indicator for each of the following 6 dimensions of health care quality: effectiveness; efficiency; timeliness; patient-centered care; equity; and safety. In round 2, they were asked to rate each indicator using a 5-point Likert-type scale with the following values: (1) Totally disagree; (2) Disagree; (3) Moderately agree; (4) Agree; and (5) Totally agree. The criteria for evaluating each indicator were as follows: (1) The indicator is a useful measure for assessing the quality of health care provided to HaH users. (2) The indicator is clearly and specifically written and does not require modification. (3) The indicator is essential and incorporates information that can be extracted from HaH program records. An indicator was considered approved if it received at least 65% approval from the expert panel for each evaluation criterion. In round 3, experts were asked to reassess their ratings, taking into account the opinions of the other experts. The reliability of this technique was ensured through credibility, reliability, and confirmability. We obtained ethical approval of the corresponding institutions and informed consent from the participating experts.

Results: Nine unpublished and reliable indicators were generated. In addition, 13 indicators were incorporated that evaluate aspects previously analyzed by other authors and/or national and international institutions, which were adapted to be used in HaH. The total indicators generated (n = 22) represented all dimensions of the quality of health care: safety; opportunity; effectiveness; efficiency; equity; and patient-centered care.

Conclusions: The 22 indicators generated through the e-Delphi technique permit a comprehensive evaluation of the quality of health care provided to HaH users.

Background and objectives: The integration of lean management in optimizing nursing workflow necessitates the careful examination of several factors, including nurses' work efficiency, patient experience, and health outcomes. To evaluate the extent of unfinished nursing care and patient satisfaction, we have incorporated the lean management approach into our quality improvement efforts. This proactive measure aims to address potential adverse outcomes, such as subpar inpatient experiences, escalated occurrence of adverse events, and decreased job satisfaction among nursing staff.

Methods: We utilized the lean management methodology of value stream mapping in a specific facility between February and August 2021, aiming to pinpoint the crucial areas for enhancing nurses' workflow. By employing fishbone diagrams, we thoroughly analyzed the underlying causes, and subsequently employed the Plan-Do-Study-Act model to execute interventions devised based on these identified causes. Interventions included: (1) specifying the time of doctors' conventional rounds; (2) changing unreasonable scheduling; (3) employing 5S management to manage nursing supplies; and (4) eliminating duplicate papers and electronic reports.

Results: After implementing these interventions, the rate of unfinished nursing reduced from 73.4% to 39.6%, and that of finished nursing care during the shift increased from 38.6% to 71.4%. Overtime was reduced from 37.2 ± 22.4 minutes to 14.1 ± 3.6 minutes. The total patient satisfaction score for the Patient Satisfaction Questionnaire short-form increased ( P < .05).

Conclusions: The lean management of quality improvement methodologies provides effective enhancement to the work efficiency of nurses.

Background and objectives: Hospitals offered remote health care services to enable seamless and safe execution of health care operations during the pandemic while ensuring all stakeholders' quality of life and well-being. We explore various push and pull motivational factors that can influence customers' intentions to adopt remote health care services. We also examine the mediating role of perceived health care quality in light of their hedonic experience during the pandemic.

Methods: We develop our conceptual framework based on push-pull motivational theory and investigate remote health management intentions. We carried out a case study in a multispecialty hospital in India. We collected data from 482 internet users who accessed their remote services during the pandemic, and we performed statistical analysis.

Results: Our findings show the push and pull motivators significantly influencing internet populations' remote health management intentions. The results also highlight the mediation of perceived health care quality between push factors and remote health management intentions.

Conclusions: The push-pull motivators significantly regulate remote health care intentions among the internet population. Technologists and researchers must address these factors while introducing future remote services to widen adoption. This is one of the pioneering studies investigating people's remote health care intentions post-pandemic.

Background: Bundled interventions and auditing have been recommended to reduce central line-associated bloodstream infection (CLABSI) events at acute care hospitals. We review the outcomes of a bundle audit program at an adult and pediatric academic medical center from April 1, 2021, to May 31, 2022.

Objectives: To analyze the impact on CLABSI rates following the introduction of a central line maintenance bundle audit process.

Methods: All audit survey data, CLABSI event rates, and line days were collected. Statistical relationships were evaluated for CLABSI bundle performance with CLABSI rates and audit volume with CLABSI rates. Analyses were conducted at the hospital and unit level.

Result: No correlation is found between CLABSI rates and audit performance at the hospital level (adult units, P = .619, r-sq = 2.13%; peds/NICU, P = .825, r-sq = 0.43%) or at the unit level (n = 7; P = .8-.896, r-sq = 0.15%-18.2%). There was no correlation in CLABSI rates when reviewing performance by audit volume at the hospital level (adult, P = .65, r-sq = 1.7%; peds/NICU, P = .677, r-sq = 1.5%) or at the unit level (n = 7; P = .25-.8, r-sq = 1.2%-8.5%). By contrast, a single unit that did not participate in the audit program during the sample period reported a lower CLABSI rate than comparable participating units (P = .008).

Conclusion: During the sample period, there was no relationship found between this CLABSI bundle audit program and improvement in CLABSI performance.

Background and objectives: Cataract surgery is common and low-risk. Preoperative evaluation and preoperative testing have not been shown to improve patient outcomes but do increase cost. Our process improvement aimed to reduce unnecessary preoperative primary care evaluation for cataract surgery.

Methods: We implemented a simple process involving a brief chart review and conversation with the patient to determine the appropriateness of preoperative primary care evaluations. After implementation of the screening tool, we reviewed 100 patient charts, 50 who underwent cataract surgery prior to and 50 after the intervention.

Results: The screening tool resulted in a decrease in primary care provider referrals from 100% to 4% and a decrease in primary care provider evaluation from 94% to 6%.

Conclusion: Implementation of a simple screening tool resulted in a dramatic decrease in unnecessary primary care preoperative testing.

Background and objectives: Basal cell carcinoma (BCC) is the most common form of skin cancer, originating from basal cells in the skin's outer layer. It frequently arises from prolonged exposure to ultraviolet (UV) radiation from the sun or tanning beds. Although BCC rarely metastasizes, it can cause significant local tissue damage if left untreated. Early detection is essential to prevent extensive damage and potential disfigurement. The United States Preventive Services Task Force (USPSTF) currently remains uncertain about the benefits and potential harms of routine skin cancer screenings in asymptomatic individuals. This paper evaluates the accuracy of predicting BCC using patients' medical histories to address this uncertainty and support early detection efforts.

Methods: We analyzed the medical histories of 405,608 patients, including 7733 with BCC. We categorized 25,154 diagnoses into 16 body systems based on the hierarchy in the Systematized Nomenclature of Medicine (SNOMED) ontology. For each body system, we identified the most severe condition present. Logistic Least Absolute Shrinkage and Selection Operator (LASSO) regression was then employed to predict BCC, using demographic information, body systems, and pairwise and triple combinations of body systems, as well as missing value indicators. The dataset was split into 90% for training and 10% for validation. Model performance was evaluated using McFadden's R2, Percentage Deviance Explained (PDE), and cross-validated with the area under the receiver operating characteristic curve (AUC).

Results: Diagnoses related to the Integument system showed an 8-fold higher likelihood of being associated with BCC compared to diagnoses related to other systems. Older (age from 60 to 69) white individuals were more likely to receive a BCC diagnosis. After training the model, it achieved a McFadden's R2 of 0.286, an AUC of 0.912, and a PDE of 28.390%, reflecting a high level of explained variance and prediction accuracy.

Conclusions: This study underscores the potential of LASSO Regression models to enhance early identification of BCC. Extant medical history of patients, available in electronic health records, can accurately predict the risk of BCC. Integrating such predictive models into clinical practice could significantly improve early detection and intervention.