Quality Management in Health Care最新文献

Background: The United States continues to have the highest rates of maternal mortality and morbidity of all high-income countries. Added to this is the high rate of unintended pregnancy, making it clear that reproductive health services in this country are still deficient. National health care organizations recommend prioritizing reproductive health in primary care.

Methods: Using the knowledge-to-action framework, this quality improvement project applied evidence-based practice regarding preconception and contraceptive services to primary care. King's Goal Attainment Theory was used to disrupt the usual routine and create a culture promoting reproductive health conversations with patients. Staff received education on reproductive health in the primary care setting, leading to the development of a new process to screen for pregnancy intent. Providers were guided on the use of shared decision-making tools to discuss reproductive services and to provide preconception care and the full range of contraceptive methods.

Results: Over 40% of female patients seen received unexpected yet desired reproductive health services. Providers and staff reported that the implementation of the process was feasible and sustainable. A new culture emerged in which reproductive health inquiry became usual care.

Conclusions: Including reproductive health inquiry as part of the primary care visit was an easy intervention to implement that led to success in bridging the gap to reproductive health in primary care without placing undue burden on providers or staff.

Background and objectives: This research aims to examine the current level of soft and hard Total Quality Management (TQM) at Shahroud University of Medical Sciences and explore the factors contributing to its success.

Methods: The present cross-sectional study included 462 university managers and employees selected by stratified random sampling method in 2022. The participants were asked to answer TQM, soft TQM, and hard TQM questionnaires. The data were analyzed using ANOVA and chi-square tests, Pearson's correlation coefficient, and structural equation modeling.

Results: Managers made up 73 of the participants (15.8%). The average score for TQM was 143.02 ± 11.56, soft TQM 52.47 ± 9.35, and hard TQM 36.49 ± 9.54. There was no statistically significant variation in the mean score of hard TQM, soft TQM, and TQM based on gender or course completion. There was a strong and positive link between the scores of soft TQM, hard TQM, and TQM. Leadership commitment, employee suggestion systems, problem-solving groups, information quality, and preventive maintenance were strongly associated with quality management in the structural equation model.

Conclusion: Given the university's average level of TQM implementation, it appears that steps should be taken to improve the evaluation process and provide feedback to employees. Steps should also be taken toward improving the commitment of senior management to implementing and enhancing the system of employee suggestions, establishing problem-solving groups, and training job duties. TQM can assist employees and increase the quality of the information to improve the university's rank in quality management.

Objective: To explore the effect of a family-centered care (FCC) model on clinical efficacy, quality of life, and the emotional behavior of children with acute otitis media (AOM) in an otolaryngology clinic.

Methods: Data were collected from a hospital otolaryngology clinic in relation to 112 cases of children with AOM who met the inclusion criteria. These cases were randomly divided into 2 groups of 62 patients each. The control group was given routine nursing for AOM, whereas the intervention group was provided with FCC nursing. The general data, periosteal congestion and ear pain scores, effective rate, and treatment time for the 2 groups of children and their caregivers were compared. The children's quality of life and strengths and difficulties scale as well as the caregivers' nursing skills scores before and after the intervention were compared between the groups.

Results: The general data from the 2 groups of children and caregivers were comparable. After the intervention, the scores for tympanic membrane congestion and ear pain in the intervention group were significantly lower than those of the control group. Moreover, the intervention group's effective rate was higher, and the treatment time shorter than those of the control group (P < .05). The intervention group's scores on quality of life, emotional symptoms, hyperactivity and attention deficit behavior, and conduct and peer interaction problems were higher than those of the control group (P < .05). The intervention group caregivers' scores for nasal and ear drip methods and bed shampoo skills were higher than those of the control group after the intervention.

Conclusions: The application of the FCC model in the nursing management of children with AOM can effectively shorten treatment time and improve clinical efficacy, mood and behavior, and the level of caregivers' nursing skills.

Background and objectives: The Transforming Clinical Practices Initiative (TCPI) was a 4-year, large-scale, collaborative, peer-based learning initiative for physician practices of all specialties with broad goals to improve the quality of patient care, spend health care dollars more wisely, and assist practices in being ready to succeed under value-based payment (VBP). We investigated whether the COVID-19 pandemic had erased or diminished practice transformation progress made during the TCPI program period, through a follow-up survey of participating practices fielded in October 2021.

Methods: In October 2021 to April 2022, we surveyed a probability sample of 2207 primary care and specialty practices that participated in the TCPI, receiving 610 responses. We asked about practice characteristics, financial and ownership stability, clinical performance, and quality improvement efforts, both now and prior to COVID-19. The COVID-19 content was part of a larger survey. Responses were weighted to account for sample selection, unknown eligibility status, and nonresponse. We generated weighted univariate descriptive statistics representative of practices with clinicians enrolled in TCPI. These estimated percentages have a 95% confidence interval of about ±5%. Multivariate analysis of unweighted data examined associations between practice characteristics and other variables of interest.

Results: For all but one of 13 practice transformation activities engaged in prior to the COVID-19 public health emergency, a majority of practices (at least 52%) reported that the progress on these activities were either not hurt or were helped by their COVID-19 experience. Compared to January 2020, only about 7% of practices reported that their quality of care or clinical performance was worse due to COVID-19, and 32% reported that their quality of care was better. More rural than urban practices reported that half or more of their transformation activities were hurt by COVID-19 (29% and 14%, respectively). Physician-owned practices were more likely to report quality is better today than prior to COVID-19 relative to practices with other ownership types such as hospitals or health systems (43% vs 24%).

Conclusions: Most practices have been able to recover from the deep stress of the COVID-19 pandemic and continue their efforts to improve patient care and performance to succeed under VBP. These results suggest a high perceived return on investment in value-readiness support along with emergency financial support in times of crisis, as well as room to continue preparation for any future pandemic and the national movement toward increased VBP, especially in rural settings.

Background and objectives: To establish whether posters displayed across the walls on hospital wards are read, what information is important, and how the information should be received.

Methods: Sixty-eight staff and 32 patients' relatives were interviewed across 3 older people's medical wards followed by 20 follow-up secondary questionnaires postintervention.

Results: Only 23% of those interviewed were able to recall any of the posters displayed, and of those, 34% did not find the information useful. Those interviewed were enthusiastic about utilizing alternative media. A quarter felt the walls across the hospitals wards should be for artwork. Among patients' relatives interviewed, common information requests were "the discharge pathway," "delirium," and "falls." Based on the initial findings, a targeted information board was installed and a mural was painted across the wall in one of the wards. Further post-intervention interviews with patients' relatives showed that the board was well received, but further unmet information needs were uncovered. Despite the new mural, 45% called for more paintings.

Conclusions: Most people ignore the posters displayed across the walls of hospital wards, and unmet information needs are rife. An appetite exists for alternative media. Paintings were earnestly called for, highlighting how a comforting environment could be part of the holistic care we offer patients in hospital.

Background and objectives: Managers in health care today face an array of digital technologies that assist or augment certain human tasks. But these technologies are often fraught and present challenges to managers, whose competencies must evolve to keep pace with technological advancements.

Methods: Drawing on theory about technology, work, and organizations, we present a human-technology continuum to facilitate this discussion for managers. Furthermore, we illustrate how managerial competencies are linked to the entire human-technology continuum, rather than to specific technologies, using diabetes management examples.

Results: The human-technology continuum indicates that augmentative technologies are layered onto assistive ones in health care settings. This suggests that technological advancements not only enhance but alter managerial competencies.

Conclusions: Digital technology stretches the boundaries of managers' day-to-day work in health care. Therefore, we make the following suggestions so the managers can be responsive to ongoing digital transformations: restructuring work, training the workforce, neutralizing threats, establishing ethical boundaries, and building partnerships.

Background and objectives: The U.S. Preventive Services Task Force (USPSTF) does not currently recommend routine screening for kidney cancer, even though approximately 14 390 people are expected to die from this disease in the United States in 2024. Individualized risk-based kidney cancer screening offers the potential to effectively detect cancer at an early stage and avoid unnecessarily screening the rest of the population who are at low risk. This study proposes electronic health records (EHR) risk evaluation for kidney cancer by examining a comprehensive set of medical history including diagnoses, comorbidities, viruses, and rare diseases.

Methods: The relevant medical history for predicting kidney cancer occurrence was identified from the analysis of All of Us data in three steps. First, a Systematized Nomenclature of Medicine (SNOMED) code binary indicator variable in EHR was set for the presence of kidney cancer. Second, the relationship between this binary indicator of cancer and all prior health conditions was examined using the Strong Rule for Feature Elimination and Least Absolute Shrinkage and Selection Operator logistic regression methods of variable selection. Third, the accuracy of the model was reported using cross-validated McFadden's R2 and Area under the Receiver Operating Characteristic curve (AROC) values.

Results: The analysis identified 133 out of an initial set of 25 683 clinical diagnoses (represented by SNOMED codes) that were predictive of kidney cancer. The model achieved a cross-validated McFadden's R2 of 0.195 and an AROC of 0.799. Most of the identified codes are consistent with the known risk factors for kidney cancer.

Conclusions: It is possible to accurately predict the risk of kidney cancer from medical history using this method. Additional studies to establish high-dimensional predictive risk factors are needed to see if EHR personalized risk prediction can lead to cost-effective cancer screening and eventually better clinical outcomes.

Background and objectives: Women of reproductive age with substance use (SU) disorders have lower rates of contraceptive use and higher rates of unintended pregnancy than women without SU disorders and are less likely to access treatment than men. Integration of SU and sexual and reproductive health (SRH) services, using a model known as Screening, Brief Intervention, and Referral to Treatment (SBIRT), has been proven effective in reducing SU and improving health care equity. The SBIRT model includes screening, brief intervention (a short client-centered conversation providing an opportunity to identify/discuss concerns), and referral to treatment. The purpose of this study was to test whether an established quality improvement (QI) learning collaborative model could be used to support SU and SRH sites in implementing an SBIRT/SBIRT-like model to improve health outcomes for women. Five SRH sites and 4 SU sites across New York State participated in the Partnership to Advance Integrated Referrals (PAIR), an 18-month QI learning collaborative designed and implemented by Public Health Solutions.

Methods: Six standardized mixed-methods data collection tools were used over 18 months to gather process and outcome data from over 130 QI team members and site staff and over 5000 clients.

Results: By the end of PAIR, QI team members and site staff showed a reduction in bias, increased knowledge and comfort, increased rating of organizational practices related to client-centered care, and increased access to peer learning, information about best practices, and training and technical assistance. SU sites increased SRH screening from 47.9% in the first quarter of data collection to 67.4% in the final quarter and increased brief interventions from 92.5% in the first quarter to 100.0% in the final quarter. Similarly, SRH sites increased SU screening from 51.6% to 75.6% and increased brief interventions from 81.3% to 85.1%. The processes and outcomes were very different for the SU and SRH sites, and their varying successes and challenges are discussed. Making and verifying referrals remained challenging.

Conclusions: The results of PAIR demonstrated the feasibility of SU and SRH sites implementing an SBIRT/SBIRT-like model when supported by a QI learning collaborative. Larger community and organizational challenges (COVID-19, staff turnover) still present barriers to improved reproductive health and SU outcomes for women.

Background and objectives: Malignant brain and other central nervous system tumors (MBT) are the second leading cause of cancer death among males aged 39 years and younger, and the leading cause of cancer death among males and females younger than 20. There are few widely accepted predictors and a lack of United States Preventive Services Taskforce recommendations for MBT. This study examined how medical history could be used to assess the risk of MBT.

Methods: Using over 400,000 patients' medical histories, including nearly 1,800 with MBT, Logistic Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to predict MBT. More than 25,000 diagnoses were grouped into 16 body systems, plus pairwise and triple combinations, as well as indicators for missing values. Data were split into 80/20 training and validation sets with fit and accuracy assessed using McFadden's R2 and the area under the receiver operating characteristic curve (AUC).

Results: Diagnoses of the endocrine, nervous, and lymphatic systems consistently showed greater than three times more association with MBT. The best performing model at an AUC of 0.83 consisted of 14 body system diagnosis groups and pairwise interactions among groups, in addition to demographic, social determinant of health, death, and six missing diagnosis grouping indicators.

Conclusions: This study demonstrated how large data models can predict MBT in patients using EHR data. With the lack of preventive screening guidelines and known risk factors associated with MBT, predictive models provide a universal, non-invasive, and inexpensive method of identifying at-risk patients.