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A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks.

ABSTRACT: Most of the healthcare facilities (HFs) have to face the nosocomial infections (NIs), which increase the rates of morbidity, mortality, and financial burden on the HFs and the patients. The control of the NIs is a global issue and requires additional effort. Because the pathogenic microbes can be transmitted among all the HF departments, the layout and design of the HFs (or the department configuration) is considered to play a significant role in control of the NIs. A few of the departments transmit the microbes more than other departments, called 'cause', while some other departments are more infected than others, called 'effect'. Here, the researchers have stated that both the cause and effect departments are risky. This research tried to propose a comprehensive mathematical algorithm for choosing the high-risk department(s) by applying the NI and the managerial criteria to minimize NIs through rearchitecting of the HFs. To develop the algorithm, the researchers applied the multiple criteria decision-making (MCDM) methods. They used Decision-Making Trial and Evaluation Laboratory (DEMATEL) and modified weighted sum method (WSM) methods, and their hybrid, along with a modified nominal group technique (NGT) for data collection. The proposed algorithm was later validated by implementation in a HF as a case study. Based on all results of the algorithm, the high-risk departments in the HF were identified and proposed to be eliminated from the HF in such a way that the facility would retain its functionality. The algorithm was seen to be valid, and the feasibility of the algorithm was approved by the top managers of the HF after the algorithm was implemented in the case study. In conclusion, the proposed algorithm was seen to be an effective solution for minimizing the NIs risk in every HF by eliminating the high-risk departments, which could simplify the HF manager's decisions.

SUBMITTER: Parsia Y

PROVIDER: S-EPMC7037869 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks.

Parsia Yasaman Y Sorooshian Shahryar S

International journal of environmental research and public health 20200130 3

Most of the healthcare facilities (HFs) have to face the nosocomial infections (NIs), which increase the rates of morbidity, mortality, and financial burden on the HFs and the patients. The control of the NIs is a global issue and requires additional effort. Because the pathogenic microbes can be transmitted among all the HF departments, the layout and design of the HFs (or the department configuration) is considered to play a significant role in control of the NIs. A few of the departments tran ...[more]

PMID: 32019085

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Project description:BackgroundShared decision-making, a communicative process to reach decisions based on informed preferences, evidence, and co-created goals, improves care satisfaction and patients' quality of life. However, shared decision-making has not been widely implemented in long-term care facilities, and few studies have examined how to promote the shared decision-making practice. This study aimed to identify the influencing factors of shared decision-making based on the Person-centered Practice Framework in long-term care facilities.MethodsA total of 300 staff (nursing staff, social workers, and personal care workers) in 13 Korean long-term care facilities participated in this study. Data from 280 respondents were finally analyzed, excluding respondents with missing values. Data were collected using structured questionnaires that included items on shared decision-making, personal factors (e.g., knowledge about dementia, person-centered care education, person-centered attitude, communication behavior, and job tenure), and care environment factors (e.g., person-centered climate, staffing level, effective staff relationships, supportive supervisors, and power-sharing). Multilevel linear regression analyses were performed using Mplus Version 8.8.ResultsThe mean shared decision-making score was 35.78 (range 8-45). Staff with experience of person-centered care education (β = 0.198, p = 0.034), a higher person-centered attitude score (β = 0.201, p = 0.007), and a higher communication behavior score (β = 0.242, p < 0.001) were more likely to report a higher shared decision-making score. In addition, staff who viewed their care environment as more person-centered were more likely to report a higher shared decision-making score (β = 0.416, p < 0.001).ConclusionsThis study highlights that personal (e.g., person-centered care education, person-centered attitude, and communication behavior) and care environment (e.g., person-centered climate) factors could influence shared decision-making for long-term care residents. These findings could be foundational evidence for facilitating shared decision-making practice in long-term care settings.

Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Project description:ImportanceThe population of infants, children, and adolescents cared for at pediatric long-term care facilities is increasing in complexity and size and thus consumes substantial health care resources. Infections are a significant cause of morbidity and mortality in this population, but few recent data describe their incidence and effects.ObjectivesTo describe the types of infections diagnosed in residents of pediatric long-term care facilities, calculate infection rates, and identify risk factors for respiratory tract infections (RTIs).Design, setting, and participantsThis prospective cohort study, which was part of a larger trial called Keep It Clean for Kids, was conducted from September 1, 2012, to December 31, 2015, at 3 pediatric long-term care facilities in New York. Residents of the facilities who were 21 years or younger and either residents or admitted during the study period (n = 717) were enrolled in the study. Medical records were reviewed to identify infections diagnosed by site clinicians.Main outcomes and measuresIncidence of infections, such as RTIs; skin and soft-tissue infections; chronic comorbid conditions, including neurologic and respiratory disorders; and device use, including gastrostomy tubes and tracheostomies, was determined. Risk factors for RTIs were assessed by generalized linear mixed method regression modeling.ResultsThe 717 residents had a median (interquartile range) age at enrollment of 2.6 (0.4-9.1) years; 358 (49.9%) were male. Four hundred twenty-eight residents (59.7%) had feeding tubes and 215 (30.0%) had tracheostomies. Most chronic comorbid conditions were musculoskeletal or ambulation (532 residents [74.2%]), neurologic (505 [70.4%]), respiratory (361 [50.3%]), and gastrointestinal (230 [32.1%]) disorders, and 460 residents (64.2%) had 4 or more chronic comorbid conditions. Site clinicians diagnosed 2052 infections during the 3-year study period. Respiratory tract infections were most common and were diagnosed in 1291 residents (62.9%). The overall infection rate was 5.3 infections per 1000 resident-days, and RTI rates were 3.3 infections per 1000 resident-days. Overall infection rates and rates of RTI, skin and soft-tissue infection, urinary tract infection, and bloodstream infection varied among the 3 sites. In the multivariable model, younger age (incidence rate ratio [IRR], 1.05; 95% CI, 1.03-1.06), increased number of chronic comorbid conditions (IRR, 1.12; 95% CI, 1.06-1.19), and the use of feeding tubes (IRR, 1.34; 95% CI, 1.03-1.64) and tracheostomies (IRR, 1.40; 95% CI, 1.17-1.69) were associated with RTIs.Conclusions and relevanceIn this study, RTIs were the most common infections diagnosed, but modifiable risk factors for RTIs were not identified. Future work should focus on optimizing infection prevention and control strategies to reduce infections, particularly RTIs, in the pediatric long-term care population.

Dataset Information

A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks.

Publications

A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks.

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