Key Performance Indicators: A Framework for Allied Healthcare Educational Institutions.

Background: Performance evaluation in the allied healthcare education sector is complex, making it essential for policymakers and managers to approach it comprehensively and thoughtfully to understand their performance. Hence, the development and monitoring of Key Performance Indicators (KPIs) in this domain must be considered one of the key priorities for the policymakers in AHIs. Aim: This study aims to develop a framework for the AHIs to extract and profile the indicators, measure, and report the results appropriately. Methods: The authors adopted a general review of the literature approach to study the primary goals of the institutional KPI framework, emphasizing the need for benchmarking while implementing KPIs and how to track performance using a KPI dashboard. Results: The study provides the scope, relevant KPI categories, and a list of KPIs for evaluating the effectiveness of allied healthcare programs. The study findings also emphasized the need for benchmarking the KPIs and establishing a KPI dashboard while measuring and monitoring performance. Conclusion: KPIs are considered an invaluable tool that contributes immensely to the performance monitoring process of AHIs, irrespective of the specialties. This helps to identify and guide AHIs for developing KPIs and the associated minimum data set to measure organizational performance and monitor the quality of teaching and learning. In addition, the KPI framework reported in this study is a tool to assist performance monitoring that can subsequently contribute to the overall quality of AHIs.

Key performance indicators for pharmaceutical services: A systematic review.

Background: Key performance indicators (KPIs) are a set of indicators that improve the quality of services provided by pharmacists. They enable the monitoring and evaluation of result progress and optimize decision-making for stakeholders. Currently, there is no systematic review regarding KPIs for pharmaceutical services. Objectives: To identify and assess the quality of KPIs developed for pharmaceutical services. Methods: A systematic review was conducted in PubMed, Scopus, EMBASE, and LILACS from the inception of the database until February 5th, 2024. Studies that developed a set of KPIs for pharmaceutical services were included. The indicators were evaluated using the Appraisal of Indicators through Research and Evaluation (AIRE) instrument. Two independent reviewers performed the study selection, data extraction, and quality assessment. Results: Fifteen studies were included. The studies were conducted in different regions, most of which were developed for clinical services in hospitals or ambulatory settings, and used similar domains for the development of KPIs such as medication review, patient safety, and patient counseling. Literature review combined with the Delphi technique was the method most used by the studies, with content validity by inter-rater agreement. Regarding methodological quality, most studies described information on the purpose, definition, and stakeholders' involvement in the set of KPIs. However, little information was observed on the strategy for risk adjustment, instructions for presenting and interpreting the indicator results, the detailed description of the numerator and denominator, evidence scientific, and the feasibility of the set of KPIs. Only one study achieved a high methodological quality in all domains of the AIRE tool. Conclusion: Our findings showed the potential of KPIs to monitor and assess pharmacy practice quality. Future studies should expand KPIs for other settings, explore validity evidence of the existing KPIs, provide detailed descriptions of evidence, formulation, and usage, and test their feasibility in daily practice.

A comparative study of key quality performance indicators in anesthesia and surgery in operation theatre at a tertiary care hospital in Rishikesh.

Background and Aims: A Key Performance Indicator (KPI) is a critical metric measuring organizational success or specific activities, reflecting the periodic achievement of operational goals aligned with strategic objectives. Material and Methods: A retrospective cross-sectional study of key quality performance indicators, (CQI-NABH) concerning anesthesia and surgical services was conducted at a tertiary care hospital after taking institutional ethical clearance (AIIMS/IEC/21/150). It was as per COP 13, COP 14, and COP 15 standards of NABH 4th edition published in 2015. The study was conducted at the department of anesthesiology at a tertiary care hospital, and data were retrospectively analyzed from 01 October 2019, till November 2020. All patients undergoing elective or emergency surgical procedures under monitored anesthesia care and regional or general anesthesia during the study period were considered.The primary objective was to analyze Continuous Quality Improvement (CQI) in perioperative services, with secondary goals including gap identification, suggesting corrective actions, and examining quality indicators during the COVID-19 pandemic in 2020. Results: Out of 8574 patients operated during the study, 6705 were in the four months before the pandemic, and 1869 were operated during the COVID-19 scenario. In the pre-COVID era, many of the cases were performed on an elective basis (71.23%), and emergencies constituted 16.9% of the total number of cases. In the COVID era, most of the cases were emergencies (45.4%) and semi-emergencies (40.25%), and elective cases were only 14.05% of the total cases performed. The percentage of modification of anaesthesia plans ranged from 0-3.34%. Adverse anaesthesia events were observed in 0-2.1%, primarily related to cardiovascular, respiratory, and airway issues. No anaesthesia-related mortality was reported. Prophylactic antibiotic administration within one hour of surgery ranged from 88-100%. Conclusions: Monitoring and evaluating healthcare performance, specifically through KPIs, is vital for optimizing care and resource utilization. These indicators provide an overview of hospital service efficiency, aiding in key areas such as patient treatment, satisfaction, healthcare quality improvement, cost reduction, and optimal resource utilization. Addressing identified issues ensures effective and sustainable quality improvement in anaesthesia services, emphasizing ongoing monitoring as a foundation for hospital quality assurance.

An evaluation of the management of severe open tibial fractures in the United Kingdom's major trauma centres.

AIM: To evaluate the management of patients with severe open tibial fractures at major trauma centres (MTCs) in the UK with respect to BOAST 4 guidelines. METHODS: Data collected by the Trauma and Audit Research Network (TARN) for all severe open tibial fractures treated at the 23 adult MTCs were evaluated. Key performance indicators (KPIs) included MTC admission under orthoplastics, administration of antibiotics within three hours of injury, initial debridement within 24 h and definitive fixation and soft tissue coverage within 72 h. Outcomes included 30-day infection rate, amputation rate and mortality rate. A score was calculated according to overall attainment of KPIs, and correlated to outcomes. RESULTS: From 2014 to 2020, 3359 adults with Gustilo-Anderson (GA) IIIB and/or IIIC fractures were admitted to MTCs. Male to female ratio was 2:1 with a mean age of 43 and 65 years respectively. There was a negative correlation between KPI score and mortality rate (r=-0.4929, p = 0.0169). Direct admission to an MTC was positively correlated with receipt of antibiotics within three hours (r = 0.5452, p = 0.0070). Joint orthoplastic plans were documented in 89 % of patients (MTC range 30-95 %). Soft tissue cover was achieved within 72 h for 48 % (MTC range 5.23-89.39 %). Patients over 65 were significantly more likely to have a delay to MTC admission and prophylactic antibiotic administration. Mortality rate in this group was 6% vs 2 % in those under 65. The older cohort were twice as likely to require an amputation. CONCLUSION: This is the largest cohort of open tibial injuries managed in the UK with wide variation in practice between centres demonstrated and better adherence to BOAST guidelines linked to reduced mortality in those aged 65 and over. The older cohort of patients also had higher rates of infection and amputation. It is unclear whether these poor outcomes are due to the pre-morbid physiological status of the patient or non-compliance to Standards. We present these data to highlight the need for improved adherence to Standards - the adoption of a scoring system provides a simple way to evidence this.

Identifying potential predictable indicators for the management of tertiary hospitals.

BACKGROUND: The European University Hospitals Alliance (EUHA) recognises the need to move from the classical approach of measuring key performance indicators (KPIs) to an anticipative approach based on predictable indicators to take decisions (Key Decision Indicators, KDIs). It might help managers to anticipate poor results before they occur to prevent or correct them early. OBJECTIVE: This paper aims to identify potential KDIs and to prioritize those most relevant for high complexity hospitals. METHODS: A narrative review was performed to identify KPIs with the potential to become KDIs. Then, two surveys were conducted with EUHA hospital managers (n = 51) to assess potential KDIs according to their relevance for decision-making (Value) and their availability and effort required to be predicted (Feasibility). Potential KDIs are prioritized for testing as predictable indicators and developing in the short term if they were classified as highly Value and Feasible. RESULTS: The narrative review identified 45 potential KDIs out of 153 indicators and 11 were prioritized. Of nine EUHA hospitals, 25 members from seven answered, prioritizing KDIs related to the emergency department (ED), hospitalisation and surgical processes (n = 8), infrastructure and resources (n = 2) and health outcomes and quality (n = 1). The highest scores in this group were for those related to ED. The results were homogeneous among the different hospitals. CONCLUSIONS: Potential KDIs related to care processes and hospital patient flow was the most prioritized ones to test as being predictable. KDIs represent a new approach to decision-making, whose potential to be predicted could impact the planning and management of hospital resources and, therefore, healthcare quality.

Optimizing the Relationship Between Perioperative Personnel and Infection Preventionists to Reduce Surgical Site Infections.

Prevention of surgical site infections (SSIs) is a critical aspect of ensuring positive patient outcomes. One of the challenges of SSI prevention is the communication barrier between perioperative staff members and infection preventionists (IPs), which may lead to frontline staff members who are primarily responsible for infection prevention being unaware of pertinent hospital SSI data. To overcome this challenge, IPs and perioperative staff members should develop a partnership that facilitates the sharing of feedback on SSI case review data and effective key performance indicators. A partnership also can help engage perioperative staff members in quality improvement efforts and increase collaboration with IPs. Perioperative leaders should identify effective methods to improve data transparency, SSI case reviews, audit and feedback programs, and education for perioperative team members. A strong perioperative-IP partnership and increased sharing of data in accessible formats may improve engagement and interest in SSI prevention.

Prediction of defensive success in elite soccer using machine learning - Tactical analysis of defensive play using tracking data and explainable AI.

The interest in sports performance analysis is rising and tracking data holds high potential for game analysis in team sports due to its accuracy and informative content. Together with machine learning approaches one can obtain deeper and more objective insights into the performance structure. In soccer, the analysis of the defense was neglected in comparison to the offense. Therefore, the aim of this study is to predict ball gains in defense using tracking data to identify tactical variables that drive defensive success. We evaluated tracking data of 153 games of German Bundesliga season 2020/21. With it, we derived player (defensive pressure, distance to the ball, & velocity) and team metrics (inter-line distances, numerical superiority, surface area, & spread) each containing a tactical idea. Afterwards, we trained supervised machine learning classifiers (logistic regression, XGBoost, & Random Forest Classifier) to predict successful (ball gain) vs. unsuccessful defensive plays (no ball gain). The expert-reduction-model (Random Forest Classifier with 16 features) showed the best and satisfying prediction performance (F1-Score (test) = 0.57). Analyzing the most important input features of this model, we are able to identify tactical principles of defensive play that appear to be related to gaining the ball: press the ball leading player, create numerical superiority in areas close to the ball (press short pass options), compact organization of defending team. Those principles are highly interesting for practitioners to gain valuable insights in the tactical behavior of soccer players that may be related to the success of defensive play.

Assessment of the Key Performance Indicator Proposed by NABH in the Blood Centre of a Tertiary Health Care Hospital in Southern India.

Quality indicators are tools for continuous improvement to enable the blood center to achieve its standards of the highest quality. Hence, they have to be established and monitored regularly for which NABH (National Accreditation Board for Hospitals) accreditation should be sought for. This study was undertaken to assess the Key Performance Indicators (KPI) through clinical audit quality control study of ten parameters, with a goal to improve and meet the benchmark as defined by NABH. All 10 Key Performance Indicators defined by NABH were analysed prospectively in a tertiary care blood centre of southern India. Parameters were compared to that of bench mark standards. Root cause analysis of all non-conformance parameters were done. Problem were identified and action taken to achieve KPI benchmarks in all deviations. Out of the ten KPI's which were studied, more than 50% meet the quality standards. The ones that did not meet the bench mark were TTI-HIV% which was 0.44%, TTI-Syphilis (RPR)% 0.26%, Number of units received back for discarding 5.96%, PRBC wastage% (on-shelf) was 2.11%, FFP, Cryoprecipitate wastage % (on-shelf) was 2.71%, the mean TAT for crossmatch of emergency PRBC blood was 18.3 min, 41.11% of FFP QC failure failed, Delay in transfusion time beyond 30 min after issue was 19.14%, Donor Deferral rate was 16.36% and TTI Outliers% No. of deviations beyond ± 2SD for HBsAg, HCV, HIV were 14.43%, 12.59% and17.73% respectively. Present study has helped to understand the flaws and problems faced by a tertiary care blood center in sustaining quality. It also actively captured and analysed multiple cross sections of non-conformances.

Key nitrogen and phosphorus performance indicators derived from farm-gate mass balances on dairies.

Efficient management of N and P on dairy farms is critical for farm profitability and environmental stewardship. Annual farm-gate nutrient mass balance (NMB) assessments can be used to determine the nutrient-use efficiency of farms, set efficiency targets, and monitor the effect of management changes with minimal inputs required. In New York, feasible N and P balances have been developed as benchmarks for dairy farm NMB, alongside key performance indicators (KPI) that serve as predictors for high NMB. Here, 3 yr of NMB data from 47 farms were used to evaluate the main drivers of N and P balances and identify additional KPI. From the 141 farm records, 26% met both the feasible N balances per hectare and per megagram of milk produced. For P, 53% of the records met both benchmarks. Imports, rather than exports, drove NMB primarily by feed and fertilizer purchases, consistent with earlier findings. Linear regression analysis showed that a selection of KPI currently used, particularly animal density, nutrient-use efficiency, and the amount of home-grown feed, explained a large portion of variation in NMB. Heifer-to-cow ratio and the relative proportion of various forage crops may provide further insight into the drivers of feed and fertilizer imports and ultimately farm-gate NMB. This study provides avenues toward a better assessment of whole-farm nutrient management and means for farms to communicate progress to stakeholders and consumers.

Small pharma vendor management practices in clinical trials: A case study within Faron Pharmaceuticals.

Many small pharmaceutical companies find that they lack the resources, knowledge and expertise of the regulatory landscape for adequate vendor management in clinical trials, making the organization vulnerable. Recent research suggests that some pharmaceutical companies have found themselves out of compliance with ICH, FDA or EMA guidelines. This paper aims to perform a comprehensive review of the regulatory landscape for vendor selection, oversight and ongoing evaluation in clinical trials. In addition, the case study performed studies the practices recently implemented at small pharmaceutical company Faron Pharmaceuticals to assess regulatory compliance and identify any potential best practices. Faron Pharmaceuticals conducted a process improvement activity at the beginning of 2022 to improve the vendor selection, oversight and evaluation of their clinical trial partners. The results of this case study indicate that Faron Pharmaceuticals' processes are regulatory compliant, suggesting that QTLs, KPIs, SOPs and communication plans are effective vendor oversight mechanisms for small pharmaceutical companies to utilize.

"In the Wild" Video Content as a Special Case of User Generated Content and a System for Its Recognition.

In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality that is practically indistinguishable from professional content, although at the beginning of UGC creation, this content was frequently characterized by amateur acquisition conditions and unprofessional processing. In this research, we focus only on UGC content, whose quality is obviously different from that of professional content. For the purpose of this paper, we refer to "in the wild" as a closely related idea to the general idea of UGC, which is its particular case. Studies on UGC recognition are scarce. According to research in the literature, there are currently no real operational algorithms that distinguish UGC content from other content. In this study, we demonstrate that the XGBoost machine learning algorithm (Extreme Gradient Boosting) can be used to develop a novel objective "in the wild" video content recognition model. The final model is trained and tested using video sequence databases with professional content and "in the wild" content. We have achieved a 0.916 accuracy value for our model. Due to the comparatively high accuracy of the model operation, a free version of its implementation is made accessible to the research community. It is provided via an easy-to-use Python package installable with Pip Installs Packages (pip).

Low-Cost UWB Based Real-Time Locating System: Development, Lab Test, Industrial Implementation and Economic Assessment.

This paper presents the technical development and subsequent testing of a Real-Time Locating System based on Ultra-Wideband signals, with the aim to appraise its potential implementation in a real industrial case. The system relies on a commercial Radio Indoor Positioning System, called Qorvo MDEK1001, which makes use of UWB RF technology to determine the position of RF-tags placed on an item of interest, which in turn is located in an area covered by specific fixed antennas (anchors). Testing sessions were carried out both in an Italian laboratory and in a real industrial environment, to determine the best configurations according to some selected performance indicators. The results support the adoption of the proposed solution in industrial environments to track assets and work in progress. Moreover, most importantly, the solution developed is cheap in nature: indeed, normally tracking solutions involve a huge investment, quite often not affordable above all by small-, medium- and micro-sized enterprises. The proposed low-cost solution instead, as demonstrated by the economic assessment completing the work, justifies the feasibility of the investment. Hence, results of this paper ultimately constitute a guidance for those practitioners who intend to adopt a similar system in their business.

Impact of Implementing Key Performance Indicators on Catheter-Associated Urinary Tract Infection (CAUTI) Rates Among Adult ICU Patients in Saudi Arabia.

Background: The prevalence of catheter-associated urinary tract infections (CAUTIs) in hospitals characterizes one of the most significant problems in healthcare. This study aims to assess whether the implementation of impact of key performance indicators (KPIs) checklist reduces the number of CAUTI in adults present in intensive care unit (ICU) with indwelling catheters. Methods: This is a retrospective analytical study conducted in a tertiary hospital in Riyadh, Saudi Arabia, from June 2020 to June 2021. One hundred and thirty-four patients with CAUTIs met the criteria and were included in the study. Socio-demographic data was collected to enable informed analysis based on personal information (age, gender, marital status, monthly income, level of education, and department) and medical history (duration of catheterization, types of organisms, history of chronic illness, and duration of hospitalization). The research also used a prevention of CAUTI checklist containing 26 items. The outcome measures were 1) the rate of CAUTIs measured pre- and post-implementing performance measurement indicators (KPI) of CAUTIs prevention practice and 2) the prevention of catheter-associated urinary tract infection in three areas: general information recording, insertion practices, and maintenance practices. Results: The study found that there was compliance with the prevention of CAUTIs in terms of recording the general patient's information (72%), insertion practices (52%), and maintenance practices (50%). However, most safety practices, including poor hygiene and safety standards, patient handling, and audited protocol programs, were not strictly followed, resulting in increased risk factors for CAUTIs. Conclusion: Compliance with the prevention of CAUTIs in terms of recording the general patient's information, insertion practices, and maintenance practices lies within the range of 50-75%, and the recommended practices are usually followed. A targeted education on CAUTI-prevention practices curtailing the most aggravating risk factors and adopting a safety culture driven by a patient handling and audited protocol program should be explored to reduce hospital CAUTIs.

Comprehensive Environmental Assessment Index of Ecological Footprint.

This paper aims to contribute to the growing body of research literature on assessing environmental efficiency by introducing a new key performance indicator (KPIs) in more complete and dependable aspects of ecological footprint indices. For this purpose, the DEA model considering three inputs (energy consumption, labor force, and capital stock), one desirable output (GDP), and different undesirable outputs (CO2 emissions, ecological footprint indicators) are applied to 27 OECD countries from 2000 to 2017. According to the results, Norway, Luxemburg, and United Kingdom are the most environmentally efficient countries in terms of environmental efficiency and ecological footprint efficiency. On the other hand, the lowest environmental and ecological footprint efficiencies were in countries like Lithuania, Slovak, Czech, Estonia, and the USA. In addition, these nations fare poorly regarding their carbon footprint and farmland efficiency. In further detail, Lithuania, South Korea, Portugal, and Spain have a critical status in fishing ground efficiency, while the forest area efficiency is very acute in Estonia, Latvia, Lithuania, and Czech.

Force plate vertical jump scans are not a valid proxy for physical fitness in US special warfare trainees.

Background: The United States Air Force Special Warfare Training Wing (SWTW) administers a comprehensive physical fitness test to active duty Airmen entering the Special Warfare training pipeline. The Sparta Science™ system utilizes proprietary software to analyze the force-time curve of a vertical jump and purports to serve as a proxy for traditional military fitness tests. The Sparta Science™ system produces four proprietary metrics, including the Sparta™ Score, which is correlated to high magnitudes of force production purportedly performance. This study investigated how Sparta™ Jump Scans correlate to components of a physical fitness test utilized within the SW training pipeline. Methods: At the entry and exit of an 8-week Special Warfare Training Wing preparatory course (SW PREP), 643 trainees completed both an initial and final Sparta™ Jump Scan and a Candidate Fitness Test (CFT). The Candidate Fitness Test consists of eight components and tests several different domains of fitness including strength, power, muscular endurance, swimming proficiency, and cardiovascular fitness. Paired t-tests were used to determine if Sparta™ Jump Scan metrics and CFT components changed during SW PREP. Sparta™ Score's correlation was assessed against every other Sparta™ Jump Scan metric and all CFT fitness measures. Results: This study found that the Sparta™ Jump Scan metrics decline slightly over SW PREP (p < 0.05; negligible-small effect size), while most CFT measures improve (p < 0.05; small-medium effect size). Changes in Sparta™ Jump Scan metrics did not reflect the changes in CFT performance over SW PREP (r 2: 0.00-0.03). Conclusion: The Sparta™ Score was not correlated to the most tactically-relevant fitness measures (rucking and swimming), and only weakly correlated with the only jumping measure on the fitness test, the standing broad jump.

DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection.

To ensure the normal operation of the system, the enterprise's operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long-short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.

Intelligently driven performance management: an enabler of real-time research forecasting for innovative commercial agriculture.

Research in commercial agriculture is instrumental to achieve the targets set under the second Sustainable Development Goal (SDG), i.e., 'Zero Hunger by 2030.' Execution of research for the success of commercial agriculture becomes a tedious task and research organizations have been long struggling to assess their performance unequivocally in the face of COVID-19. Any evacuation plan in place to improve the performance, monitoring, and evaluation of a research institute must guarantee that the institute is on the right momentum and let it evades metrics-obsessed research drives during this pandemic. A survey was conducted through the participation of the topmost administrators attached to key research institutes working on agriculture in Sri Lanka to explore the current performance management practices deeply. The conclusions derived from a thematic analysis of the survey data were used to propose a set of solutions that facilitate a well-thought research agenda in a digitally transformed performance management system. The solutions imply that intelligently driven key performance measurements worked by artificial intelligence and big data could be used with policy innovations to support research integrity and assessment security within the coexistence of humans and machines for the well-being of research development in the commercial agriculture sector. Supplementary Information: The online version contains supplementary material available at 10.1007/s43545-022-00484-8.

Sustainability reports as a tool for measuring and monitoring the transition towards the circular economy of organisations: Proposal of indicators and metrics.

Sustainability reports may play an important role as a supporting tool in the transition of organisations towards more circular economy models, since their content can help to measure, monitor and communicate the organisations' transition and to establish goals in the short/medium term. The aim of this study is to determine whether it is possible to calculate indicators capable of measuring the transition of organisations towards circularity from the information that they are currently communicating in their Corporate Sustainability Reports (CSRs), and what information would need to be incorporated in these reports to successfully carry out this procedure. To this end, by applying a three-step methodology, 34 indicators grouped into 10 categories were proposed to measure the level of circularity of organisations. This was completed with a detailed proposal of units/metrics to measure the indicators, based on those that organisations commonly use in their CSRs. For this purpose, information from 8 international programmes/frameworks that measure circularity at the territorial level was combined with circularity information that organisations are currently communicating in their CSRs. Finally, the proposed set of indicators and metrics were applied to a Spanish organisation dedicated to the forestry and paper sector with a CSR based on GRI-Standards. The results demonstrated that 25 of the 34 proposed indicators (74%) can be measured directly using the information included in the CSRs.

The Perioperative Steering Committee as an Accountable Infrastructure to Enable and Sustain Change.

Leaders in perioperative and interventional procedure areas need to be able to initiate and sustain change to improve operational processes in their departments or service lines. Although available literature discusses change in health care organizations, there is a lack of published articles on the implementation and sustainment of change. This article provides a review of supporting literature on change management and an infrastructure model that we have successfully implemented to sustain change. An organizational case study focused on creating sustained improvements for first procedure on-time starts and turnover times details the process of creating the accountability system for actualizing the performance targets in a perioperative environment. The case study examines the existing process and initial challenges with creating sustainable and quantifiable outcomes, describes the process of implementing the infrastructure discussed in the article, and evaluates the results. Perioperative leaders can use the information to improve processes in their work environments.

Updating analysis of key performance indicators of 4G LTE network with the prediction of missing values of critical network parameters based on experimental data from a dense urban environment.

In practice, field measurements often show missing data due to several dynamic factors. However, the complete data about a given environment is key to characterizing the radio features of the terrain for a high quality of service. In order to address this problem, field data were collected from a dense urban environment, and the missing parameters were predicted using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) algorithm. The field measurement was taken around Victoria Island and Ikoyi in Lagos, Nigeria. The test equipment comprises a Global Positioning System (GPS) and a Fourth Generation (4G) Long Term Evolution (LTE) modem equipped with a 2×2 MIMO antenna, employing 64 Quadrature Amplitude Modulation (QAM). The Modem was installed on a personal computer and assembled inside a test vehicle driven at a near-constant speed of 30 km/h to minimize possible Doppler effects. Specifically, the test equipment records 67 LTE parameters at 1 s intervals, including the time and coordinates of the mobile station. Thirty-two parameters were logged at 42,498 instances corresponding to 11 h, 48 min and 18 s of data logging on the mobile terminal. Sixteen important 4G LTE parameters were extracted and analyzed. The statistical errors were calculated when the missing values were exempted from the analyses and when the missing values were incorporated using the PCHIP algorithm. In particular, this update paper estimated the missing values of critical network parameters using the PCHIP algorithm, which was not covered in the original article. Also, the error statistics between the data (histograms) and the corresponding probability density function curves for the measured data with missing values and the data filled with the missing values using the PCHIP algorithm are derived. Additionally, the accuracy of the PCHIP algorithm was analysed using standard statistical error analysis. More network parameters have been tested in the update article than in the original article, presenting only basic statistics and fewer network parameters. Overall, results indicate that only the parameters which measure the throughput values follow the half-normal distribution while others follow the normal distribution.