Pioneering digital disruption: Australia's first integrated digital tertiary hospital

Sullivan, Clair; Staib, Andrew; Ayre, Stephen; Daly, Michael; Collins, Renea; Draheim, Michael; Ashby, Richard

doi:10.5694/mja16.00476

Cited by 33 publications

(27 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the Healthcare sector, "because the digital transformation of a hospital is a clinical event, patient outcomes, rather than Information Technology (IT) project goals, need to remain the focus" [15]. For the research purposes patients have been considered as the customers of the Healthcare sector, thus the patient focus can be intended as customer focus.…”

Section: Resultsmentioning

confidence: 99%

“…In the Healthcare sector, the main focus is on patients [15]. In order to improve the quality of care processes are improved and therefore become more efficient.…”

Section: B) Healthcare and Social Assistancementioning

confidence: 99%

“…Digital hospitals features include electronic medical record (EMR) systems and other technical components such as integrated digital vital sign monitoring and digital electrocardiogram (ECG) records. As a result, the hospital has gained the ability to provide available data from the EMR to improve the quality and the efficiency of care, as rich clinical information facilitates system improvement [15]. Since the goals pursued by the hospital are coherent with the sector triggers, this can be considered as a relevant example of digital transformation in the sector.…”

Section: B) Healthcare and Social Assistancementioning

confidence: 99%

See 2 more Smart Citations

What Causes Companies to Transform Digitally? An Overview of Drivers for Australian Key Industries

Lammers

Tomidei

Regattieri

2018

2018 Portland International Conference on Management of Engineering and Technology (PICMET)

View full text Add to dashboard Cite

Business leaders and entrepreneurs are facing new challenges in the rapidly transforming digital economy. The benefits obtained by the employment of digital technologies are broadly acknowledged. However, decisions need to be made about which technologies to acquire and how to integrate them into the business. In order to do this efficiently, organizations and disruptors all over the world need to understand the key triggers of digital transformation that affect their operations and industries. In this paper, the outcomes of a systematic literature review are presented which identify the triggers for digital transformations across key industries-using the example of Australia and its five core industries of services, mining, manufacturing, agriculture and construction. Outcomes indicate that triggers for digital transformation vary significantly across different industries. However, some triggers such as "environmental sustainability" were found to be important across most industries. The results contribute to current research in this field by providing a comprehensive overview of industry-specific transformation triggers. This will support decision-making for technology managers and provide the foundation for similar studies in other countries. I.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In the Healthcare sector, the main focus is on patients [15]. In order to improve the quality of care processes are improved and therefore become more efficient.…”

Section: B) Healthcare and Social Assistancementioning

confidence: 99%

Section: B) Healthcare and Social Assistancementioning

confidence: 99%

See 1 more Smart Citation

What Causes Companies to Transform Digitally? An Overview of Drivers for Australian Key Industries

Lammers

Tomidei

Regattieri

2018

2018 Portland International Conference on Management of Engineering and Technology (PICMET)

View full text Add to dashboard Cite

show abstract

“…PAH is a large public tertiary health care centre located in Brisbane Metro Area in the state of Queensland, Australia and provides care in all major adult specialties except maternity. PAH has been at the forefront of hospital digital transformation in Australia and one of the first hospitals that implemented a new information system in ED, Cerner FirstNet, in a fully integrated manner (Sullivan et al, 2016).…”

Section: Clinical Settingmentioning

confidence: 99%

Forecasting Waiting Time to Treatment for Emergency Department Patients

Pak¹,

Gannon²,

Staib³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Problem definition. The current systems of reporting waiting time to patients in public emergency departments (EDs) has largely relied on rolling average or median estimators which have limited accuracy. This paper proposes to use the statistical learning algorithms that significantly improve waiting time forecasts.Practical Relevance. Generating and using a large set of queueing and service flow variables, we provide evidence of the improvement in waiting time accuracy and reduction in prediction errors. In addition to the mean squared prediction error (MSPE) and mean absolute prediction error (MAPE), we advocate to use the percentage of underpredicted observations as patients are more concerned when the actual waiting time exceeds the time forecast rather than vice versa. Provision of the accurate waiting time also helps to improve satisfaction of ED patients. Methodology. The use of the statistical learning methods (ridge, LASSO, random forest) is motivated by their advantages in exploring data connections in flexible ways, identifying relevant predictors, and preventing overfitting of the data. We also use quantile regression to generate time forecasts which may better address the patient's asymmetric perception of underpredicted and overpredicted ED waiting times.Results.We find robust evidence that the proposed estimators significantly outperform the commonly implemented rolling average. Using queueing and service flow variables together with information on diurnal fluctuations, quantile regression outperforms the best rolling average by 18% with respect to MSPE and reduces by 42% the number of patients with large underpredicted waiting times.Managerial implications. By reporting more accurate waiting times, hospitals may enjoy higher patient satisfaction. We show that to increase the predictive accuracy, a hospital ED may decide to provide predictions to patients registered only during the daytime when the ED operates at full capacity translating to more predictive service rates and the demand for treatments.

show abstract

“…Understanding how these digital systems impact existing workflow for nurses is therefore imperative. There is a growing body of research that evaluates the implementation of digital management systems (Baumann, Baker, & Elshaug, 2018;Nguyen, Bellucci, & Nguyen, 2014;Ross, Stevenson, Lau, & Murray, 2016;Sullivan et al, 2016), and nurses' perceptions and experiences of them (Farre, Bem, Heath, Shaw, & Cummins, 2016;McBride, Tietze, Hanley, & Thomas, 2016). However, nursing literature concerning the effectiveness of digital health systems (including e-prescribing and documenting systems) in improving workload efficiencies is mixed (Boonstra, Versluis, & Vos, 2014), with some authors reporting that digital health record implementation decreases work efficiency (Boyer, Samuelian, Fieschi, & Lancon, 2010;Gephart, Carrington, & Finley, 2015;Hollingworth et al, 2007;Lee, Mills, & Lu, 2008;Poon et al, 2004;Scott, Rundall, Vogt, & Hsu, 2005;Ward, Vartak, Schwichtenberg, & Wakefield, 2011), while others identify improvements (Houser & Johnson, 2008;Katsma, Spil, Ligt, & Wassenaar, 2007;Seto et al, 2014), or no change (Hakes & Whittington, 2008;Munyisia, 2011;Munyisia, Yu, & Hailey, 2012;Smith, Smith, Krugman, & Oman, 2005).…”

Section: Introductionmentioning

confidence: 99%

The impact of an integrated electronic health record on nurse time at the bedside: A pre-post continuous time and motion study

et al. 2020

View full text Add to dashboard Cite

Pioneering digital disruption: Australia's first integrated digital tertiary hospital

Cited by 33 publications

References 7 publications

What Causes Companies to Transform Digitally? An Overview of Drivers for Australian Key Industries

What Causes Companies to Transform Digitally? An Overview of Drivers for Australian Key Industries

Forecasting Waiting Time to Treatment for Emergency Department Patients

The impact of an integrated electronic health record on nurse time at the bedside: A pre-post continuous time and motion study

Contact Info

Product

Resources

About