Mobile task management tool that improves workflow of an acute general surgical service

Abstract

Background: Understanding and being able to measure constraints within a health system is crucial if outcomes are to be improved. Current systems lack the ability to capture decision making with regard to tasks performed within a patient journey. The aim of this study was to assess the impact of a mobile task management tool on clinical workflow within an acute general surgical service by analysing data capture and usability of the application tool. Methods: The Cortex iOS application was developed to digitize patient flow and provide real-time visibility over clinical decision making and task performance. Study outcomes measured were workflow data capture for patient and staff events. Usability was assessed using an electronic survey. Results: There were 449 unique patient journeys tracked with a total of 3072 patient events recorded. The results repository was accessed 7792 times. The participants reported that the application sped up decision making, reduced redundancy of work and improved team communication. The mode of the estimated time the application saved participants was 5-9 min/h of work. Of the 14 respondents, nine discarded their analogue methods of tracking tasks by the end of the study period. Conclusion: The introduction of a mobile task management system improved the working efficiency of junior clinical staff. The application allowed capture of data not previously available to hospital systems. In the future, such data will contribute to the accurate mapping of patient journeys through the health system.

The Use of Data Collected From mHealth Apps to Inform Evidence‐Based Quality Improvement: An Integrative Review

“…Studies varied greatly in their aims, methodologies, and sample sizes. A variety of study designs were selected for analysis, including four systematic reviews (Holmen et al., ; Kitsiou, Pare, Jaana, & Gerber, ; Lee et al., ; Peiris, Praveen, Johnson, & Mogulluru, ), four randomized controlled trials (Armstrong, Coyte, Brown, Beber, & Semple, ; Cingi et al., ; Lakshminarayana et al., ; Wolf et al., ), one nonrandomized controlled trial (Sundberg et al., ), three retrospective cohort studies (Dickson, Sumathipala, & Reeves, ; Khanna, Sambandam, Gul, & Mounasamy, ; Twichell et al., ), six pilot studies (Foo et al., ; Gunter et al., ; Jakel et al., ; Macpherson et al., ; Patel, Siegler, Stromberg, Ravitz, & Hanson, ; Semple, Sharpe, Murnaghan, Theodoropoulos, & Metcalfe, ), and one case report (Gernart et al., ).…”

“…The predominant population of interest was patients (eight adult and one pediatric) with two studies looking at both adult and pediatric patients (Lee et al., ; Macpherson et al., ) and three studies not explicitly stating the age of the population impacted by the app (Cingi et al., ; Dickson et al., ; Lee et al., ). Five studies reviewed apps relating to healthcare clinicians including medical, nursing, social work, pharmacists, and clerical staff (Foo et al., ; Jakel et al., ; Khanna et al., ; Lee et al., ; Patel et al., ).…”

“…The types of data collected by the apps included numerical data (e.g., numbers, survey scores, vital signs; Cingi et al., ; Dickson et al., ; Foo et al., ; Gernart et al., ; Jakel et al., ; Sundberg et al., ), textual data (e.g., using words, phrases, or more in‐depth descriptions; Cingi et al., ; Gernart et al., ), photographic data (e.g., photographs of wounds; Khanna et al., ; Semple et al., ), graphic data where graphs or scales were generated as a result of patient or physician input or where images were selected by users to express symptoms (Foo et al., ); a number of apps were able to collect a variety of data types.…”

“…Data collected through apps were used both in real time to deliver health care, inform healthcare decision‐making, and create self‐awareness (Armstrong et al., ; Cingi et al., ; Dickson et al., ; Foo et al., ; Gunter et al., ; Jakel et al., ; Khanna et al., ; Macpherson et al., ; Semple et al., ; Sundberg et al., ; Twichell et al., ), and retrospectively to inform healthcare management (Gernart et al., ; Lakshminarayana et al., ; Semple et al., ; Wolf et al., ), communication strategies (Khanna et al., ; Patel et al., ), and healthcare delivery changes (Foo et al., ).…”

Coordination of care in hospitals: A rapid review of the literature

“…The other research found in this review that described possible methods for improving care coordination in hospitals included literature that focussed on: the use of communications technology to improve clinical communications, [8] the effectiveness of quality improvement and other interventions taking place in acute care settings (a systematic review), [9] studies that considered the effectiveness of information display systems in hospitals, [10][11][12] studies that considered the use of electronic task management tools, [13,14] a study that considered the effectiveness of nurse clipboard design, [15] and one that looked at the effectiveness of using a secure messaging application in a hospital. [16] However, as a group, this research did not describe measurable improvements to patient outcomes or efficiency, instead focussing on results that: were process rather than outcome oriented (such as the extent of usage of tools, or impacts on team behaviours); were subjective (such as measures of user satisfaction and self-efficacy, attitudes towards teamwork, and perceptions of care); reported improvements that were not compared against any other measure; or were statistically non-significant.…”