Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial

Golas, Sara; Nikolova-Simons, Mariana; Palacholla, Ramya; Buijs, Jorn op den; Garberg, Gary; Orenstein, Allison; Kvedar, Joseph C.

doi:10.1038/s41746-021-00463-y

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…A digital health tool that is useful in practice is one that is able to trigger a specific workflow: the insight that the tool provides must be translated into an actual human intervention in order to realize the tool’s value 25 . Prior work suggests that predictive models have the capacity to move the needle on clinical outcomes when their output is coupled with tailored human interventions 26 . Thus, both the tool’s target-outcome metric and its ability to affect this outcome metric must be considered.…”

Section: Is There a Clear Demonstration Of Clinical Value?mentioning

confidence: 99%

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation

et al. 2022

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In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation.

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Section: Is There a Clear Demonstration Of Clinical Value?mentioning

confidence: 99%

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation

et al. 2022

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“…Studies have demonstrated the use of computational modeling in medicine, which involves creating novel diagnostic tests and determining appropriate treatment plans for patients with specific diseases [7]. An example of such research involved building a computational model that accurately predicted mortality rates among acute myeloid leukemia patients [8].…”

Section: Selected Clinical Applicationsmentioning

confidence: 99%

Predictive Modeling in Medicine

Toma

Ong

2023

Encyclopedia

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Predictive modeling is a complex methodology that involves leveraging advanced mathematical and computational techniques to forecast future occurrences or outcomes. This tool has numerous applications in medicine, yet its full potential remains untapped within this field. Therefore, it is imperative to delve deeper into the benefits and drawbacks associated with utilizing predictive modeling in medicine for a more comprehensive understanding of how this approach may be effectively leveraged for improved patient care. When implemented successfully, predictive modeling has yielded impressive results across various medical specialities. From predicting disease progression to identifying high-risk patients who require early intervention, there are countless examples of successful implementations of this approach within healthcare settings worldwide. However, despite these successes, significant challenges remain for practitioners when applying predictive models to real-world scenarios. These issues include concerns about data quality and availability as well as navigating regulatory requirements surrounding the use of sensitive patient information—all factors that can impede progress toward realizing the true potential impact of predictive modeling on improving health outcomes.

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“…Regular updates and refinement of predictive models based on new data are necessary to maintain accuracy. Healthcare providers should be trained to interpret and act on predictive insights to improve patient care [40].…”

Section: Data Collection and Analysismentioning

confidence: 99%

Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review

Bhati,

Deogade,

Kanyal

2023

Cureus

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This comprehensive review delves into the critical role of effective hospital administration in shaping patient outcomes within the healthcare ecosystem. Exploration of key components, strategies, measurement methodologies, and future trends elucidates the multifaceted nature of hospital administration. Key findings underscore the profound impact of administrative decisions and practices on patient safety, satisfaction, and overall well-being. The review highlights the importance of patient-centred care and interdisciplinary collaboration for enhancing patient outcomes. It emphasises the significance of datadriven measurement and benchmarking, which are instrumental in assessing hospital performance and fostering continuous improvement. Looking ahead, emerging technologies, evolving healthcare policies, and persistent challenges are drivers of change in healthcare administration. However, amidst these transformations, the overarching message remains consistent: effective hospital administration is integral to improving patient outcomes. The conclusion calls for a collective commitment from healthcare leaders and policymakers to prioritise the development of capable administrators, invest in technology, promote value-based care, and address healthcare disparities. This collaborative effort ensures that the pursuit of better patient outcomes remains at the forefront of healthcare administration, ultimately shaping the future of healthcare for generations to come.

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Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial

Cited by 13 publications

References 27 publications

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation

Predictive Modeling in Medicine

Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review

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