In marketing, this means seeing how different channels, tactics, strategies and changing technologies are interconnected. It's about understanding that a change in one area, like a shift in SEO strategy, can impact other areas, such as content marketing or paid advertising. For marketing leaders, adopting a systems mindset can be the key to transforming strategies into successful executions. It involves:
1. Complexity Management: Modern marketing campaigns often span multiple channels, from social media to email marketing, enterprise SEO and more. A systems mindset helps leaders understand how these channels interact, ensuring that efforts in one area complement, rather than conflict with, efforts in another. 2. Efficient Resource Allocation: By understanding the interconnectedness of various marketing components, leaders can allocate resources more efficiently, ensuring that every dollar spent contributes to the overall desired outcomes and key results (OKRs). This also means optimizing time and human resources, ensuring that teams are working cohesively and effectively. 3. Anticipating Challenges: A holistic systems view allows marketing leaders to foresee potential challenges and address them proactively. Whether it's a change in the algorithm of a social media platform, a sudden market disruption or a shift in consumer behavior, a systems mindset prepares leaders to adapt swiftly and better prioritize with other leaders in the company. 4. Informed Decision Making: With a comprehensive view of the marketing landscape, leaders can make more informed decisions, weighing the potential impact of each choice on the entire system. This clarity can lead to more strategic investments and campaigns that resonate deeply with target audiences. From Vision To Execution: The Power Of A Systems Approach
Having a vision is crucial, but the real challenge lies in translating that vision into actionable steps. Here's how a systems mindset aids in this transition:
• Strategic Alignment: By viewing marketing as an interconnected system, leaders can ensure that every tactic aligns with the overarching strategy. This alignment ensures that all efforts push in the same direction. This approach assists in making wise decisions on how to allocate investment in growth opportunities. • Feedback Loops: A systems approach emphasizes the importance of feedback and leading indicators. By continuously monitoring and adjusting, marketing leaders can refine their strategies in real time, ensuring that campaigns remain effective and relevant. This iterative process allows for agility in a constantly changing market. • Collaboration and Communication: Recognizing the interconnectedness of marketing components fosters a collaborative environment. Teams can work together more effectively, understanding how their efforts fit into the larger picture. This synergy can lead to more innovative solutions and campaigns that truly stand out. The Systems Mindset In Action
In digital marketing, the systems mindset can be seen in various ways. For instance, consider the integration of content marketing with SEO. While content provides value to the audience, SEO ensures that this content reaches the right audience. Understanding this interplay can lead to content that not only resonates but also ranks well in search engines. Similarly, in multichannel campaigns, understanding the user journey across different touchpoints can lead to more cohesive and effective campaigns. A user may first interact with a brand through social media, then through an email newsletter and, finally, through a website. Ensuring a consistent and interconnected experience can significantly boost conversion rates. With these factors in mind, here are a few of the steps to building a systems mindset in your organization:
• Education: Begin with foundational reading on systems thinking, such as books by pioneers like Peter Senge. • Workshops: Attend workshops or seminars focused on systems thinking. • Collaboration: Engage with peers who already employ this mindset. • Document: Draw the interactions and marketing messages with your clients in a whiteboard application. • Iterative Implementation: Start small. Apply the systems mindset to a single project before scaling up. • Review and Refine: Regularly review the outcomes of your strategies and refine your approach. Conclusion: Embracing The Future Of Marketing Leadership
The digital age has brought about unprecedented opportunities and challenges for marketers. Companies have never had more opportunities and more chances to fail. As the landscape continues to evolve, the role of a marketing leader is no longer just about crafting compelling messages. It's about understanding the intricate web of interactions that drive modern marketing campaigns and then walking the CEO through the options for wise investment, the leading indicators to monitor and when to revisit the implementation strategy. By embracing a systems mindset, marketing leaders can navigate this complexity with grace, turning challenges into opportunities and visions into realities. It's not just about thinking outside the box—it's about understanding how all the pieces of the box fit together. In doing so, leaders can drive innovation, foster collaboration and ensure that their strategies translate into tangible results. In the words of Peter Senge , a systems thinking pioneer, "Systems thinking is a discipline for seeing wholes." For marketing leaders, it's a discipline for seeing success. Forbes Communications Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify? Follow me on  Twitter  or  LinkedIn . Check out my  website .

From Strategy To Execution: The Role Of A Systems Mindset In Marketing Leadership

Systems Engineering for Amplification and Value Across four decades, I have worked as a systems engineer in the information technology (IT) industry
The post What’s the Business Value of AI? A Systems Engineer’s Take appeared first on Gigaom.

What’s the Business Value of AI? A Systems Engineer’s Take

The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper
The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper
Authors of this article:
2Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL, United States
Corresponding Author:
University of Alabama at Birmingham
1720 University Blvd
Abstract
Emergency department (ED) crowding and its main causes, exit block and boarding, continue to threaten the quality and safety of ED care. Most interventions to reduce crowding have not been comprehensive or system solutions, only focusing on part of the care procession and not directly affecting boarding reduction. This position paper proposes that the ED crowding problem can be optimally addressed by applying a systems approach using predictive modeling to identify patients at risk of being admitted to the hospital and uses that information to initiate the time-consuming bed management process earlier in the care continuum, shortening the time during which patients wait in the ED for an inpatient bed assignment, thus removing the exit block that causes boarding and subsequently reducing crowding. Interact J Med Res 2023;12:e42016
Keywords
The Emergency Department Crowding Problem
The ED crowding problem occurs when the ED demand exceeds the staff’s ability to provide quality care in a reasonable period of time [ 1 , 2 ]. The literature suggests that hospital exit block [ 3 , 4 ] (ie, when patients cannot transition into the hospital from the ED because a hospital bed has not been assigned [ 3 ]) and ED boarding [ 5 - 8 ] (ie, when a patient due to be admitted to the hospital remains in the ED, occupying a bed [ 3 ]) are the main causes of ED crowding and posits that an impactful solution lies in changes in the bed management strategy, the processes involved in the transition of patients from the ED to the hospital, and when securing a hospital bed [ 9 , 10 ]. This position paper proposes that the complexities of the ED crowding problem can be optimally addressed by applying a systems approach to the hospital bed management strategy. The systems approach views an environment as a whole, which is made up of many parts or subsystems for the purpose of understanding the relationships between the system and its parts and to aid in problem-solving [ 11 ]. A systems approach that uses predictive modeling to identify patients who are at risk of being admitted to the hospital and uses that information to initiate the complex and time-consuming bed management process earlier in the care continuum could potentially shorten the time during which patients wait in the ED to be transitioned into an inpatient bed, thus removing the exit block that causes boarding and subsequently reducing crowding [ 12 ]. The challenges facing the health care industry are greater than ever before, with increasing complexities of care, regulations, and higher quality care expectations. At the same time, the industry is challenged to address preventable medical errors, poor amenable mortality rates, nursing and physician burnout and shortages, and general inefficiencies. These industry challenges are magnified in the ED where the nature and environment of emergency care cannot tolerate threats to quality care delivery or to patient and provider safety. A systems approach views the ED as a complex microcosm of a larger health ecosystem where optimal functionality requires that it be resilient to the unpredictable demands [ 13 ] characteristic of the urgent care environment. To manage new and unpredictable challenges, a systems approach can be used to address known threats. An efficient manner by which to accomplish this is to identify predictable and repeatable processes to which information technology can be applied. Crowding and its main causes, exit block and boarding, have threatened the quality and safety of ED care for over 20 years despite the efforts of many to resolve it [ 14 - 16 ]. Boarding compromises care quality [ 3 , 17 ], stresses hospital operations [ 18 ], and strains resources because boarded patients occupy beds and divert staff resources from new and existing patients [ 17 ] and reduce revenue generation through the reduction in available beds for treating new patients [ 19 ]. The industry would benefit from improved approaches to resolve the crowding problem. The existing research focused on this problem suggests that predictive modeling holds promise to make a significant contribution toward addressing ED crowding; for example, models have predicted imminent hospital admissions for older ED patients [ 20 ], identified patients who are likely to require hospital care in the future [ 21 ], predicted ED crowding using calendar and weather variables [ 22 ], and forecasted ED flow digitally [ 23 ]. Applying Predictive Modeling to Resolve Crowding and Bed Management
Predictive modeling is a form of data mining technology that functions by analyzing historic and current data, and generating a model to help predict a future outcome [ 24 ]. It is an explicit, empirical approach for estimating the probabilities that an event will or will not occur in the future [ 25 ]—such as death, contracting a disease, surgical complications, or hospital admission—by using statistical techniques to predict future events. Models use data about patients, diseases, or treatment characteristics to estimate the probability that a condition or disease is present or the probability that an outcome will occur [ 26 , 27 ]. However, models are only just starting to be used to produce actionable information to impact operations and patient care. We posit that predictive models to identify patients who are at risk of being admitted could be applied in the bed manager environment to remove the exit block that causes boarding by initiating the bed management process earlier in the care continuum, thereby shortening the time during which patients wait in the ED for an inpatient bed assignment. This, in turn, reduces exit block, boarding, and subsequently crowding. As no clinical decisions are made on the basis of patients’ risk of admission, this process could be automated to streamline part of the complicated bed management process and take advantage of predictable and repeatable processes using standardized data. Many interventions to reduce crowding have not been comprehensive or system solutions but rather focus on part of the care procession and do not directly affect boarding reduction [ 28 ]. However, existing interventions that addressed crowding as a systemic problem have reduced the time during which a patient is boarded in the ED [ 9 , 10 , 29 ], which, in turn, reduces the backlog of boarded patients who contribute to ED crowding. Interdepartmental collaboration with hospital management support was a feature in these interventions. Two of these interventions also used real-time ED data on congestion, flow, and patient admissions to prepare for and manage inpatient admissions and bed demand [ 9 , 29 ]. Individual interventions are parts of the system, rather than being considered a collective, and are automated to contribute valuable data to augment bed management. The use of predictive models in health care have quadrupled over the last 2 decades and their accuracy has increased [ 12 , 28 ]. While these have traditionally been applied to identify risk, the time is right for integrating predictive models with existing technologies such as electronic health records, clinical decision support systems, and clinical data warehouses, to result in action and efficiencies. n 2019, the use of predictive modeling was reported among 60% of health care executives within their organizations, and another 20% of them expressed intent to begin using them the following year [ 30 ], again primarily for risk prediction and not necessarily action. This existing technological infrastructure holds promise to reduce prior barriers to integration. Moreover, as clinical care becomes increasingly personalized, aid from predictive analytics may become a best-practice procedure. Concerns for Risk Models
Developing quality predictive models is challenging [ 28 , 31 ]. Deciding what variables to measure to answer a particular question can even be problematic. For example, if the goal is to predict “health,” which data are measured as indicators of health? The answer to this question varies. If rigorous study methods are not used throughout study development, data gathering, and analysis, there are numerous avenues for the model to make errors and lead to unintentional bias. Recent studies, including a systematic review of admission prediction models [ 28 ], have questioned the quality and rigor of existing predictive models [ 12 , 28 , 31 - 33 ] due to an overall lack of external validation studies [ 31 ], multiple prediction models for the same outcome or target population [ 26 ], and risks of bias [ 34 - 36 ]. A bias unique to predictive models, algorithmic bias, occurs when technology reflects the attitudes and values of the humans who coded, collected, selected, or used the data to train the algorithm [ 37 ]. Thus, machine-generated algorithms are human products executed by a machine. Algorithms should not be blindly trusted or considered neutral and unbiased [ 37 ]. Reliance on an algorithm to predict health-related outcomes or to make decisions about care would increase the pace of decision-making, but the point at which the decision should be transferred from machine to human is necessary, unclear, and currently unregulated [ 37 ]. Conclusions
Large amounts of data are available for analysis, and the demands on the health care industry are increasing, making the use of predictive modeling to aid hospital operations sensible and increasingly necessary. The old adage “garbage in garbage out” remains true when applied to predictive models—models developed without quality methodologies risk producing predictions deficient of quality. A model that produces biased predictions may not resolve the problem at hand. Evidence that a model is effective and safe is necessary before its use in a clinical setting. Best practices promoting standards for development and operation will have a role to play in model improvement and their use in clinical settings. Application of models that predict hospital admission could aid hospital bed managers to secure an appropriate bed for a patient in a timely manner while boosting hospital efficiency and with no harm to patients. The result of this timely and streamlined systems process is better patient care delivered sooner. We posit that applying a systems approach using prediction models to the hospital bed management strategy for ED patients would reveal the many parts and subsystems involved before and after bed assignment and would ensure that they are part of the solution. This unique application of a prediction model provides bed managers information to support initiation of bed management processes earlier in the care continuum. This strategic use of information has significant potential to reduce hospital exit block and ED boarding, and subsequently ED crowding [ 12 ]. Conflicts of Interest
Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol  2016 Jan;69:245-247  [ https://europepmc.org/abstract/MED/25981519 ] [ CrossRef ] [ Medline ]
McIntosh GS, Steenstra I, Hogg-Johnson S, Carter T, Hall H. Lack of prognostic model validation in low back pain prediction studies: a systematic review. Clin J Pain  2018 Aug;34(8):748-754 [ CrossRef ] [ Medline ]
Shamsoddin E. Can medical practitioners rely on prediction models for COVID-19? A systematic review. Evid Based Dent  2020 Sep 25;21(3):84-86  [ https://europepmc.org/abstract/MED/32978532 ] [ CrossRef ] [ Medline ]
Miles J, Turner J, Jacques R, Williams J, Mason S. Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review. Diagn Progn Res  2020 Oct 02;4(1):16  [ https://europepmc.org/abstract/MED/33024830 ] [ CrossRef ] [ Medline ]
Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K. Machine learning versus usual care for diagnostic and prognostic prediction in the emergency department: a systematic review. Acad Emerg Med  2021 Feb 02;28(2):184-196  [ https://onlinelibrary.wiley.com/doi/10.1111/acem.14190 ] [ CrossRef ] [ Medline ]
Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E,  et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ  2020 Apr 07;369:m1328  [ http://www.bmj.com/lookup/pmidlookup?view=long&pmid=32265220 ] [ CrossRef ] [ Medline ]
Watson K. Predictive analytics in health caremerging value and risks. Deloitte Insights 2021 March 12, 2022. 2019. URL: https:/​/www2.​deloitte.com/​us/​en/​insights/​topics/​analytics/​predictive-analytics-health-care-value-risks.​html [accessed 2022-03-12]
‎
ED: emergency department
Edited by  T Leung; submitted 29.08.22; peer-reviewed by J Jewer,  S Nagavally,  G Kolostoumpis; comments to author 25.11.22; revised version received 12.01.23; accepted 10.05.23; published 10.07.23

The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper

resilience
July 5, 2023
Show Summary
On this episode, global systems researcher Iñigo Capellán Pérez joins Nate to discuss net energy analysis, and its use as a tool in analyzing the feasibility of an entire system. While net energy analysis is complex and polarized, some form of it will be necessary to guide society into a resource-constrained future. The energy outlook of a technology changes when scaled up to meet the needs of a larger society. Many models and EROI analyses that fail to take a system-wide view and only look at a single technology use case, only reflect the partial net energy story. When looking at the huge scale-up needed for ‘solutions’ such as Green Growth and Net Zero Transitions, do the hopes for renewable technologies hold up the lifestyles we’ve come to expect over the last century of fossil surplus? Do our policymakers and leaders have the full picture to make competent decisions? How can we best use these tools to make creative responses to guide us through a Great Simplification? About Iñigo Capellán Pérez
Iñigo is an Industrial Engineer currently doing research with The Group of Energy, Economy and Systems Dynamics (GEEDS) of the University of Valladolid. He holds a Master degree in Electric Energy and Sustainable Development and a PhD in Economics with his dissertation on “Development and Application of Environmental Integrated Assessment Modelling towards Sustainability” at the University of the Basque Country. His research interests focus on the analysis and modeling of the energy-economy-environment systems, the transition to renewable energies in the context of the depletion of fossil fuels and climate mitigation and the technical and social transformations towards sustainability. Teaser photo credit: The 40.5 MW  Jännersdorf Solar Park  in  Prignitz , Germany. By Parabel GmbH – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=32375257
Nate is a well-known speaker on the big picture issues facing human society and currently teaches a systems synthesis Honors seminar at the University of Minnesota ‘Reality 101 – A Survey of the Human Predicament’   Nate is on the Boards of Post Carbon Institute, Bottleneck Foundation, IIER and Institute for the Study of Energy and the Future. Previously, he was lead editor of  The Oil Drum , one of the most popular and respected websites for analysis and discussion of global energy supplies and the future implications of the upcoming energy transition.Nate’s presentations address the opportunities and constraints we face after the coming peak of global economic growth. On the supply side, Nate focuses on the interrelationship between debt-based financial markets and natural resources, particularly energy with and the unique (and so far unplanned for) risks from the coming ‘Great Simplification’. On the demand side, Nate addresses the evolutionarily-derived underpinnings to
Tags: energy transition, net energy, net energy analysis, systems thinking
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Iñigo Capellán Pérez: “Net Energy Analysis: Through a Systems Lens”

While the ARPANET is generally viewed as the predecessor of the internet, what surprised me is how quickly the ideas of the internet came together once the ARPANET existed. The first ARPANET hosts were connected in 1969, and as early as 1972 Bob Kahn was working on the ideas of “internetting”: connecting different networks together. There weren’t a lot of networks to connect, but packet radio was developed around the same time, so the idea that very different networks could be connected arose quite early. Realizing that the protocols that he was creating to support internetting were going to need to be implemented on various operating systems, Kahn teamed up with Vint Cerf, who had experience with the implementation of NCP (which can be viewed as the predecessor of TCP/IP) on ARPANET. The idea that TCP/IP had to support heterogeneous networks was another big departure from the networks of the past–telephone networks were much more homogeneous. Much more than just TCP and IP
My favorite paper on how the internet developed is the 1988 paper [ PDF ] by David Clark: The Design Philosophy of the DARPA Internet Protocols. With the benefit of 15 years of experience, Clark – often referred to as The Architect of the Internet – clearly lays out what he, Kahn, Cerf, and many others were trying to achieve when the internet was designed. There was a lot more to the internet than the TCP and IP protocols, and Clark lays out a detailed list of design goals, all in service of one top-level goal: to interconnect existing networks. That meant, importantly, that you couldn’t impose changes on the existing networks to make them “fit” the internet architecture: the internet had to be able to accommodate widely varying types of networks, eg: those with no reliable delivery mechanisms or ability to provide notifications of failures. The ability to accommodate networks in spite of wide variations among their capabilities, including networks not yet invented, was quite a radical idea, and drove a lot of critical design decisions, such as the adoption of a best-effort service model. The list of second-order design goals starts with:
Internet communication must continue despite loss of networks or gateways. I feel obligated to point out that there is no mention in this paper of the internet being designed to withstand nuclear attack. Unfortunately, I was responsible for perpetuating that misconception in the an early edition of Computer Networks: A Systems Approach. Leiner et al debunk this myth and explain its origins in their paper, but it’s a remarkably persistent one. “[C]ontinue despite loss of networks or gateways” is as close as it gets. Of the other design goals, the one that I keep coming back to is “distributed management of resources.” I’m personally very interested in the many ways in which the decentralized architecture of the internet has been eroding, and have written about this topic here and here . As far back as 2016 I began to take an interest in blockchains as a possible path to a more decentralized approach, but these days I mostly find myself agreeing with Molly White and her ironic Web3 is Going Just Great site, which catalogs the steady stream of Web3 failures and grifts. Fortunately, there is more to the decentralization of the internet than blockchains and Web3. There is no mention in this paper of the internet being designed to withstand nuclear attack
As we discussed last year , there are real signs of life for the decentralization of social media thanks to the emergence of ActivityPub and the Fediverse. Just as I was putting finishing touches on my slide deck, there was yet another meltdown in the world of centralized platforms, with the CEO of Reddit deciding that sudden changes to API pricing – to the point that lots of third-party applications become economically unsustainable – was such a good idea at Twitter that he would bring the same approach to Reddit. The response from volunteer moderators at Reddit – the unpaid community members who make the platform valuable to users – has been swift and, in some cases, hilarious. But the aspect of this story that really caught my eye was the rapid rise of ActivityPub-powered Reddit alternatives Kbin and Lemmy . The future
The number of users of these services, which are interoperable with each other and connect to the rest of the Fediverse, has roughly tripled in a week (off a low base). Furthermore, there is a flurry of activity to beef up the implementations so that moderation tools – an essential part of any social media platform – can keep up with the growth in users. As an active user of another part of the Fediverse, Mastodon , I can tell you that there is lots of discussion among Fediverse users at present around how best to deal with all this growth and what it would take to scale the Fediverse up to the size of the big centralized platforms (which are still orders of magnitude larger than the Fediverse). It’s really too early to tell how this will all play out, and there is certainly more to the internet than social media platforms, but I do feel a degree of optimism that the pendulum is swinging back towards the decentralized approaches that enabled the internet to grow and flourish over the last sixty years. ®
Larry Peterson and Bruce Davie are the authors of Computer Networks: A Systems Approach and the related Systems Approach series of books. All their content is open source and available on GitHub . You can find them on Mastodon , their writings on Substack , and past The Register columns here . Share

It's time to mark six decades of computer networking

Acquired

A Systems is a Brazilian IT Service provider for renting, HW maintenance, data center services and professional services.

Date	7/24/2018
Round	Acquired
Amount
Investors	Evernex
Valuation	$99M
Revenue	0 FY undefined
Sources	2

First funding	7/24/2018
Last Funding	7/24/2018
Investor	Evernex
Rounds	1 Acquired
Board Seats
Type	Corporation
Location	France

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