Elevating healthcare operations cannot occur merely by adding technology layers that require nurses and doctors to collect more data. It is about defining outcomes - patient, operational, or financial - and creating the structure to achieve those outcomes. Recall the oft-used and much-maligned adage "work smarter, not harder." The phrase trickles from those managers and leaders who don't know what else to do or say. The effect is rarely well-received. Yet……, sometimes, it is true. In this case of healthcare operations, it's mandatory.
Drive better health through smart operations.
Smart Operations intentionally aligns people, processes, and technology to deliver complex and interdependent clinical, operational, and business functions. Smart Operations pull health systems forward by focusing on good-design, intelligent workflows supported by actionable data to achieve tangible outcomes.
Checklist for Restarting Healthcare
The question of how and when to re-open businesses in the United States in the wake of COVID-19 is fraught with a multitude of economic, political, and often emotional decisions. Central to this debate is how to and when to resume delivering traditional healthcare services that has been retooled to manage the COVID-19 surge or on standby hoping the surge never happens. As the hospitals prepared for a deluge of critically ill patients, most placed elective procedures on hold, reallocated resources and reduced capacity to manage the surge of highly infectious patients in acute respiratory distress.
HuBo - Human-Bot Hybrid COVID Toolkit
Edgility SmartConnect is a scripted, bot and human (HuBoTM) hybrid, outgoing touchpoint (calls/texts) service for high-risk and quarantined COVID-19 Patients.
Systems Operations: Call Center Toolkit
The Benefits of a COVID-Centric Call Center. Click here to view the Call Center Toolkit.
The COVID-19 Call-Center Toolkit is the latest in a series of applications designed by Edgility to assist hospitals and healthcare organizations operationally manage the Coronavirus outbreak.
Additional Toolkits can be found here:
COVID-19 Toolkit – Click Here
COVID-19 Digital Toolkit – Click Here
Edgility COVID-19 Digital Toolkit
Edgility COVID-19 Toolkit
Edgility has released the Emergency Systems Operations Module, for all existing clients, to help health care providers and hospital systems manage the COVID-19 outbreak. Edgility is also releasing the framework as an open source tool kit, for all health systems.
Come see us @ HIMSS 2020 Booth 6084
Come to the Edgility Booth at HIMSS 2020 to design a Systems Operations Center.
A Case For Seamless Data Flow
Check out the latest article from Forbes Tech Council featuring our founder Balaji Ramadoss.
In a recent Harvard Business Review article entitled "Why Data Science Teams Need Generalists, Not Specialists," Eric Colson defines the limitations of traditional “division of labor” structure as it pertains to data science and compares it with a “learn as you go” structure facilitated by the generalist. The division-of-labor model works well, he notes, when the requirements “fully describe all aspects of the product and its behavior.” But when “knowledge” is the requirement, the specialization construct found in the division-of-labor models only hinders. As a result, Mr. Colson describes the need to balance learning vs. efficiency gains by hiring full-stack data scientists -- generalists who are responsible for everything from conception to implementation.
Today, information technology and data platforms perpetuate the barriers for operationalizing with architectures that mimic the silos based on the function-based division of labor. Contemporary data platforms are designed around these function-based divisions that have not evolved much since the 1990s. As an example, storage and normalization specialists are still the bottlenecks for the as-a-service platforms of today, mimicking the traditional silos in specialization.
Bloated investments in data architectures such as multiple warehouses, on-premise data marts, cloud-based data lakes and disparate business intelligence tools tend to result in data remaining mired in their operational silos. This trend is observed across all industries and, in many cases, business lines within industry verticals tend to have different solutions perpetuating the need for specialists to care and feed data and technology platforms.
Organizations that aspire to divest from the silos of specialization and focus on reducing the incumbent friction and inertia should look past technology and focus on outcomes and ownership. Practically, this can be achieved by organizing teams and specialties by business units and outcomes to avoid traditional technology silos. Through this strategy, teams are not only permitted but encouraged to cross the all-too-often uncrossable silos to ideate multiple solutions before converging on the best one.
Technology executives also need a new operating model and culture that puts the focus primarily on outcomes and empowers the generalist to own the outcome. It is critical not to see this as a technology investment but rather a rewiring of existing investments to maximize returns by creating process efficiencies that emanate from reducing handoffs and specialization silos.
It's important to focus on the philosophical change in your organization’s approach to problem-solving. Analyzing workflows and then reducing or eliminating the low-value functions performed by high-value resources is the first step. These changes, by design, will lead to new ways of tapping into and expanding the value of both the siloed data and your incumbent technology investments.
When appropriately architected, data and technology that is rewired around organizational outcomes can mechanize the operating model and enable iteration, learning and cognitive capabilities. These capabilities will allow you to implement a cognitive learning platform and will allow generalists to move fluidly between silos of specialization, data pipelines and measurements. This structure is amenable to generalists who seek insight into the business not readily apparent to the specialist.
The key to a learning organization is iteration, and a cognitive platform is architected to reduce the "tax on iteration." To facilitate learning, we recommend the cognitive platforms of tomorrow focus on closing the knowledge gaps and practice loops between silos. Removing friction and inertia requires a radical rethinking of current data, technology and development methodologies that have created specialization silos. We believe cognitive platforms should allow data to coalesce around specific outcomes so that generalists can build models, applications and solutions.
Organizations that have spent the last decade warehousing data will be required to utilize and exploit their data using this new and distinctive cross-functional, collaborative model. We recommend strategically disabling the traditional silos in specialization, both from a technology and from a talent perspective. This new operating model, philosophy and technology architecture will do more than automate and optimize workflows; they will become responsive and intelligent. Moreover, instead of wasting cycles in operational handoffs between specialists, the generalist can focus on removing ineffective and inefficient processes imposed on workers by eliminating the low-value workflows from high-value assets.
As an operating principle, when a cognitive platform is paired with generalists who have the full-stack ownership, the output is a high-performing organization.