May 11, 2023 | 5 minute

How to Identify, Prioritize, and Deploy Digitization Initiatives in Hospitals

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Digital Transformation Playbook for Health System Executives – 3 of 3

In previous posts, we set out on a journey to digitally transform the operational side of health systems and the care delivery process. We established a framework to optimize and balance operating efficiency and quality of experience.

In the same way as digital marketers think about websites, our goal is to develop a systematic and holistic understanding of the patient journey. This knowledge will help us solve experience quality issues for our staff and patients and will help uncover wastefulness while modernizing the tools we provide to operators. By taking the foundational steps to collect and centralize data, we place the patient in the context of clinical space, staff, medical device and process.

The final phase of digital transformation is also the most challenging one— identifying, prioritizing, and deploying digital initiatives. We recommend this as a general rule of thumb, because we see health providers through the lens of either experience or cost-savings. That approach sometimes traces back to the data gathering phase, resulting in only a partial understanding of the journey, and will quickly lose the ability to balance efficiencies with experience quality. Taking a journey-centric approach instead, here is a useful way to think about hierarchy and order of deployment for digitizing health system operations.

Patient movements

We start by looking at the movement of the patient through care delivery. There are some ideas to consider:

  • Establish a clear model that maps journey stages to cost, value, and experience friction in real-time with visibility for the C-level .
  • Establish plug-and-play to map staff, space, and devices to patient movement, keeping in mind the need to predict bottlenecks, the agility to contract and expand operations easily (consider just-in-time), and in general, the use of APIs to enable connectivity between systems.
  • Repeat this process but from specific role players’ standpoints (e.g., nurses).

Following or in parallel to building journey intelligence, it is useful to initially focus on capital assets, namely medical devices. As suggested previously, it is an excellent place to start because of its potential to generate fast wins in both quality and efficiency, and, most notably, its promise for strong, near-term savings. Balancing fleet size and availability of service is the core problem to optimize. I like the “digital concierge” approach we take at using AI in clinician interfaces; but smart API integration across service workflow, supply chain, and in-department displays can achieve the same goal with systems that health providers already have in place.

Implementing big data models

The true opportunity is the ability to deploy large data models to data sets. Medical imaging uses tools developed for rental scooter pickup in city centers to modernize the way a health system supply chain and biomed services cooperate to deliver nurses with medical device services. A more basic machine learning model can easily predict inventory levels needed in various clinical spaces, yielding massive benefits, economically and in care participant satisfaction.


If not first, safety should be the second area to focus on. In this space we see an initial provider focus on nurse safety through the deployment of location-aware duress buttons, and more importantly, the use of spatial incident analytics to better align preventive strategies with the characteristics of events over time. Safety use cases can also relate to infectious disease prevention through smart hand hygiene, nurse call cancellation, and other traditional use cases.

Patient experience

The last area for preliminary focus is the patient experience—perhaps the most confusing space for many of our customers. To be honest, I feel much of today’s ‘hot topics’ in this area are based on speculation and too often presented as fact. I suspect that as in commercial real estate, where office worker experience is a space similarly bubbling with new ideas, it will go through a shakedown. Nevertheless, there are some basics that are easy to get done right. Based on our framework, experience must be coupled with operating efficiency. These are the best areas to start with and where value ambiguity is the lowest. For example, the monitoring of waiting spaces and the streamlining of patient flow through ambulatory workflows improve satisfaction and throughput by re-assigning resources to relieve bottlenecks in near real-time. Rather than only providing insights into issues, an agile care delivery system adjusts operating parameters with the potential of impacting both the quality of service and efficiency of operation.

From data discovery, digital concierge services, and staff and patients to real-time predictive models, the digital healthcare space is on fire. A new breed of cloud-native vendors is moving into the space with almost unlimited on-demand computing (powered by players like Snowflake) and AI processing from major cloud vendors. This area holds fascinating promise.

I hope that this series of posts was helpful in sharing what we are witnessing in the market and our insights on what we see work best: 

  • System-wide outcome vectors and clarity on vision
  • Alignment between IT, supply chain, biomed, and nursing services
  • Well-planned data mapping to achieve data accessibility
  • Enabling existing systems  to operate synchronously using APIs
  • Implementing a digital care journey platform to consolidate data and publish new digital services
  • Finally, shifting mindset from buying technology to buying data and outcomes, as a service, that scales with the provider’s needs

Download our Digital Transformation in Hospitals Whitepaper and learn how Smart Hospital Solutions are delivering better clinical outcomes, greater efficiencies, and higher patient satisfaction through digital transformation using technologies like Internet of Things (IoT) and Artificial Intelligence (AI).