The power of digital transformation in streamlining hospital operations to cut costs

INTRODUCTION

Digital transformation for efficient workflows

Digital transformation in hospitals can lead to significant cost reduction as hospital processes are streamlined, increasing efficiency and enhancing patient care. How can hospital operations be streamlined, and why does hospital digital transformation play a critical role in making workflows simpler and more effective?

Hospital operations can be made smooth and effective by adopting automation technologies, converting manual and paper-based work into digital and online activities, and using resources in an optimal manner.

However, digital transformation in hospitals does not merely entail transferring paper processes to the digital format. It also implies the use of digital technologies to make drastic changes that improve the reach and performance of a healthcare organization. As research firm Deloitte Insights puts it, “In a digitally transformed business, digital technologies enable improved processes, engaged talent, and new business models. This can take significant time, and can expect scrutiny of efforts. It therefore requires a thoughtful approach to ensure alignment with end goals while demonstrating value along the way.”

Digital transformation in hospitals facilitates operational efficiency through the use of technologies that allow a healthcare entity to realize its goal by adopting a clear hospital digital transformation strategy aligned with its long-term vision.

USE CASE

Operational decision-making to complement clinical needs

Almost every enterprise efficiency blog, software solution or product brochure claims to streamline operations…but how exactly does that work at the ground level? Operational efficiency comes from tracking, analyzing, and re-purposing data that flows in from clinical decision-making to enable hospitals to provide the right care at the right time to the right patient. Meaning some datasets can be used not only for clinical decision-making but also for operational decision-making by evaluating patients’ needs correctly and serving them through the effective use of hospital resources.

Here are some examples where custom software solutions were of particular help:

Triage

A patient comes in gasping for breath; the clinical need is to allow an ICU admission, but the beds may be occupied, with one patient’s condition relatively better. Data analytics of the medical records of both patients can help generate decision-support algorithms that make suggestions: admission to a surgical ICU until the medical ICU bed is vacated or discharge of the ICU patient to make room for the new admission. It helps to reduce re-admissions and extended stays.

Real-world application: The Boston Children’s Hospital uses an ICU-stay predictor that tells how many patients are likely to stay for longer than seven days. The solution uses logistic modeling based on a variety of clinical variables. The more long-stay patients it has, the ICU's capacity to handle short-stay ICU patients is reduced, but the operations team has a week’s time to smooth out planned or elective ICU stays or identify other ICUs for transitory stay.

Scheduling

A surgeon needs to book an operating room for an appendectomy. Usually, the time required for this procedure is an hour. However, big data analysis -say of a million patients belonging to a younger age group -reveals that the surgery is completed in 25 minutes. This finding can be used to streamline the bookings, free up operating room capacity, and reduce the expenses related to surgery both for the patient and the hospital.

Real-world application: The Beth Israel Deaconess employed AI and ML techniques to make improvements to a scheduling system that helped to increase the capacity by 30% across 41 operating rooms. The data that was obtained and analyzed also came in handy to predict when patients were most likely to miss appointments with in-demand specialists. Using these learnings, it developed an app for doctors to schedule surgeries.

Staffing

Algorithms can predict staff absenteeism, the need for surge staffing, and optimize team composition -all of which help to maintain consistency in work schedules. While many hospitals continue to draw up duty rosters and shift schedules relying on spreadsheets, texting and phone calls, AI can leverage insights gained from data analytics to provide correct recommendations on staffing, increase productivity and arrest attrition rates.

Real-world application: At one of the largest US home health agencies, time-stamped work log data and ML techniques identified schedule volatility as the primary reason behind nurses' voluntary turnover. Variable work scheduling seen as “flexible” by employers was rejected as “unstable” by employees, with 20% likely to quit by the end of the year.

Capacity management

Sophisticated software solutions based on IoT platform integrations with smart sensors are helping to manage hospital capacity both in terms of workforce and assets. New-age technologies such as Bluetooth Low Energy, Real-time Locating Systems, and Radio Frequency Identification are being deployed for equipment order fulfillment and asset inventory management. They help to reach life-saving medical equipment, medication, and injections on time by optimizing resources.

Real-world application: America’s #1 children’s hospital - home to the largest pediatric research facility in the country, relies on a single source of truth for operational agility through hospital digital transformation. Yours truly worked with the healthcare operations vendor to develop a custom software and hardware solution to optimize asset inventory management.  We are working on streamlining patient transport now.

Digital transformation in hospitals is worth the investment as it streamlines operations and reduces costs