Healthcare Insights Using Voice-Generated Data Visualizations

Dr. Erin Stevens collapses into her office chair after another hectic day at the clinic. She just spent 10 straight hours bouncing between patients without a single break. Now, comes her least favorite part of the day—spending the next 2 – 3 hours typing up cryptic notes from the 40+ patient appointments she conducted.

The documentation burden has contributed to rising physician burnout, with rates increasing between 46 – 54% since 2014. Dr. Stevens dreads having to recap every detail about each patient’s medications, family history, specialist recommendations, and more. She knows these notes contain valuable insights, but who has the time to organize all that information? There has to be an easier way.

Here’s a fascinating thing: over 80% of patient data in healthcare exists trapped in unstructured physician notes and narrations. Yet, advanced analytics promises to optimize everything from disease treatment to workflow efficiency. So, how do you tap into these invaluable care insights? 

This article explores how natural language processing (NLP) structuring massive volumes of voice documentation uncovers a goldmine for enterprise analytics via creative data visualization.

Turning narrations into structured data assets

Leading electronic medical records (EMRs) now integrate seamless speech recognition so physicians can narrate patient encounters easily. What’s groundbreaking, though, is how advanced NLP accurately structures these voice notes for downstream analytics in a way that is similar to traditional records.

Why does structuring these narrations matter when EMRs already store clinician notes? Consider the limitations physicians face typing long summaries after appointments—a lack of time and tools to neatly organize critical details like family histories, specialist recommendations, or medication changes.

Voice eliminates these friction points, with doctors talking freely as they normally would. The tech handles converting even complex medical terms into neatly indexed fields ready for analysis.

We’ll illustrate the impact with real examples:

• Geisinger Health visualizes parsed pathology records using Tableau dashboards predicting cancer outcomes, enhancing precision medicine treatment options for patients
• MetroHealth’s voice analytics reduced mortality by over 40%, discovering early respiratory and kidney disease warning signs

The key is voice narrations providing incredibly rich, timely data compared to traditional notes. So, when structured properly, both population health insights and individual patient care can advance rapidly.

Driving adoption through data transparency

Understanding voice tool adoption rates is pivotal for healthcare administrators to guide implementations and optimize value. Public Tableau dashboards with usage metrics create healthy transparency while revealing opportunities.

For example, drill-down visualizations uncover variances in documentation turnaround times across medical specialties, facility locations, or individual physicians. Granularity down to a single doctor provides clarity if their voice tech usage declined after initial training.

This helps target additional change management tactics to boost adoption in lagging areas. It also prompts exploration into supplementary voice-assisted workflows like patient bedside charting, surgery planning, and coordination.

Don’t forget the big picture, either. Aggregate metrics tracking overall hours of documentation time saved to make the enterprise-level business case for workflow automation and AI assistance.

Gamification can motivate adoption

Ranking top voice tool users by minutes saved taps into doctors’ competitive spirits while incentivizing adoption. High score lists posted in lounges or the company intranet may sound trivial, but even basic gamification drives engagement.

For example, Holy Name Medical Center saw 80% of their cardiologists actively contribute data to a quality improvement dashboard once performance visibility and monthly rewards entered the mix.

Ongoing contests, whether for the highest utilization or most accurate structured notation, can keep usage habits sticky. Public leaderboards propagate organic peer accountability and motivation far beyond what top-down policies ever could.

Protip: let your physicians choose what rewards suit them best, whether continuing medical education (CME) credits, gift cards, or donated PTO. This incentivization model simultaneously empowers doctors with control while aligning their interests with adopted desired behaviors—a winning combination!

Quantifying patient experience sentiment

Virtual voice assistants and AI chatbots are rapidly transforming the patient experience. By 2025, the healthcare chatbot market is predicted to reach $1.9 billion globally, expanding at a CAGR of 22.5%. These conversational interfaces build deeper connections through natural dialogue while providing 24/7 self-service convenience.

How can health systems measure success beyond periodic satisfaction surveys? Advanced natural language processing uncovers detailed insights from conversation transcripts.

For example, topic clustering groups virtual assistant dialogs into categories like medication concerns, appointment logistics, side effect advice, and more. Graphing trends over time shows which areas see more patient inquiries. Any spikes warrant further investigation to streamline common requests.

Meanwhile, sentiment analysis assigns emotion scores accounting for tone, word choice, and phrasing. Was the patient confident and satisfied or confused and frustrated? Identifying negative sentiments allows customer experience teams to proactively remedy pain points.

In tandem, these NLP techniques deliver unprecedented visibility into patient needs and gaps. Instead of delayed surveys, analytics on daily virtual assistant usage provide real-time feedback. This enables continuously optimized, personalized engagement.

Geospatial mapping for equal access

We all agree equitable access to care falls critically short today, especially in disadvantaged communities. This negatively impacts patient outcomes and public health alike.

Tableau spatial analytics now enable health systems to visually pinpoint service gaps or underutilization down to neighborhood levels. Plotting voice assistant adoption or virtual visit uptake over maps illustrates unseen socio-economic barriers.

Proactively identifying these access deserts allows targeted community outreach interventions with transportation assistance, local partnerships, or telehealth options. The result? Your system gets closer to truly personalized care rather than one size fits all.

Closing care access gaps through data

Health disparities persist across disadvantaged communities, critically impacting outcomes. For example, life expectancy varies by as much as 20 years between the most and least advantaged neighborhoods in some regions. Closing these gaps in care access and delivery represents an ethical and economic imperative.

Advanced analytics is finally shining a light on where needs persist. Tableau spatial visualizations enable pinpointing underutilization down to the neighborhood level. Plotting metrics like preventative screening uptake or telemetry monitoring adherence over maps illustrates socioeconomic barriers.

Drilling down further, natural language processing of voice assistant transcripts can detect access barriers. Are lower-income patients showing confusion navigating appointment logistics or transportation options? Identifying these pain points is key to developing targeted support.

Proactively discovering care access deserts this way allows health systems to intervene appropriately. Tactics may encompass transportation assistance, local community partnerships, educational outreach around virtual care options, and more.

Ochsner Health saw blood pressure control rates within their hypertension program improve from 53% to over 97% by prescribing wireless monitoring devices and using Tableau analytics to customize treatment for high-risk groups.

As a result, healthcare organizations inch closer to equitable care delivery tailored to each patient’s unique needs and environment; not just one size fits all.

Architecting a strategic voice data foundation

Hopefully, the opportunities covered illustrate why voice should be a strategic priority beyond a point solution. With mountains of data unlocked, health systems must architect their analytics foundations accordingly.

We recommend four best practices in summary:

1. Start with the highest potential pilots—surgery coordination or patient call deflection offer fast returns
2. Integrate early with downstream analytics systems like data warehouses and business intelligence tools
3. Build dashboards for tracking enterprise adoption tied to KPIs
4. Expand voice data science capabilities through internal investments or external partnerships

The future is already here

Keep in mind, the patterns we see today represent just the tip of the iceberg for the impact of voice technology’s analytics. Once ambient listening and AI become ubiquitous across clinical settings, newly available data volume and variety will transform care.

The health systems best positioned to ride this wave spent recent years strategically implementing speech recognition, structuring narrative data, building advanced analytics, and embracing innovation. With the right foundations laid, they will thrive in this data-rich future.

So what does YOUR organization look like? Will you sink or swim when each spoken conversation unlocks insights for optimizing care? The time for healthcare leaders to make this vision a reality is now.