Reports: Opioid-Abuse-Prevention

Applied filters: (33) None

The Nurse EHR Experience 2020

The Nurse EHR Experience 2020
Nurses are the lifeblood of healthcare, keeping organizations running while balancing the needs of patients and physicians. Though they are on average more satisfied with the EHR than their physician counterparts, nurses have unique EHR needs that may be overlooked when clinician EHR satisfaction is examined as a whole. Accordingly, this report focuses specifically on the EHR satisfaction feedback shared by the more than 70,000 nurses (from 189 unique organizations) who have completed the Arch Collaborative survey. These findings can help organizations pinpoint ways to better support nurses in their crucial roles.

Lauren Manzione and Anna Beyer | Friday, July 31, 2020

A Note about This Research

These findings were gathered from organizations who chose to survey nurses as part of their EHR satisfaction measurement. The respondent sample size varies from question to question as not all organizations asked their nurses all of the standard survey questions and because the standard survey has periodically been updated with new questions. To increase the granularity of our nursing data, the Arch Collaborative survey was recently updated to gather information on nurse respondents’ clinical backgrounds, including clinical nurse specialists, LPNs, medical assistants, nursing assistants, RNs, and unit clerks. While breakouts by clinical background will be included in future reports, the change is too recent to be reflected in this current research.

What Factors Have the Greatest Impact on Nurse Satisfaction?

On average, nurses tend to report higher EHR satisfaction than physicians. Among nurses, what factors have the highest impact on EHR satisfaction?

Care setting

Nurses who work in both inpatient and outpatient environments have the lowest satisfaction scores. Outpatient nurses are slightly more satisfied than inpatient nurses.

Nursing focus area

Context matters. Surgical nurses have the highest satisfaction, with scores 26.2 points higher than NICU/PICU and perinatal (mother/baby) nurses, who report the lowest satisfaction.

Satisfaction with training

Nurses with high EHR satisfaction are much more likely to report that their initial and ongoing training was helpful and effective. The difference in satisfaction scores between those with strong vs. poor initial training is 50.9, and for ongoing training the difference is 52.3. Highly dissatisfied nurses often want more training.

Satisfaction with various EHR stakeholders

The Arch Collaborative survey asks respondents to rate three different EHR stakeholders: themselves, their organization, and their EHR vendor. Nurses who rate these stakeholders highly are more likely to report higher EHR satisfaction. In particular, vendors’ EHR delivery is significantly correlated with nurse satisfaction.

Satisfaction with various EHR tools/outcomes

Nurses who view various EHR tools positively report higher EHR satisfaction than those who don’t.

Use of personalization tools

Nurses who use personalization tools tend to be more satisfied with the EHR (by 12.9 points on average) than those who don’t.

What Are the Greatest Contributors to Nurse Burnout?

About 25% of nurses report having symptoms of burnout, with the greatest contributors being a chaotic work environment, time spent on bureaucratic tasks, and a lack of effective teamwork.

percent reporting burnout by clinical background

Which Areas of the EHR Are Most in Need of Optimization?

Many EHR functions need optimization to fit nurse workflows and can impede nurse efficiency, the delivery of care, or both if not implemented or trained on effectively. Nurses identified the following key EHR functions as most likely to negatively impact their efficiency or ability to deliver care.

areas of the ehr most likely to impede efficiency care

EHR Satisfaction for Recent Go-Lives

How Does Time with an EHR Affect Satisfaction?

KLAS’ Arch Collaborative, which has collected EHR-satisfaction data from thousands of clinical end users from hundreds of health systems, has identified and documented three best practices that lead to increased EHR satisfaction. Dubbed the Three Pillars of EHR Satisfaction, these best practices include strong user mastery, an EHR’s ability to meet users’ unique needs, and an organizational sense of shared EHR ownership. This report shows that these best practices are applicable for any acute/ambulatory care organization—regardless of how recently (or not) they implemented their EHR.

Connor Bice and Cole Duda | Friday, July 10, 2020

Findings on the Three Pillars of EHR Satisfaction

Previously published Arch Collaborative reports illustrate how the Three Pillars relate to satisfaction:

The “Clinician Training” report details the nearly 90-point difference in Net EHR Experience Score (NEES)^† between clinicians who strongly disagree initial EHR training prepared them well and clinicians who strongly agree.
“Improving the EHR Experience through Personalization” found that nearly two-thirds of providers don’t personalize their EHR. Adopting this practice can have an immediate impact; 81% of organizations with high adoption of personalization tools also have above-average EHR satisfaction.
The “2019 Arch Collaborative Guidebook” highlights how organizations with EHR governance headed up by clinical, administrative, and IT leaders typically score much better than those whose EHR governance is headed by only one of these groups.

†The Net EHR Experience Score (NEES) is a snapshot of clinicians’ overall satisfaction with the EHR environment(s) at the organization. The survey asks respondents to rate factors such as the EHR’s efficiency, functionality, impact on care, and so on. The Net EHR Experience Score is calculated by subtracting the percent of negative user feedback from the percent of positive user feedback. Net EHR Experience Scores can range from -100% (all negative feedback) to +100% (all positive feedback).

Impact of Time with an EHR on Satisfaction

Overall, the longer an organization is live with an EHR, the more satisfied their physicians tend to be. The trend is much less strong for individuals than it is across organizations. This suggests that the amount of time an individual physician has spent with an EHR has less impact on satisfaction than how long the organization has been live. As a result, the rest of this report focuses on the latter measurement.

Key Finding #1:

Initial training has a dramatic and immediate impact on physicians’ EHR satisfaction, even for recent go-lives.

Arch Collaborative data shows that the quality of users’ initial EHR training (i.e., how strongly they agree that their initial training prepared them well to use the EHR) is a key indicator of EHR satisfaction. The effects of strong training are not lessened by how recent (or not) the EHR go-live was. Training is most effective when it is specific to users’ needs and delivered by high-quality educators.

net ehr experience score by agreement that initial training was sufficient

Key Finding #2:

Even amid the inevitable struggles of a new go-live, efforts to improve trust in IT and physician support can still have a significant, positive impact on EHR satisfaction.

Shared EHR ownership also impacts satisfaction regardless of go-live timing. Trust in IT (measured through clinician agreement that the organization effectively implements and supports the EHR) is the related metric that is most under an organization’s control. Organizations that went live before 2017 and those that went live in 2017 or later both see a difference in NEES of over 100 points between those who report the strongest trust in IT and those who report the weakest. Even during the inevitable difficulties of a new EHR implementation, organizations that provide strong support and take ownership for EHR success help physicians feel much more satisfied with their EHR.

net ehr experience score by agreement that leadership it supports and implemented ehr well

Key Finding #3:

Personalizing the EHR has a significant impact on satisfaction—in both the short term and the long term.

Personalization of the EHR can have a significant effect on EHR satisfaction, regardless of go-live timing. Organizations who went live prior to 2017 and those who went live in 2017 or later both have a difference in NEES of over 40 points between physicians with high personalization versus those with little or none. Some high-satisfaction Arch Collaborative members suggest giving physicians a strong foundation in EHR workflows and functions—and then introducing personalization to allow each user to tailor their EHR experience.

net ehr experience score by level of personalization

Is There a Relationship between EHR...

Is There a Relationship between EHR Satisfaction and Hospital Quality Ratings?
A common question KLAS hears from organizations participating in the Arch Collaborative is whether there is any correlation between EHR satisfaction and an organization’s performance in other measures of hospital quality. To answer this question, KLAS looked at the Net EHR Experience Score (NEES) of Arch Collaborative members who appear in at least one of five key hospital quality ratings:

CHIME’s Most Wired (levels 8–10)
CMS Star ratings (5-star organizations)
Healthgrades (America’s 100 Best Hospitals list)
Leapfrog (Top Hospitals list)
US News and World Report (Best Hospitals list)

Jacob Jeppson and Anna Beyer | Friday, May 22, 2020

A Note about This Research

Currently, 103 hospitals that are ranked in at least one of the hospital quality programs listed above have also measured clinician EHR satisfaction via the Arch Collaborative survey (only 7 of the 103 are ranked in two programs). Given this small sample, the findings in this report must be considered preliminary.

Additionally, it is important to note that there are many metrics and programs by which hospitals can measure and benchmark their performance and quality. For this research, KLAS chose to focus on five of the most well-known ratings programs, each of which bases their results on different metrics and criteria.

On Average, Physicians at Ranked Hospitals Have Higher EHR Satisfaction

quality ranking and ehr satisfaction physicians On average, physicians at organizations whose performance is celebrated in at least one of the key hospital quality programs examined in this report have higher EHR satisfaction than peers at non-ranked organizations. The relationship is weakest for organizations on the US News and World Report Best Hospitals list. While there is likely no causation in the relationship (i.e., it is unlikely that higher satisfaction with the EHR is a cause of high performance in these rankings, or vice versa), the correlation tells us that some high-performing hospitals also put strong emphasis on ensuring their clinicians achieve mastery of and high satisfaction with the HIT tools they use every day.

Nurse Experience with the EHR Follows the Physician Trend

quality ranking and ehr satisfaction nurses As with physicians, nurses who work at ranked facilities are more likely to be happier with their EHR than nurses at non-ranked organizations. There are two notable outliers to this trend. First, nurses at organizations that perform well in the Leapfrog ratings seem to be much happier with their EHR than nurses at non-ranked facilities (there is a nearly 27-point difference). Second, nurses at organizations in the CHIME Most Wired rankings are actually less satisfied on average than nurses at non-ranked organizations. This difference in satisfaction may be due to the small sample size of Arch Collaborative members that appear in the CHIME Most Wired rankings.

Other Arch Collaborative Resources Related to Quality

The case studies below provide additional quality-related learnings from Collaborative members.

Anne Arundel Medical Center: “DataBay: Google-Like Capability”
Anne Arundel Medical Center: “Integrating Data across Different EHRs”
Novant Health : “Building Effective Best Practice Alerts”

Providing High-Quality Care: The...

Providing High-Quality Care: The EHR Factors at Play
One of the biggest promises of EHRs is that they will help healthcare providers deliver higher-quality care to their patients—the main measure of healthcare success. But do they? This report shares frontline clinicians’ feedback on how well their EHR is living up to the promise of enabling high-quality care.

Matt Brunken | Friday, May 1, 2020

agreement that ehr enables high quality care

Current data suggests over 40% of clinicians are not satisfied with (or indifferent about) their EHR’s effect on care quality. Comments from both satisfied and dissatisfied clinicians suggest a few possible reasons why EHRs sometimes fall short.

**How Do Clinicians Successfully Leverage Their EHR for High-Quality Care?**

keys to ehr support of high quality care While the EHR itself undoubtedly affects clinicians’ perception of its support of care quality, user accountability is actually much more impactful. Many satisfied clinicians attribute their EHR proficiency to their ownership in mastering the EHR. Because they have accepted that the EHR will be part of patient care, they take accountability for their EHR proficiency in pursuit of improving the patient experience. In this case, initiative to learn the EHR improves perception of how well the EHR supports quality care. A smaller (but significant) percentage of clinicians talk about the EHR itself improving care, including a few who say it helps them more easily access patient information and be more efficient, and say that it provides helpful functionality like speech recognition, free-text searches, and HIE data.

**What EHR Barriers Hinder High-Quality Care?**

barriers to ehr support of high quality care Comments from clinicians having a negative EHR experience (i.e., strongly disagree their EHR enables high-quality, safe, patient-centered care) illuminate what they expect from their EHR in terms of enabling high-quality care—and what barriers and gaps they see today.

Dissatisfied clinicians mention many challenges that hinder their ability to deliver high-quality care. Most often, clinicians say the EHR distracts them from interacting with patients. Other EHR shortcomings include inefficiencies, unintuitive user interfaces, occasional downtime, and functionality gaps. For many, this results in a feeling that they are delivering “computer-centered care” rather than patient-centered care. They report that often they spend more time dealing with the EHR and its more frustrating aspects than they do interacting with the patient during a visit.

For some clinicians, quality care means efficiency—and many deem the EHR a roadblock in their efficiency. Charting requirements take up a lot of time and can limit how many patients that clinicians can care for in a day. Others feel that all EHRs are designed primarily for charge capture or billing—not for patient-care workflows—and therefore inherently don’t adequately support high-quality care.

Another common concern is patient safety, typically in the form of documentation errors or poor access to patient information. Clinicians sometimes feel the templated charting functionality in their EHR makes it difficult to either document sufficiently or understand others’ documentation. Others feel that poor access to essential patient information, like medical histories or orders, puts patients at risk. These types of accessibility issues usually result from lack of EHR integration or cumbersome user interfaces. Occasionally, clinicians also cite system downtime as a hindrance to patient safety.

Fortunately, KLAS has found that many of these issues can be combatted with not only strong user accountability but also organizational support. There are many organizations in the Collaborative who have helped their clinicians improve their efficiency, documentation habits, or access to patient information through training, increasing personalization use, or improving their integration. These efforts can be essential in improving providers’ care and keeping patients safe. For more details about these organizational efforts, see the Expanded Insights section.

Quantitative Data Trends Support the Voice of Clinicians

Quantitative measurements in Arch Collaborative data also back up what makes an EHR supportive (or not) of high-quality care. For example, some dissatisfied clinicians talk about alerts posing a threat to patient safety: as one said, the “high alert fatigue is frustrating and causes important messages to be overlooked.” Collaborative data backs this up—showing a strong relationship between the quality of alerts and how well the EHR enables high-quality care.

ehr support of quality care vs agreement that alerts prevents mistakes 60% of the variation in organizations’ average quality-of-care rating can be explained by their helpfulness-of-alerts rating (R²=0.60). When alerts prevent mistakes in care delivery (rather than causing them), clinicians feel that their EHR enables high-quality care. To learn about an organization who has helped their clinicians set up meaningful alerts, read Novant Health’s case study “Building Effective Best Practice Alerts.” Alerts are just one example of a wider trend in KLAS data that lines up well with clinician commentary on what makes an EHR supportive of high-quality care. For more details on these findings, see the Expanded Insights section.

What Can Epic's Signal Data Tell Us...

What Can Epic's Signal Data Tell Us About EHR Satisfaction and Burnout?
Through the Signal tool, organizations using Epic have easy access to a wealth of user-level Physician Efficiency Profile (PEP) data, including in-depth data on how physicians use In Basket, orders, notes, and clinical review. Provider executives use this data to inform their informatics strategies and identify how users compare to peers in the Epic community. Leaders looking to improve EHR satisfaction and reduce burnout among their clinicians may wonder, Can this data be used to identify individual physicians who are struggling with the EHR?

Jacob Jeppson | Friday, April 17, 2020

Promising Trends to Watch:

Which Signal Metrics Show Preliminary Correlations with EHR Satisfaction and Burnout?

While Signal data is not designed to be a predictor of an individual physician’s EHR satisfaction or feelings of burnout, many high-performing Epic organizations in the Arch Collaborative have found success using their Signal data to spark conversations and guide interactions with physicians (see full report for examples). Additionally, several Signal metrics do show some degree of correlation with EHR satisfaction or burnout.

The Signal metric that currently does the best at predicting EHR satisfaction and burnout is the physician’s same-day appointment-closure rate. The more appointments physicians close the same day, the higher their EHR satisfaction and the less burnout they report. Presumably, those who close more appointments per day know the tool better and are managing their workload well. However, since individual physicians’ workflows vary dramatically, using this variable as a predictor may lead to false positives (i.e., physicians who appear to be struggling but are not).

ehr satisfaction by percent of appointments closed same day

self reported burnout by percent of appointments closed same day

Other factors shown to impact satisfaction and burnout:

Time spent in the EHR: Physicians with busier schedules spend more time in the EHR, get more practice with it (measured via the Signal metrics “time per day in In Basket,” “time per day in notes,” and “time per day in orders”), and thus become more proficient. These physicians also report higher levels of burnout.
Degree of EHR personalization: Physicians that create more of their orders from preference lists and SmartSets are more likely to be satisfied with the EHR.
Use of advanced functionality: Physicians that use SmartPhrases and Epic’s mobile applications are more likely to report high satisfaction.
Percent of orders self-entered: Physicians who enter more of their own orders but can rely on team contributions when needed are more likely to be satisfied with the EHR.
Time spent in the EHR on off days/hours or at home: Not surprisingly, the less time physicians spend working in the EHR on unscheduled days, during “pajama time,” and outside the hours of 7:00 AM to 7:00 PM, the less likely they are to report burnout.

Epic’s Response: Signal Changes

Epic has used these findings and other feedback from their collaboration with KLAS to make enhancements to Signal. A few specific examples include:

Pajama time: In an effort to better quantify clinician time spent in the evenings on workdays and on days off, a new metric was created to supplement other afterhours metrics.
Level of service and diagnosis shortcuts: Previously, Signal recorded whether level of service and diagnosis shortcuts were available to clinicians. It now records how often clinicians use them.
Chart review filters: Previously, Signal recorded whether filters were available. It now records how often clinicians use them.

While Signal isn’t intended to be a predictor of burnout or the sole assessment of someone’s work satisfaction, it can be used in tandem with a strong clinical informatics program to identify physicians who would benefit most from additional training and support.

Signal Data Not a Reliable Predictor of EHR Satisfaction or Burnout . . . Yet

Today, only about 4% of the variation in EHR satisfaction^† across individual ambulatory physicians can be predicted using Signal data (data set is limited). When we look at organization averages, this percentage increases but only to 15%. Signal data is slightly better at predicting physician burnout—5% of the variation in reported burnout between individual ambulatory physicians can be predicted using Signal data; 22% can be predicted across organizations.

†The Arch Collaborative uses the Net EHR Experience Score (NEES) to measure clinicians’ overall satisfaction with their EHR environment(s). Clinicians are asked to rate factors such as the EHR’s efficiency, functionality, impact on care, and so on, and the NEES is then calculated by subtracting the percent of negative user feedback from the percent of positive user feedback. The NEES can range from -100% (all negative feedback) to +100% (all positive feedback).

Actual vs. Predicted Net EHR Experience Score

Actual vs. Predicted Burnout Rating

Next Steps: We Need Your Help!

To date, comparing Signal usage data with EHR satisfaction data from the Arch Collaborative shows no reliable predictive correlation among ambulatory physicians. To further improve these findings, Epic and KLAS need more data points. If this research is interesting to you or your organization, please fill out this form to begin the process of adding your organization’s Signal data to our study.

The Science of Improving the EHR...

The Science of Improving the EHR Experience

An Arch Collaborative Impact Report

Theories are proven (or disproven) through application. Technologies from the ham radio to the microprocessor were successful applications that ended the debate about theoretical principles. Similarly, today’s EHR experience pioneers prove that they understand the science of EHR success by measuring the feedback of their clinical staff, making improvements, and then measuring again. This report details the experiences of the first organizations in the Arch Collaborative to measure their clinicians' EHR satisfaction, make changes to improve, and then measure again.

Taylor Davis & Connor Bice | Friday, March 6, 2020

54% of Organizations Are Seeing Statistically Significant Improvements

trending net ehr experience scores of repeat respondents

What Drove Improvements?

Rush University was the largest provider organization to see a dramatic improvement: a combination of repeat and new provider ratings brought the organization’s Net EHR Experience Score up by over 21 points (n = 36 repeats, n= 469 non-repeats; p <.001). Between their two surveys, Rush communicated to providers with, among other methods, the flyer below.

With this flyer, Rush University leadership focused on delivering improvements in the three pillars of EHR user optimization:

Improved proficiency: Communicated improved support through methods such as the Provider Optimization & Experience Team (POET) program and Provider Optimization Workshops (POW), which helped improve education.

Alignment to workflows: Through the POW program, efforts were made to help providers better personalize their EHR to their specific needs. In addition, changes were made to the EHR with regard to the CMS HCC codes to better meet user needs.

Shared ownership: Open inclusion of Shafiq Rab the CIO, and Brian Patty, the CMIO, as two very engaged parties in Rush’s efforts conveyed this was not just a training push. Senior leadership made significant efforts to listen to clinical needs and respond.

Significant Improvements from Other Organizations

OrthoVirginia, a focused orthopedic group, saw Net EHR Experience Score improvement of 30+ points. They created a Provider Support Specialist program to drive proactive service while creating a solid relationship between these Provider Support Specialists and the providers themselves. The organization recognized the need for good governance when implementing any EHR changes and updated their governance process accordingly (for more details, see their case study “A Model for Improvement”).

Anne Arundel Medical Center saw their Net EHR Experience Score jump by 14 points after making two significant changes. They built a new data center, improved data infrastructure, and upgraded storage within their area network, increasing the EHR’s reliability. They also invested in their clinically integrated network and (in-house) developed functionality within the Epic platform and Healthy Planet to ensure that all providers in their community, on Epic or not, had full data exchange so that they could provide meaningful care in every encounter. Providers can reliably look at a patient’s medical record and understand the full continuum of care, regardless of what EHR the patient primary care doctor uses. This has led to increased satisfaction with EHR integration.

Edward-Elmhurst Healthcare, who had high clinician satisfaction on their first Arch Collaborative measurement, reported making very few changes between their first survey and their second; however, their Net EHR Experience Score improved by almost 22 points in satisfaction. They achieved this by continuing the efforts they already had in place based on their philosophy of eliminating or filtering out unhelpful functionalities as quickly as possible, along with a dedication to communication through the clinical support team. More changes are coming at this organization in the future; they are planning training sprints (as inspired by Dr. CT Lin at University of Colorado Hospital), encouraging clinicians to bring up development ideas, and carving out protected time for analysts and informaticists to dive into these development ideas in a collaborative manner.

One large health system saw improvements of 15 points (n=103, p <.001) as they aimed multiple initiatives at their providers and their nurses. Physicians received one-on-one training, were sent weekly education snippets, got a revamped training website, attended superuser conferences, and had a dedicated committee to review clinical decision support; the last helped remove low-value alerts (50% of existing alerts) and in turn decreased documentation time. Nurses received more trainings, saw improved workflows, received roadshow presentations for any upgrades, and implemented a new process for enhancements.

Lehigh Valley Health Network saw significant improvements among providers (15-point increase in Net EHR Experience Score) after they increased at-the-elbow support and focused on optimization after an EHR upgrade. They also hired additional informatics staff. Unique from many other organizations who have re-surveyed, this health system also expanded their use of scribes and saw a significant increase in satisfaction among their repeat survey takers.

University of Kansas saw scores improve among their providers by 7 points, driven by improved communication from the informatics team regarding upgrades and optimizations (for an example, see this video). They also created the Epically Efficient trainer-credentialing program focused on training and personalization. And they implemented a third-party tool to quantify chart fatigue and determine how long it took clinicians to read through an alert, then reduced the alert volume. In addition, they started a new technology governance process where they strategically prioritize a road map of EHR areas to work on.

The largest organization so far to see significant improvements rolled out a superuser program, created a change agent role, invested in a communication tool, and made a significant number of EHR enhancements.

Why Did Some Not See Improvements?

Some organizations did not see the improvements they hoped for, often because of the negative impact of another factor within the organization.

One organization saw a slight drop in satisfaction scores. While this organization had invested in more education and training, a series of downtimes before the second survey left providers dissatisfied with their EHR experience and questioning their trust in the organization’s IT and informatics teams.

Another organization made significant investments into EHR education but still saw increased dissatisfaction. Providers reported they were increasingly asked to work late hours, and burnout spiked. Trust of organization leadership (not just IT/informatics) dropped overall.

One organization with very high clinician satisfaction in their original measurement (in the top 5%) saw a slight decrease in satisfaction as the result of an upgrade that removed key functionality providers relied on. The decline in satisfaction was small because of broad, clear communication in preparation for the change.

At one academic health system, the only organization to see a statistically significant drop in satisfaction, nurses reported increased frustration with poor support and communication. Providers also reported a decrease in satisfaction because they were receiving too many functionality changes in a short period of time without sufficient assistance.

Other organizations made a specific push to improve the EHR but only improved in one area, or they chose to not focus on making improvements.

One academic health system reported that they made system enhancements and were beginning to focus more on personalization. They did not undertake significant training, workflow, or governance improvements.

Another organization with strong clinician satisfaction was in the process of making significant changes in an EHR upgrade that affected clinicians’ workflows. This organization focused on strong change management and communication so that their clinicians’ experience would not deteriorate. After these significant efforts, this organization achieved their goal of steady satisfaction throughout the changes.

All Four Training Sprint Groups Saw Improvements

Inspired by University of Colorado’s training sprints, four organizations made targeted improvements with smaller groups of clinicians to try to drive improved EHR satisfaction. These sprints are attempts by organizations to target a specific group (i.e., a single specialty or clinic) and hone in on their training for a short period of time. A sprint usually includes analysts and trainers observing the providers as they work, planning time to develop individualized training for the providers, and then giving one-on-one instruction for a set period of time (usually 2–4 hours). These sprints rarely involve technical changes at the time; rather, they focus primarily on workflow efficiency and increased personalization training.

Summary

Provider organizations that think they cannot improve their EHR experience now have scientific data to rely on that demonstrates that improvement is not only possible, it is happening. It is within their reach to significantly improve on the clinicians’ experience. In order to drive improvements, organizations must first deliver an EHR foundation that is functional, stable, reliable, and responsive—and multiple EHR solutions are capable of being that type of foundation today. Then leaders must engage in user optimization efforts, focusing on aligning workflows through study and personalization, investing in user proficiency, and creating a sense of teamwork through shared EHR ownership.

For more insights, read the Expanded Insights section of this report, which dives deeper into these questions:

What changes that organizations made were the most effective at driving change?
What were the differences (if any) in improvement depending on organization type or size?
What were the differences between provider and nurse satisfaction improvement efforts and outcomes?
What types of organizations saw the greatest gains in satisfaction? Who was left behind?
Which aspects of the EHR have seen the most improvement? What areas continue to lag?

Are EMRAM Stage 7 Nurses More...

Are EMRAM Stage 7 Nurses More Successful?

A 2020 KLAS-HIMSS Analytics Collaboration

Health information technology is a commonly cited source of clinician frustration and burnout. But what about clinicians at organizations that have reached the peak of EHR adoption—HIMSS EMRAM Stage 7? Do their clinicians report more or less EHR frustration? In 2019, KLAS (in cooperation with HIMSS Analytics) examined this question as it relates to physicians. This 2020 follow-up explores how an organization’s EMRAM stage impacts EHR satisfaction among nurses.To conduct the analysis, KLAS analyzed the Arch Collaborative survey responses of over 34,000 nurses from hundreds of individual hospitals. Throughout this report, responses from the nurses are grouped by organization, with each organization designated as either Stage 7, Stage 6, or Stage Not Validated, depending on the publicly available information from HIMSS Analytics.

Bryant Wood and Connor Bice | Thursday, February 6, 2020

HIMSS Analytics is a global healthcare information and technology standards organization assisting regulatory bodies and healthcare providers in making lasting improvements in efficiency, performance, and care outcomes. Offering a suite of Maturity Models led by flagship model EMRAM, HIMSS Analytics provides prescriptive frameworks to healthcare organizations allowing for global comparability, benchmarking, and strategic clinical and financial improvement. HIMSS Analytics' Certified Consultant Program offers professional training for vendors to educate and build strategy around the Maturity Models. HIMSS Analytics is a wholly owned subsidiary of HIMSS (Healthcare Information and Management Systems Society). For more information, visit www.himssanalytics.org.

The Arch Collaborative is a global, provider-led effort to improve EHR usability and satisfaction through consistent measurement, benchmarking, and collaboration around best practices. To date, over 150 provider organizations have measured the feedback of their clinical EHR users and have begun to take steps to improve the EHR experience. If your organization is interested in maximizing the value of your EHR, learn more at https://klasresearch.com/arch-collaborative or email us at archcollaborative@klasresearch.com.

Finding the Right Recipe for...

Finding the Right Recipe for Documentation

Speech Recognition, Scribes & Other Methods—Impact Report

Many healthcare organizations wonder what recipe will lead them to EHR documentation success. Are scribes a cure-all? What factors lead to success with speech recognition? And should organizations still use dictation?

Taylor Davis and Connor Bice | Wednesday, December 4, 2019

Based on the feedback of 37,718 physicians from 202 organizations, this report examines the impact of various documentation “recipes” on the following satisfaction and efficiency metrics:

Net EHR Experience Score (NEES) (EHR satisfaction rating)

Burnout rate (percent of physicians reporting burnout)

Self-reported ambulatory same-day chart-closure rate

Self-reported inpatient same-day chart-closure rate

Satisfaction with personal documentation quality

Satisfaction with peers’ documentation quality

Documentation Recipes

To determine how an organization’s documentation recipe is likely to impact EHR satisfaction and efficiency, each measured EHR deployment in the Arch Collaborative was sorted into one of eight groups depending on the organization’s average adoption level (as self-reported by physicians) of speech recognition, scribes, and dictation (adoption is considered high if it is above the median). While satisfaction and efficiency vary across these groups, not all variation is statistically significant. Overall, documentation method is a poor predictor of EHR experience and provider burnout (mathematically has only a limited impact). Statistically significant correlations are called out in future pages.

organization ehr satisfaction and efficiency by documentation recipe

Key Takeaways

Speech recognition works best when utilized on top of strong EHR proficiency. Speech recognition adoption must include strong change management and robust education.

While many physicians expect scribes to solve their documentation challenges, organizations that invest in scribes do not usually report significantly better outcomes. The few exceptions are organizations that ensure their scribes are well trained and that expect providers using scribes to be highly proficient with the EHR.

“A provider who does not know how to use the EHR will not know how to use speech recognition—or a scribe—to make the EHR easier to use.” —3M M*Modal Insights, pg. 7

“Done right, new technologies can alleviate administrative burdens for providers; done wrong, they may magnify administrative burdens, with the onus on organizational leadership for deployment, training, and support.” —Dr. Robert Budman, CMIO, Nuance Communications

Shifting the Paradigm

Surprised by the lack of impact that documentation method has on EHR satisfaction and clinician burnout? KLAS encourages organizations to consider that how these technologies and services are implemented can play a significant role in how well they move the needle.

As noted above, the success of scribes is often dependent on the quality of the training and the expectations set for providers. Similarly, use of speech recognition technology today is not always accompanied by sufficient user education and workflow redesign. Though the technology is likely to change drastically in coming years (thanks to the development of ambient intelligence), KLAS asserts that proper training and user assistance will still be important success factors.

Outcomes by Recipe

organization documentation recipe and outcomes

After controlling for the EHR in use, KLAS found the following correlations at the organizational level:

Organizations with higher use of dictation also have lower EHR satisfaction (p<.01).

There is no statistically significant correlation between documentation method and burnout rate.

Physicians at organizations with higher speech recognition adoption report higher same-day chart-closure rates (p<.01).

Physicians at organizations with higher dictation rates view their peers’ documentation as being of lower quality (p < .01).

Overall, the most effective organizations focus on only a couple of documentation methods (including personal entry and excluding dictation), likely because specializing in a couple of documentation methods allows for better support and training. Organizations with dictation or with many means of documentation see lower success.

Don’t Just Check the Box for Speech

Over 2,000 of the surveyed physicians commented on their speech recognition technology, with 45% reporting frustrations. The high number of positive comments indicates that the technology can be utilized well when accompanied by excellent EHR proficiency and speech recognition education. The quotes below are representative of the most common sentiments about speech recognition:

provider perceptions of speech recognition

“I LOVE the new [version of our speech recognition solution]. I feel like after just a few days, it knows me better than the other version did after years of me yelling at it.”

“My biggest complaint is with [our speech recognition solution]. It’s not reliable. It shuts down and crashes Epic on a daily basis. I expected this to be resolved with the latest Epic upgrade, but it was not. I usually stop using [the speech recognition solution] for the day if it crashes. The cycle repeats itself the next day. When it works smoothly, it makes the workflow more efficient.”

“Please provide the opportunity for ongoing, hands-on, at-the-computer training for both [our speech recognition solution] and Epic. I believe giving employed physicians the opportunity to observe Epic-savvy staff use and optimize the system would be a great financial investment for the corporation. Additionally, having an optimization-minded advocate from our organization looking at user problems and resolving them with Epic would really reduce job frustration and improve user efficiency.”

The data shows that the use of speech recognition technology does not guarantee documentation success—an organization can achieve very strong or very weak results depending on how the technology is implemented and how well users are trained. With how speech recognition technology is commonly implemented today, providers often report better efficiency, similar or lower EHR satisfaction, and sometimes lower confidence in their own documentation. In general, physicians report better outcomes when organizations make an all-in bet with speech recognition (i.e., don’t use scribes or dictation).

Scribes

Among those using scribes, the differences are significant depending on how the data is analyzed.

The physicians who are given scribes are often the most frustrated, so it is not surprising that as a group, they report lower EHR satisfaction and worse chart-closure rates than their peers who use other documentation methods. However, the longitudinal data indicates that these metrics can improve with use of a scribe. Next year, KLAS will be able to show trends in how scribes impact physician burnout rates.

In Summary

It is increasingly clear that organizations looking for a quick fix to their EHR satisfaction challenges, either through scribes or speech recognition, are likely to be disappointed. Those that adopt these technologies and processes in tandem with strong change management and strong EHR proficiency will likely see efficiency gains.

It should be noted that the results achieved today could change as ambient intelligence supplements speech recognition technology, as organizations adopt better implementation and training programs for their technology environment, and as organizations improve how they implement scribe programs. As these technologies and services evolve, the Arch Collaborative will continue to monitor their impact and provide insights into how they can best be implemented and what results organizations can expect.

Achieving EHR Satisfaction in Any...

Achieving EHR Satisfaction in Any Specialty

Impact Report

Insights shared by over 30,000 physicians reveal that EHR satisfaction is highly variable across specialties. For example, pediatrics physicians have an average Net EHR Experience Score (a measurement of overall satisfaction similar to a net promoter score) of 24.0, but other specialties report much lower satisfaction (e.g., cardiology ^† with a score of 1.6).

Matt Brunken & Connor Bice | Wednesday, October 23, 2019

† Pediatrics and cardiology are cited here and in later paragraphs instead of other, more extreme examples due to their larger sample sizes and to avoid overemphasis on potential outliers in Collaborative data.

One critical factor in the difference between specialties’ satisfaction is the quality of initial training. Collaborative data shows a correlation between high physician agreement that initial training prepared them well and high agreement that the EHR has functionality needed for
their specialty.

However, while organizational efforts around training can help mitigate functionality issues, some specialties simply don’t have needed functionality in their EHR, even with great training. One common complaint is that the size or scope of the specialty does not match up with the EHR’s intended user base; for example, one dermatologist described the EHR as “overly bulky for outpatient specialty care.” Another issue is that the EHR does not support specialty-specific clinical workflows; one ophthalmologist said their EHR doesn’t meet the needs of clinicians who “need to be able to document with drawing or images.” Other physicians, notably orthopedists, have concerns with things like specialty-specific templates/order sets, slow response times, or an unintuitive user interface.

average net ehr experience score by specialty

To determine how much of the physician EHR experience is reliant on training compared to the product itself, the following charts compare Cerner and Epic users (the two largest customer bases in Collaborative data), showing how training impacts their EHR experience relative to meeting specialty-specific needs.

Compared to Epic users, Cerner users tend to agree less that they have needed specialty-specific functionality, even when controlling for training quality. However, ophthalmology and dermatology stand out for lacking needed functionality regardless of EHR. This highlights EHR vendors’ responsibility to pay special attention to struggling specialties and ensure the EHR includes the functionality these specialties need to succeed.

While these findings may lead some less-satisfied physicians to believe that they can’t succeed with the EHR because of their specialty, trends within specialties debunk this assumption. In many cases, physicians in lower-scoring specialties who have had high-quality training report high overall EHR satisfaction. For example, cardiologists with strong initial EHR training have a Net EHR Experience Score of 58.3, well above the overall physician average. Conversely, physicians in higher-scoring specialties who receive poor training report low satisfaction; poorly trained pediatricians have a Net EHR Experience Score of -21.6.

While cardiology and pediatrics are vastly different specialties and use the EHR in different ways, this analysis
shows they are similarly susceptible to the positive effects of high-quality EHR training and the negative effects
of poor training.

vendor performance snapshot For more insights on how other factors such as EHR personalization and organizational practices affect variation in EHR satisfaction, see the Expanded Insights section of this report, which examines several of the most- and least-satisfied specialties.

While there is variation in the typical experience of different specialties,
no specialty is immune to the effects that high-quality training, strong culture, and personalization can have on EHR satisfaction. The insights in this report show how physicians can rise above their specialty’s EHR limitations by learning effective methods of improving their EHR experience, regardless
of their specialty.

Clinician Training

Clinician Training
KLAS Arch Collaborative Report Clinician Training - Follow-up EHR Education/Training

Bryant Wood & Anna Beyer | Friday, September 6, 2019

Why Provide EHR Training?

Clinicians are hungry for more information about the EHR. They want to be able to master it to make their lives easier and their patients’ lives safer. One of the earliest findings of the Arch Collaborative was that initial training makes a significant impact on clinicians’ EHR satisfaction. There is a 90-point difference in Net EHR Experience Score between clinicians who strongly agree their initial EHR training prepared them well and those who strongly disagree.

Who Should Be Involved in Training?

There are many options for who can effectively lead initial and ongoing training, but there is no single right answer for who can do it best. The key is rather to focus on the quality of trainers and where they can most effectively train.

How Much Training Is Needed?

Feedback from EHR users suggests that 5–8 total hours of initial training and 3–5 hours of ongoing training per year sets clinicians up for success. It is important to remember that training shouldn’t be so time-consuming that it contributes to clinician burnout. Instead, organizations should plan and communicate about training sessions ahead of time—this improves both attendance and overall clinician engagement in training.

What Methods of Training Are Most Effective?

Organizations can use a variety of types of training and classes for onboarding training or ongoing education to educate clinicians on the EHR. Having a rich mix of content-delivery methods appears to be most impactful on clinician’s overall EHR satisfaction. Some training practices are shown to be more helpful—for example, in-person training, rounding, and training on personalization tools and specialty-specific workflows.

Where Should Training Be Held?

Finding the most effective location to host training sessions can be difficult. Collaborative data shows that while some locations have been shown to support strong training, the location of training is less impactful than the quality of training. There has been evidence from a handful of organizations that holding trainings off-site can be highly effective, though this method can be more difficult to carry out.

Clinician Burnout

An Arch Collaborative Impact Report 2019

The Arch Collaborative’s primary focus is improving the EHR experience, and this experience involves the wellness of the clinician. Therefore, included in this study are two questions derived from the American Medical Association’s (AMA) Mini Z study. These two questions are correlated with the Maslach Burnout Inventory, which helps to properly identify when a clinician is burning out. All burnout findings in this report are shared through the lens of the impact of burnout on EHR satisfaction.This report is not a comprehensive analysis of the causes of clinician burnout, nor are its recommendations intended to be the primary solutions for this complex problem. Instead, this report is intended to show the developing relationship between burnout and EHR dissatisfaction. KLAS hopes the following insights add to a broader solution for taking care of those who continually offer care to others.

Connor Bice | Friday, August 2, 2019

Arch Collaborative data indicates that 28% of providers report some degree of burnout; this is a lower percentage than industry reports on burnout. One possible explanation for this is that organizations who participate in the Collaborative have already proved a willingness to measure their clinicians’ satisfaction and are therefore among the more progressive organizations in attempts to understand the daily experience of clinicians. This proactive approach and possible self-selection bias may make a difference in clinicians’ wellness experience.

A strong Net EHR Experience Score is correlated with lower burnout; the more satisfied providers in a certain organization are with their EHR, the less likely it is that many providers in that organization are experiencing burnout.

net emr experience score vs physician burnout rate

The second question asks, “What are the primary contributors to your feelings of burnout (if any)?” The most common contributor to provider burnout is too much time spent on bureaucratic tasks, followed by the impact of the EHR or other IT tools on efficiency, and then a heavy after-hours workload.

Key Takeaway

There is a correlation between clinicians’ Net EHR Experience Scores and burnout, but the Arch Collaborative has not seen an R² greater than 0.2, indicating that at most, the EHR experience explains only 20% of the variation in clinician burnout. The takeaway from this report should be that burnout extends beyond the EHR. Best practices that lead to higher EHR satisfaction may help, but focusing solely on the EHR is unlikely to eliminate burnout.

Arch Collaborative Guidebook 2019

Arch Collaborative Guidebook 2019
A product of the May 2019 Arch Collaborative Summit, the Arch Collaborative Guidebook lays out the best practices identified in Collaborative data and shared by the most successful organizations in the Collaborative.

Taylor Davis and Connor Bice | Thursday, July 25, 2019

Guidebook Participants

KLAS is grateful for the dedication and participation of several leaders who have given their time and insights to ensure that the information in this guidebook is accurate and helpful. We commend and thank the following individuals for their guidance:

Onboarding EHR Education Panel

David Niland, Medical Information Officer at Galway Clinic
Mike McNamara, CMIO at Kaiser Permanente Northwest
Heidi Garzo, EHR Training Manager at Petaluma Health Center
Sean Johnson, VP of Applications and Analytics at Sansum Clinic

Ongoing EHR Education Panel

H.C. Eschenroeder Jr., Orthopaedic Surgeon and CMIO at OrthoVirginia
Benjamin Suratt, Vice Chair of Medicine for Academic Affairs at University of Vermont College of Medicine
David Michael, CMIO at Vidant Health

Clinician Relationships & Communication Panel

Corey Joekel, CMIO at Children’s Hospital & Medical Center
David Kaelber, CMIO at The MetroHealth System

Shared Ownership & Governance Panel

Dhrumil Shah, CMIO at Compass Medical
John Lee, CMIO at Edward-Elmhurst Health
Jean Adams, CNIO and ACIO at Geisinger Health System
David Graham, CIO at Memorial Health System

Clinician Efficiency & Personalization Panel

Amy Chaumeton, Clinical Vice President and CMIO at Legacy Health
Keith Griffin, CMIO at Novant Health
Melissa Jost, Manager of Clinical Informatics at UC Davis Health
Eric Cheng, CMIO at UCLA Health

Clinician Wellness & Reducing Burnout Panel

Thomas Moran, VP and Chief Medical Information Executive at Northwestern Memorial Health Care
Paul Testa, CMIO at NYU Langone Health
Vicki Laurie, Consulting CIO at Tenet Healthcare
Neal Chawla, CMIO at WakeMed

Nursing EHR Success Panel

Ryan Zulkoski, Manager of Nursing Informatics at Children’s Hospital & Medical Center
Teresa Niblett, Director of Clinical Informatics at Peninsula Regional Medical Center
Ellen Pollack, CNIO at UCLA Health
Samantha Richards, VP and CNO at WVU Medicine

Opioid Abuse Prevention Panel

Laura Polito, Assistant Medical Director of Health Information Systems and Urgent Care Services at Sansum Clinic
Rob Schreiner, President at WellStar Medical Group
David Rich, CMIO at WVU Medicine

What Is the Arch Collaborative Guidebook?

The Arch Collaborative Guidebook is the result of thousands of hours of combined effort. Over 190 provider organizations around the world have measured the feedback of their clinicians, and the 100,000 clinicians who have participated by completing Collaborative surveys have dedicated an estimated 1,600 hours total to sharing their voices.

This incredible effort has allowed the Arch Collaborative to identify those organizations with the most successful EHR users. Leaders from these successful organizations have partnered with KLAS while also utilizing Arch Collaborative research findings to build this guidebook for EHR success.

There is no checklist for strong leadership; similarly, while this guide could be read as a checklist, it will be most effective when read as principles and opportunities for success meant to be skillfully applied according to an organization’s specific culture and circumstances.

This guidebook lays out the best practices identified in Collaborative data and shared by the most successful organizations in the Collaborative. If your organization is struggling with EHR satisfaction, start here!

It is expected that the principles in this guidebook will mostly remain consistent in future editions, but this guidebook may see significant additions over time as the Arch Collaborative continues to learn the science behind EHR success.

The principles in this guide come from two sources:

Evidence-Based Practices: Best practices validated by Arch Collaborative research that differentiate the high-performing organizations or have been documented to help organizations improve.

Leading Practices: Commonly reported keys to success as identified by leading organizations, though not yet broadly validated or too unquantifiable to fully validate.

Improving EHRs Globally

Today, achieving EHR satisfaction is a global endeavor as digitization of patient records becomes more and more common throughout the world. This report aims to increase awareness of the similarities and differences in EHR challenges faced by US and non-US health systems. To date, 189 organizations across the globe have measured the feedback of their clinicians (totaling >90,000 responses) and have received benchmarking data comparing them to similar organizations. 13 of these organizations are outside the United States. Do non-US health systems have a different perception of their EHR compared to US organizations? And if there are differences, what can each group learn from the other?

In most ways, US and non-US health systems have similar experiences with their EHRs. For both groups, system reliability and internal integration are the two aspects of the EHR generating most satisfaction. The areas where more significant differences appear are efficiency (non-US health systems

Connor Bice | Friday, June 28, 2019

us and non us health systems ehr satisfaction

While there are some key differences, like the impact of EHR personalizations, KLAS has confirmed that non-US health systems follow Arch Collaborative trends for two of the three pillars of success: EHR education and a culture of shared ownership.

When it comes to initial training, US and non-US clinicians have very similar experiences. Just over half of the clinicians in each group agree initial training prepared them well; US clinicians are just slightly more likely to strongly agree.

This trend persists for ongoing training, which US and non-US clinicians agree is sufficient in equal numbers (53% in each group). Again, US clinicians are slightly more likely to strongly agree ongoing training is sufficient.

Clinicians’ relationships with and trust in IT leadership are similar throughout the world. Building a symbiotic relationship and creating shared ownership between the clinicians who use the EHR and the IT and informatics personnel who support the EHR is essential for achieving EHR satisfaction, no matter the geography.

The biggest difference between US and non-US health systems is in adoption of EHR personalization tools. In the US, personalization tools help clinicians get data in and out of the EHR more quickly in the face of stringent documentation requirements. Outside the US, these requirements are less burdensome, so clinicians need less EHR personalization to be efficient. However, if the global community were to see increased regulatory burdens similar to those in the US, the use of EHR-personalization tools would likely have a significant impact on EHR satisfaction.

2019 Summit Slides - Arch...

2019 Summit Slides - Arch Collaborative Learnings Part 1
With more responses from over 100,000 clinicians from more than 190 organizations, KLAS and the Arch Collaborative team have learned a ton! So much that we had to split the findings into two sessions. Here is part 1.