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How to Turn Down the Boil on Group Conflict

12 Dec 2019|by Michael Blanding

Intergroup conflict can grind office productivity to a halt. Jeffrey Lees discusses how understanding psychological stereotypes can help divided parties compromise.

Even as polarized political discussion appears to have frozen the possibility of compromise, new research suggests that divided sides can come together on many issues to make decisions.

“Our research finds that inaccurate beliefs really drive behavior and contribute to intergroup conflict,” says Jeffrey Lees, a doctoral candidate in Organizational Behavior and Psychology at Harvard Business School.

In actuality, most people have a wildly inflated sense of just how negative the other side feels, according to a new paper that Lees co-wrote with Harvard University Associate Professor of Psychology Mina Cikara. “If you forecast that no matter what you propose, the other side will hate it, then you are going to say compromise is a waste of time,” Lees says.

“People not only have stereotypes of what other people are like, they also have stereotypes of what other people believe.”

The paper, Inaccurate Group Meta-Perceptions Drive Negative Out-Group Attributions in Competitive Contexts, was published in November 2019 in the journal Nature Human Behavior.

We want to compromise

In a series of experiments, Lees and Cikara found people are much more willing to compromise, but resist trying because they think those on the other side—and even those within their own group—will resist going along. But they also unearthed good news on how mistrust can be overcome on many issues.

Lees first started considering these dynamics in a business context. “I was thinking about how people inside organizations predict how people outside of the organization perceive it, and how they might get that judgment wrong,” Lees says. “It didn’t take me long to realize how that sort of judgment applies in other contexts.”

He teamed up with Cikara, whose lab has looked at how people’s perceptions of others changed based on whether they think of them as individuals or groups. “How we attribute motives to other people becomes distorted when we stop thinking of them as individuals and instead move to a framework of ‘us versus them,’” Cikara says.

In a political context, that can quickly lead to conflict.

“People not only have stereotypes of what other people are like, they also have stereotypes of what other people believe,” Cikara says. “‘They hate us for our freedom,’ or ‘they think we’re liberal snowflakes,’ or ‘they’re doing that to be obstructive,’ or ‘they want to ruin our American way of life.’ But when you actually talk to people about their opinions, almost nobody actually talks like that.”

Lees’ and Cikara’s experiments found most people are much less negative than the stereotypes we harbor about the other group. For each experiment, the researchers presented real-world scenarios that advantaged one side or the other, and then asked participants to predict how negatively the other side would react.

For example, one scenario presented to participants who identified as Democrat, explained that Democrats in a state legislature were considering a change to committees that draw voting lines. While currently, committee members were appointed by the governor, a Republican, the new proposal would allow equal representation by both parties.

They then asked participants to predict on a 100-point scale how much Republicans would dislike or oppose the measure or consider it politically unacceptable. Responses averaged in the 80s, with the largest clump at 100.

As negative as possible

“The forecasts were pretty much as negative as possible,” Lees says. In reality, however, the real responses were closer to 50—showing that participants overrated how badly the other side would feel.

Lees and Cikara found similar results for other scenarios, involving changes to selection of judges, campaign financing, and renaming of a state highway. (The researchers purposefully stayed away from “hot button” issues such as gun control and abortion, which might spur too much passion.)

The results were consistent for both Democrats and Republicans, or even if they just presented an anonymous “Party A” and “Party B.”

“They are totally insensitive to the scope or impact of the issue,” Cikara says. “They just think the other side is going to be upset about anything.”

Even more interesting, people made the same forecasts about others in their own group, believing their fellow Democrats or fellow Republicans were angrier about a measure, even when they themselves were only mildly opposed.

Having such polarized views of both political parties naturally leads to less willingness to negotiate and compromise, Lees says.

“If you are a legislator, you are thinking no one across the aisle or in my own tribe will support compromise, but that’s in fact wrong. Both sides might be okay with compromise, but no one’s willing to propose it because of inaccurate forecasts.”

Overcoming bias to reach cooperation

The news from their experiments wasn’t all bad. When the researchers flipped the script to create scenarios that were cooperative, study participants were much more accurate in their predictions. For example, in the voting districts scenario, the researchers told participants that it was Democrats who were proposing the change to make the commission fairer, even though a Democrat was currently in the governor’s office and stood to lose advantage through the change.

In that case, Democrats and Republicans alike accurately predicted how both sides would feel.

“Suddenly, people’s forecasts become accurate, which is quite an optimistic finding for cooperation,” Lees says. “If you can actually engender cooperation, people are much more likely to have accurate perceptions that might drive reconciliatory behavior.”

In a final experiment, Lees and Cikara showed that people could change their perceptions when confronted with new information. After making their own predictions about the negativity of the other side, participants were shown their true level of opposition—on average, much lower than they’d assumed. Afterwards, people decreased the degree to which they thought the other side was engaging in purposeful obstructionism.

“How we attribute motives to other people becomes distorted when we stop thinking of them as individuals and instead move to a framework of ‘us versus them.’”

“There’s a lot written about how people are totally insensitive to the truth when told that their beliefs are wrong,” Lees says. “This suggests that’s not the case. People are willing to update their beliefs when they are simply told they are inaccurate.”

Better business outcomes

This finding, which indicates the potential for creating cooperation, carries implications for business as well.

“In the context of teams or negotiations, adopting a competitive mindset can lead to undue pessimism about how others feel,” Lees says. “These inaccurate beliefs can lead to missed business opportunities. But if those contexts are reframed as cooperative, accurately forecasting how someone across the negotiation table might respond to a particular proposal becomes easier.”

That’s good news in a society that often seems to be grappling with intractable partisanship on every issue. While some issues may still present a gulf too wide to bridge, the study shows that there is at least some room for compromise and mutual understanding between the parties, if they can just start talking to each other.

“When you’re not talking about hot-button issues, you shouldn’t be afraid to broach the topic with people who have a different position than you,” Cikara says, “because it turns out you most likely have an inaccurate perception about what they think—and they have the same of you. All it takes is one person to break the cycle.”

About the Author

Michael Blanding is a writer based in the Boston area.

10 Rules Entrepreneurs Need to Know Before Adopting AI

11 Feb 2020|by Rocio Wu

Business leaders are just beginning to adopt artificial intelligence and machine learning into their operations. Rocio Wu offers insights into how entrepreneurs can start riding the wave.

Although adoption of artificial intelligence (AI) and machine learning (ML) for the enterprise is still in the early days, the technology has matured enough for entrepreneurs to start gathering inspiration and evaluating opportunities for potential applications.

Every day, processing capabilities for neural networks increase, as does accessibility to off-the-shelf APIs from big tech and academic institutions that help speed up innovation. Entrepreneurs have also learned the wisdom of targeting AI applications toward specific, well-defined business problems, rather than trying to sell generalized toolkits to business users or get bogged down in custom software consulting engagements that solve nonrepeatable use cases.

There are more opportunities today for entrepreneurs to focus on solving vertical problems than creating horizontal solutions. It is in the ethos of established tech companies to build generic solutions for customers across industries. But for challengers, the more they can focus on solving core business problems, the more successful they will be. They can understand customer needs deeply and customize product features based on their client’s specific pain points. As a result, the customer will see more business value in the solution and be more likely to convert to a paid customer. (An added bonus for the startup: lower customer-acquisition cost.)

Still, it’s proven difficult for entrepreneurs to write successful AI strategies. After all, the technology is a moving target, potential customers are wary of costs and implementation complexities, and use cases, while powerful, are still lacking in many areas.

Take advantage of uncertainty

All this uncertainty is a fertile breeding ground for entrepreneurs ready to make their mark at the start of the digital enterprise era. Here are 10 rules of thumb to consider as you develop an AI strategy in an established company or sinking roots as an AI-first venture. Some are unique to AI-first startups, while others are generally applicable to enterprise Software as a Service (SaaS) companies.

    1. Understanding the business problem you are solving is at least as important as your algorithms. Even though the technology and data science behind AI is what makes these applications different from conventional software, your business customers are not looking for technology per se. They want solutions to solve problems. Positioning your service or product as “AI for health care” or “AI for sales” is not nearly specific enough. While you can sell AI tools to data science teams or IT departments, business leaders want to know you understand their problems and opportunities intimately, and that your solution is tailored for their situation. Artificial intelligence should enable better solutions.
    1. Is the market ready to support your solution? As more of the world gets digitized, “datafication” (our ability to capture data from many aspects of the world have never been quantified before) continues to accelerate. The problem: Especially in legacy industries such as health care, manufacturing, and agriculture, much of the data is not digital and unstructured, increasing significantly the effort required to extract, clean, normalize, and wrangle. Before starting an AI strategy, determine the digital maturity of the industry in terms of adopting AI. Is the industry mature, with infrastructure already in place to collect data and ready to implement? Are industry stakeholders willing to adopt AI? Do they agree with you on the value of potential benefits? Is the product intuitive enough to speed users through the learning curve? Are there regulatory hurdles to overcome?
    1. Develop your data strategy from day one. Training machine-learning models to improve based on experience often requires large amounts of high-quality data, so it’s extremely important to lay out your data strategy from day one—including how things like data sourcing, volume, diversity, privacy and security will be handled. Data can be acquired in a number of ways including crawling public data, finding data-rich partners, gathering it from customers, or generating it internally. Each has its own pros and cons and their application may be best suitable at different stages. Data strategy is a strategic business decision that entrepreneurs need to define from the start.
    1. Even if your AI is brilliant, your product still needs great user experience (UX), the right workflow, and robust reporting. You will win not because your AI is superior, but because the end-to-end product  is better. Focusing on serving your customers’ end users should be baked into the team’s DNA. In most cases, you are building more than the ML product, so collaboration and coordination is required across functions and among both frontend and backend software engineers and UX designers.
    1. AI can be magical, but sales still win. Fred Wilson, venture capitalist and popular blogger, holds the view that “marketing is for companies who have sucky products.” Similarly, many AI founders believe that if the product is amazing, it should sell itself. However, that’s not the reality of the enterprise world. Big enterprises by default are averse to change unless they are convinced the alternative is worth their business development effort and the time involved of the legal-finance team to negotiate contracts and switch to a new vendor. Value experience in your sales and marketing team, especially if they come from the industry or companies on your target list. Nowadays functional leaders have more power than in the past to decide which software to use, so founders need to figure out an entry point to the enterprise.
    1. Be careful about “AI-first” messaging in marketing. Given the hype around AI that has raised everyone’s curiosity, an AI-forward positioning might be an effective strategy to get a first meeting. However, when it comes to actual buying decisions, customers do not really care whether it has AI inside or not. Some startups have actually removed AI from their marketing and sales messages. While it might not make sense to lead with AI, there’s value in weaving it through the product presentation, especially when it comes to transparency and explaining the underlying machine-learning algorithms.
    1. Avoid the “science project” trap. What’s the MVA (Minimum Viable Algorithm)? As the saying goes, “perfect” is often the enemy of the “good.” Business strategy has come to embrace the power of injecting a minimally viable product into the market quickly and iterating based on real-world feedback. Likewise, AI projects should similarly strive to develop and quickly market a minimum viable algorithm. This approach may take some convincing; the DNA of founding technical teams is often around solving technical puzzles and increasing accuracy from 90 percent to 95 percent. Many customers are not going to see the difference, but they will notice as the product improves from release to release.
    1. Manage customers’ over- and under-expectations. When it comes to successfully deploying AI in the real world, half of the battle is over expectation management and communication. Customers often overestimate your AI’s effect, thinking of it as superhuman—especially if AI is solving complicated problems such as 100 percent accuracy in self-driving cars and medical diagnosis. You have to help them understand that the performance of ML products improves over time (it’s machine learning after all) and unlikely to deliver perfect results in the beginning. Underestimates can happen as well if AI is solving a constrained problem like back-office automation or insurance claims. Helping customers understand which problems can and cannot be solved with AI is vital, just as Lemonade Insurance, which uses AI and other technologies to determine coverages and set rates, explained very clearly to potential customers how their product worked and what was within coverage and what was not. AI is still a very imperfect technology that often fails. There should not be any surprises for customers on that score.
    1. Hire both tech and domain experts from industry. Your team needs both ML engineers (often PhD level) and top software engineers who can productize and deploy AI. (Ideally you want talent who can do both, but good luck finding them!) There’s a limited supply of ML engineers and big tech companies will pay dearly for a brand new PhD in deep learning. It’s hard for startups to attract top AI talent, but even harder for Fortune 1000 companies. However, attracting domain experts from the traditional industries you are trying to disrupt is even more important. They are critical in helping target customers, deploy technology, and understand the input needed and the internal workflow used by businesses in order to trust AI’s judgments and validate results.
    1. Organizational shift: Towards a more open and experimental culture. ML/AI engineers are still a novelty in the enterprise world. Managing an AI-first startup requires fundamental organizational changes: an experimental culture, data analytics-driven mindset, and more openness towards uncertainties. As a founder, you should help cross-functional teams understand how ML products are different from conventional software products, address potential conflicts, and promote a more open and experimental culture.

Just the beginning

Artificial intelligence continues to evolve at a breakneck pace, creating plenty of ground-floor opportunities for entrepreneurs who are disciplined in their approach, identify best markets for vertical solutions, hire talented and experienced teams, and who are successful at selling AI not as a technology but rather as a means to the best solutions.

Special thanks to Brian Ascher at Venrock for valuable contributions.

Rocio Wu is a second-year MBA candidate at Harvard Business School and a venture capitalist.

Robotics Process Excellence

We have combined Lean Management, Process Re-engineering and Robotics Process Automation (RPA) into a powerful approach to eliminate waste, improve productivity, and reduce the cost of doing business.    Robotics Process Excellence (RPEx) services help organizations:

  • Ensure process performance exceeds business goals.
  • Measurably increase productivity by more than 25%.
  • Enhance the quality of customer care and ease of doing business.
  • Streamline processes and measurably reduce the cost of operating.
  • Eliminate slow, tedious, time consuming, wasteful tasks with Robotic Process Automation (RPA).

Lean management is a proven method for eliminating waste and the cost that comes with it.  RPA  is an inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.  That’s not all,  you also get highest level of enterprise grade security.


Our Approach

Through a simple seven step process, TPMG delivers a low-cost solution for process improvement along with a simple and inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.

RPA COE Process 4.0


Cafeteria of Process Excellence Consulting  Services

We view our process excellence services as the backbone of our business improvement practice.   Our consultants provide first hand knowledge of best practices and a deep understanding of high performance organizations.   We deliver top-quality  services that guarantee your organization become more productive, cost effective and customer driven.  Those services include:

  • Lean Management
  • Activity Based Costing
  • Non-Value Added Analysis
  • Business Process Re-engineering
  • Operational Assessment and Redesign
  • Value Stream Mapping and Improvement
  • Rapid Improvement Events (Kaizen)
  • Business Transformation
  • KPI’s and Metrics
  • Robotic Process Automation (RPA)


Project Description:  What is your process improvement?


Robotics Process Automation – Demo

Robotic Process Automation (RPA) is an affordable solution for organizations to streamline their operations and maximize efficiency. Robots used in RPA interact with applications to perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more. RPA is particularly suitable for processes with high human error rates. It’s an inexpensive software-based technology that sits on top of other applications. It requires no special hardware, and works well in almost any IT environment. That’s not all, you also get highest level of enterprise grade security.

Testimonial – RPA in Tax Accounting

Tax compliance work has long involved the manual extraction of data from financial records. Today, robots have arrived on the scene. Software robotics, or “bots,” can automate manual, repetitive, rule-based tasks, freeing tax department professionals to do higher-value activities.

But that’s just the beginning. Watch EY’s Sharda Cherwoo and Hadley Leach describe how robotics fit into the future of tax.


Demo – Revenue Assurance

This demo explores how robotics process automation and artificial intelligence are continually redefining the future of work. One minute of work from RPA translates to 15 minutes of human activity. RPA also provides stakeholders with additional flexibility, enabling them to focus on more demanding and value added tasks.


Demo – Customer Account Details

Robotics Process  Automation is an affordable solution for organizations to tackle repetitive, low – value added work.  Robots used in RPA interact with applications mimicking human actions and can perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more.  RPA has been adopted in industries with intense, manual, and administrative processes, such as financial services, insurance and health care.   (The information in this demo has been blurred to preserve confidentiality)



Project Description:  What is your process automation project?

Robotics Process Automation

Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is an affordable solution for organizations to streamline their operations and maximize efficiency.  Robots used in RPA interact with applications to perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more.

  • In banking, simple processes like deposits and transfers are perfect for RPA.
  • In insurance, filing and processing claims, underwriting and countless other tasks.
  • The administrative side of healthcare can measurably reduce cost by more than 35%.

RPA is particularly suitable for processes with high human error rates by helping to avoid rework and other error implications like reputational or regulatory risks.   We can help you explore the opportunities!


RPA Delivery Framework

Through a simple seven step process, TPMG delivers a low-cost solution for process improvement along with a simple and inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.

RPA COE Process 4.0

Robotics Process Automation Center of Excellence

The TPMG RPA Center of Excellence (CoE) functions as a Global Shared Service Center  flexible enough to fit with your firm’s business model.  Your company can rely on it to perform functions such as:

  • assessing and prioritizing processes to be automated
  • developing RPA bots and putting them into production
  • developing and implementing change management programs
  • performing the required process re-engineering before the selected process is automated
  • making sure the robots run without any issues
  • performing security and compliance (e.g. audit trails)
  • That’s not all,  you also get highest level of enterprise grade security

Given your needs, our CoE can centralize an RPA team in one location and deploy efforts remotely.  Our team can also perform in a managed services framework and deploy RPA developers across global functions and/or geographies.  Either way, we are structured to maintain constant interaction with your business in order to understand and respond to your needs.

[View Robotics Process Automation Demo]

What is your process automation project?

Contact us today to schedule a cost benefit analysis.  We can help you explore the opportunities!

TPMG RPA Center of Excellence (CoE)










The Process Guy

Stream Lining Processes

I am the Process Guy.  For more than 15 years, I have used best practices like lean, six sigma, and process re-engineering to streamline processes.  To date, I have saved regional, national and global companies more than an estimated $100 million dollars.  The range of industries I have consulted in include financial services, healthcare, technology, supply chain, energy & utilities and telecom.

Cost Savings

In a recent consulting engagement, I used a combination of organizational re-design, process re-engineering and six sigma to generate an FTE savings of 57%.  This happened not only through the use of traditional streamlining methods, but also through the use of a new technology made available to the process man – Robotics Process Automation (RPA).  Robots used in RPA interact with applications to perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more.  It is a simple and inexpensive software-based technology that sits on top of other applications.  It requires no special hardware and works well in almost any IT environment.  That’s not all,  you also get highest level of enterprise grade security.

The Pitch

This may sound like an advertisement, and to an extent, this is true.  But this is more than an advertisement, this is a continual posting for those companies who seek a consultant who guarantees measurable improvements.

I have combined Lean Management, Process Re-engineering and Robotics Process Automation (RPA) into a powerful approach to eliminate waste, improve productivity, and reduce the cost of doing business.    The services I provide are guaranteed to ensure:

  • Measurable improvements in productivity by more than 25%.
  • Streamlined processes and measurable cost reductions of more than 27%.
  • A significant reduction and elimination of slow, tedious, time consuming, and wasteful tasks.

If you are a Chief Financial Officer, VP of Operations, General Manager or merely a responsible leader who wants to improve your company’s return on capital invested – contact me today!

Like I said, my services are guaranteed.  Ask me about that!

You can reach me at:  The Process Guy Email

I look forward to hearing from you!



The Answer to Culture Change: Everyday Management Tactics

Organizations often invest time, money, and leadership capital in performance improvement initiatives that show early promise only to later fail. The challenge of sustaining improvement continues to frustrate health systems across the globe. Through years of studying such change management and quality improvement activities, the Research and Development team at the Institute for Healthcare Improvement (IHI) has learned that the missing piece to sustained improvement at the delivery interface has less to do with care model redesign, incentive payments, IT hardwiring, or policy shifts and more to do with rethinking management structure and practice — or, more specifically, using the management system as a substrate to create a culture of transparency, continuous improvement, and frontline engagement.

In this article, we discuss the results of two pilot initiatives, led by IHI, that resulted in sustained, significant improvements in quality and value in two very different contexts — outpatient care in the U.S. and acute inpatient care in the U.K. — through management interventions that resulted in significant cultural change.

The High-Performance Management System: A Tactical Model to Drive Sustained Value Improvement via Culture Change

Management theorist Edgar Schein defined culture as a shared way of thinking and feeling about problems that an organization faces over time.1 Changing culture can feel amorphous and unfocused, whereas making investments in data systems and analytics that can affect behaviors sometimes seems more practical, tangible, and, in some senses, easier. In order to change culture and thereby realize the performance gains that they seek, leaders need a clear pathway that will allow them to impact values and beliefs. The literature suggests that this pathway can be provided by a set of disciplined management practices that engage the front line.

To that end, IHI partnered with multiple health care providers in the U.S. and Europe to synthesize and pilot test a set of management practices (which became known as the High-Performance Management System, or HPMS) to effect a set of behaviors that result in cultural shifts toward transparency, proactive problem-solving, and continuous team collaboration. These management practices, derived from systematic approaches to quality improvement and Lean principles, have resulted in sustained new levels of organizational performance and reduced costs with our testing partners.

Developing the High-Performance Management System

Starting in the summer of 2015, IHI studied a group of ten high-performing health systems in North America with notably strong organizational cultures (e.g., Intermountain Healthcare, ThedaCare, Denver Health, Geisinger, and others) to derive a set of management practices that might result in sustained high performance.2 The findings from that study suggested that the core elements of such a system included standardization, accountability, visual management, problem-solving, integration, and escalation.


Refining the System to Reduce Utilization and Cost

Starting in fall of 2015, IHI refined this set of ideas and tools to explicitly drive reductions in utilization and cost. The refinements included weekly collection and reporting of operational measures (e.g., time of discharge), capacity measures (e.g., time spent in direct face-to-face patient care), and financial measures (e.g., agency nursing cost), as well as the incorporation of refined visual management tools and a weekly value management huddle. (It should be noted that capacity measurement in practice does not necessarily take place weekly. Capacity measurement requires frontline staff to track their activities over the course of a shift, after which the manager aggregates data from multiple shifts. Given this sampling method, the data collection often best proceeds every 3 to 4 weeks.)

Testing the High-Performance Management System

To test the effectiveness of the HPMS, IHI ran pilots in two geographically and clinically disparate settings: (1) two ambulatory surgery centers in the United States and (2) fifteen units in hospitals in the Scottish National Health Service (NHS). These tests showed that the High-Performance Management System, combined with a focus on continuous review of financial data, offers a solid foundation for positive cultural change to effect continuous value improvement.

Ambulatory Surgery Setting: Testing the Fundamentals

In 2012, the Agency for Healthcare Research and Quality (AHRQ) began funding hundreds of ambulatory surgery centers to participate in multiple cohorts as part of a learning collaborative to improve patient safety.3 Participating centers introduced practices such as surgical time-outs, safety concern escalation behaviors, and improved processes such as the introduction of updated guidelines for equipment processing and sterilization. While progress was seen clearly during the cohort period, project leaders at the Health Research and Educational Trust (HRET) noted frequent regression to mean performance in the cohorts after attention was focused on the next cohort.

In early 2016, HRET invited IHI to introduce a set of practices to promote sustainability — in other words, to ensure that the safety practices that had been introduced were maintained after the sunsetting of the formal program. IHI worked to pilot test the key principles of the High-Performance Management System in two ambulatory surgery centers. In the spring of 2016, two expert IHI coaches skilled in applying the tools of improvement science worked with the sites to introduce management practices that they had identified as means to operationalize the HPMS. For example, the coaches worked with the sites to introduce a daily huddle that focused on a small set of specific actions (such as review of safety risks for the patients on the current day’s surgery list) and safety measures (such as the number of days since the last adverse event). The coaches also helped site leaders, including an administrator in one site and a quality manager in the other, to introduce simple visual management boards to display these measures, with examples of standard work and tracking of problems that had surfaced during the huddles.

One of the two sites reported its results. The other site experienced a change in ownership and leadership that led to discontinuation of the program. The site that reported its results experienced significant improvements, with a 10–percentage point increase in the AHRQ Patient Safety Culture Survey (from an average agreement of 82.5% before the beginning of the program to >93% 4 months into the program, with a higher percentage indicating stronger agreement with statements indicating a positive culture). Improvements occurred in every domain that was tracked in the survey, from prioritization of safety to teamwork and accountability. These improvements have continued, with the most recent survey results increasing to 95% in the winter of 2018.

In addition to these cultural improvements, the site reported multiple quality improvements and increases in the sustainability of previous changes following the introduction of the HPMS practices. For example, the site had previously focused on reducing immediate-use sterilization, which is an important process measure for ambulatory surgery centers as it can indicate inadequate planning for procedures. After the introduction of safety standard work in line with the AHRQ program’s teaching on standard work, surgical time-outs, and safety communication behaviors, the site initially saw a reduction in immediate-use sterilization but then saw some regression. With the introduction of the management practices, including huddles and visual management, the rate of immediate-use sterilization returned to approximately 0%, where it has remained for nearly 2 years.

U chart HPMS-Pilot-Site-Immediate-Use-Sterilization-Chart-U-Chart

In addition to these quantifiable improvements, HPMS practices resulted in numerous other instances of positive cultural change. For example, site leaders reported that frontline staff appeared to feel more empowered and engaged, with staff participation in the performance huddles identifying emerging leaders who subsequently received recognition via promotion. In addition, the site leaders adopted a set of key management behaviors to support these improvements. For example, the site’s quality manager routinely observed the team huddles, provided coaching, and worked closely with an administrative manager to monitor progress and provide the teams with feedback and encouragement.

The quality manager in the ambulatory surgery center also devised a particularly effective model of next-level leadership that has helped to sustain the High-Performance Management System over time and speaks to the “integration” dimension of HPMS. Specifically, she developed her own weekly report spanning all teams in the center, including preoperative and postoperative care, the sterilization unit, the business office, and the operating room. Each team has its own huddle system and huddle board. Every week, the teams report whether they conducted the huddle, whether they updated their measures, and which challenges arose during the huddle and required follow-up. In this way, the quality manager makes the observation of lower-level standard work (the huddles) her own standard work and supports sustainability.

Hospital-Based Setting: Testing the Approach with a Focus on Value

In tandem with the completion of the ambulatory surgery pilot, IHI introduced a modified version of the High-Performance Management System that focused on cost and value, with significant pilot testing occurring in the Scottish NHS4 that began in October 2016.

In this context, visual management included an explicit focus on cost through the use of a “box score” (a concise performance dashboard in the form of a spreadsheet that was updated weekly) and a visual management board (a physical bulletin board that outlined analyses and improvement projects linked to a small set of performance measures). In principle, the elements of the management system used in the inpatient setting were similar to those of the system used in the ambulatory setting, with a focus on cultural change through standard work and huddles, the introduction of visual management tools, a deep focus on problem-solving and continuous improvement, and the involvement of multiple levels of management.

Box score example IHI


Visual Management IHI-HPMS-Visual-Management-Board-Example

Testing the Model in an Inpatient Respiratory Ward

The effort began in a single inpatient respiratory ward (hereafter referred to as the “value prototype team”), with promising results. Two IHI staff supported the team in the initial phase of the work, with help from Lean consultant Brian Maskell, who pioneered the box score in the manufacturing industry. The intensive work with the value prototype team included two site visits to introduce the tools and to teach the site-based staff the HPMS approach. The IHI team also remotely attended the weekly huddles, during which all team staff, a physician lead, and a supporting accountant reviewed the value-focused visual management board.

To assess staff engagement and organizational culture, the IHI team implemented a survey tool (adapted from the AHRQ Patient Safety Culture Survey) that focused on communication, management, and collegiality. Staff were asked to respond to the following four statements with use of a modified Likert scale (strongly agree / agree / neutral / disagree / strongly disagree):

  • People support one another in my unit.
  • We have enough staff to handle the workload.
  • When a lot of work needs to be done quickly, we work together as a team to get it done.
  • In this unit, people treat each other with respect.

Following the implementation of the High-Performance Management System, the value prototype team demonstrated positive results. In Year 1, 86% of staff either “strongly agreed” or “agreed” on a metric that combined the responses to the four statements above into a composite measure. A year later, that value was 92.5%. One team member recently commented, “[the] good atmosphere on the ward and good team makes work enjoyable even when there is high patient turnover and sometimes complex patients.” Another remarked on her appreciation of working together as a team: “[We have] good morale and [support] each other.”

These cultural changes coincided with improvements in cost management, productivity, and quality. Six months after the introduction of the box score, the team registered a statistical reduction in agency nursing spending (from an average of £2,278 per week to an average of £1,561 per week). Two months after that, the team registered an overall shift in cost per patient seen, based on a reduction in agency nursing spending, drug spending, and improved patient throughput (from an average of £535 per week to an average of £485 per week).

The impressive productivity improvements merit additional discussion. The value prototype team has seen three different shifts toward improved productivity — the first driven by improved use of nurse capacity to promote increased face-to-face patient time, the second due to the introduction of an improved discharge preparation checklist, and the third due to the introduction of a midday huddle in addition to morning and afternoon huddles, allowing for more proactive tracking of the timeliness of discharge orders. In all, productivity has increased by 32.8% since the start of the project, with the number of patients seen in the unit increasing from 58 to 77 per week.

Cost per patient IHI IHI-HPMS-Pilot-Unit-Cost-per-Patient-Seen-Pounds-I-Chart

The value prototype team has also maintained high-quality care. For example, the team reduced an already lower readmissions rate of 12% to 10%, representing a statistically significant change. Moreover, the team has maintained a low level of patient falls (roughly 2 to 3 per month, mainly controlled falls) and has introduced a set of improvement projects to bring this number down to 0. These improvement projects have focused on greater fidelity to existing fall prevention bundles and have involved a deeper analysis of the time of day during which most falls were occurring, resulting in heightened attention to staffing levels during those periods. The latest data indicate a 27% drop in falls in the past year — from 51 to 37.

Expanding the Model Within NHS Scotland

The health board that commissioned the work (NHS Highland, a regional health system in the north of Scotland) subsequently spread the application of the management system in two consecutive waves, first to 4 additional hospital-based cardiology teams and then to another 9 teams, separated by intervals of approximately 6 months. Several of these teams have shown significant sustained improvements in terms of quality and cost. For example:

  • An endoscopy team reduced late starts from a median of 100 minutes per week to consistently 0 minutes per week.
  • A medical unit in an outlying community hospital reduced the number of patient falls (including mostly controlled falls) from 12 to 7 per month.
  • A pediatric inpatient unit successfully introduced a new standard operating procedure for children’s meals, with the fidelity to the standard increasing from approximately 18% initially to >80% (maintained consecutively for 8 weeks) and then to 100% (maintained consecutively for 7 weeks).
  • A cardiac intensive care unit reduced its cost per admitted patient by 7% by reducing spending on drugs and supplies.

In all, among the 14 teams that have implemented the value-focused version of the High-Performance Management System for at least 6 months, 9 have shown at least 1 statistically significant improvement in terms of quality and/or cost, and some have shown >1 such improvement.

As part of these efforts, the additional teams also periodically received the AHRQ culture survey questions. The cardiac intensive care unit mentioned above had an average score of 94% (“strongly agree” or “agree”) across its two latest surveys (n = 20 respondents). All surveyed teams have had either stable or improving culture scores. One team has not administered the survey.

As with the ambulatory surgery centers, unit leads and quality leads in the test sites in Scotland emphasized the key role of management behaviors in sustaining the system, again reinforcing the importance of the “integration” component of the High-Performance Management System. Middle-level managers routinely attended huddles, asked questions, and provided encouragement. An assigned physician lead helped to address challenges in engaging physicians — particularly specialists and those without significant quality improvement experience. The hospital executive teams were involved in all key decisions in the development of the work, including the selection of teams, the pacing of spread, and relative investment in improvement priorities (e.g., a focus on overall flow and productivity).

In addition to their focus on the weekly management of value, the Scottish teams have now begun work to introduce daily management boards and to evolve existing daily management huddles to include more of a holistic view of performance (rather focusing simply on patient status updates during transitions between staff shifts). In this way, their work has started to resemble that of the ambulatory sites. Taken together, the combination of daily management and weekly management of value offers a powerful set of tools to rigorously manage all aspects of performance.

Key Lessons

Across both the ambulatory surgery and inpatient sites, the teams that showed the most impressive results shared some factors in common:

  • Improvement capability: Successful teams used basic quality improvement methods with ease: they could generate effective run charts with annotations, conduct detailed cause-and-effect and Pareto analyses, and then plan and execute a sequential series of Plan-Do-Study-Act (PDSA) cycles. These different methods reflect part of what is meant by “problem-solving” in the overall management model.
  • Standard work: Successful teams invested significant time focusing on standard work — not only by having checklists and other tools, but also by carving out aspects of standard work for different roles in order to ensure seamless execution. For example, standard work for effective patient discharge likely requires assigned tasks across many different roles: a nurse assistant might assist with arranging the patient’s belongings and family transportation; nurses might lead teach-back activities, communicate with the physician regarding discharge orders, and review safety bundles as a quality check; and a charge nurse might adjust staffing to ensure that others can complete their discharge tasks in an orderly way and also ensure that those in other roles complete their standard work through observation.
  • Leadership: Successful teams had support from several levels of leadership. The most important of these included:
    • Senior executives: Buy-in from the C-suite team (as indicated by such activities as attending huddles, removing barriers, coaching unit leaders, and celebrating success) helps to maintain momentum. Ideally, senior executives have their own measures and visual boards as well as a role in solving problems and executing improvement.
    • Physicians: Having physicians attend huddles and lead their own PDSA cycles helps to drive improvement and engagement.
    • Next-level managers: In the Scottish NHS setting, each team had administrative support from a service manager, who was accountable for issues such as physician scheduling. The active involvement of such an administrator helped to ensure accountability for the frontline team leader. In the ambulatory setting, a senior quality improvement coordinator helped to provide similar support.

An Evolving Approach

The HPMS practices described here offer health systems a way to improve quality and reduce cost, to sustain the results of their systems improvement and change management work, and to improve value through cultural change.

The High-Performance Management System takes significant time to introduce and evolve. Teams must start with a small number of pilot huddles, and the system should ultimately evolve to include huddles, visual management, standard work, and other elements at each level of management. This process represents a long-term organizational journey rather than the strategic flavor of the year.

Overall, the experience to date indicates that, when adopted at a health system level, these management interventions can create a strong set of linkages between otherwise abstract and discontinuous initiatives focused on value, staff satisfaction, and culture change.


1. Schein, E. Organizational Culture and Leadership. 2nd ed. San Francisco: Jossey-Bass; 1992.

2. Scoville R, Little K, Rakover J, Luther K, Mate K. Sustaining improvement. IHI White Paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2016.

4. Agency for Healthcare Research and Quality. AHRQ Safety Program for Ambulatory Surgery: Final Report. AHRQ Publication No. 16(17)-0019-1-EF. 2017.

4. Mate KS, Rakover J, Cordiner K, Maskell B. A simple way to involve frontline clinicians in managing costs. Harvard Business Review. October 11, 2017.



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