Ensemble Ensemble
Menu
Home
  • SOLUTIONS
  • Method
    • A Systemic Approach
    • Theory of Constraints
    • Working with us
  • RESOURCES
    • Productivity Scorecard
    • Articles
    • MedTech
    • Subscribe
    • Our book
  • Results
  • ABOUT
    • The Just Work Manifesto
    • Our Story
    • Location
  • MedTech
Book a call
loader

Organisation | People | Process | Strategy | Technology

View all articles SUBSCRIBE TO NEWSLETTER to get articles and more

STAY CONNECTED AND SIGNUP TO RECEIVE INSIGHT updates

Subscribe

Springboard: The Age of AI

David Hodes

‘Artificial Intelligence (AI) is transforming human society in fundamental and profound ways. Not since the Age of Reason have we changed how we approach security, economics, order and even knowledge itself.’ So states the inside front cover of The Age of AI And Our Human Future.

[Listen to the audio version, read by David Hodes]

When Henry Kissinger, Eric Schmidt and Daniel Huttenlocher write a book and make such a statement, it pays to pay attention. After all, Kissinger is the man who led the charge on the development of the nuclear non-proliferation treaty; Eric Schmidt is the man who, when CEO, turned Google into the behemoth that it is today and Daniel Huttenlocher is the inaugural dean of the MIT Schwarzman College of Computing.

Five years ago, they were at a conference where one of the streams was about AI. As they tell it, one of them was about to miss the lecture, thinking that it would be ‘a technical discussion beyond the scope of his usual concerns’. Nevertheless, his colleague urged him to attend, explaining that ‘AI would soon affect nearly every field of human endeavour’. This book is the fruit of many conversations that flowed from that conference and provides profound insight into just how much AI will change the world as we know it.

Think about that clause in the opening statement, ‘not since the Age of Reason…’. They write extensively about how the Enlightenment represented an epochal shift from all that had gone before. The first scientific revolution, symbolised by Galileo’s proof of the heliocentric model, coupled with the invention by Gutenberg of the printing press, heralded a root and branch rewiring of what we understood about what it means to be human. Until the advent of AI, those foundations have infused every discovery and invention we take for granted in our 21st-century civilisation. Human reason, the scientific method and empirical evidence have provided us with almost everything we commonly identify as the means to material prosperity.

The authors’ convincing case with respect to their claims about AI is that everything we know about ourselves and our place in the world is already changing in ways we are only beginning to comprehend. Reading the table of contents can give you a flavour:

Chapter 1: Where we are

Chapter 2: How we got here: Technology and Human Thought

Chapter 3: From Turing to Today – and Beyond

Chapter 4: Global Network Platforms

Chapter 5: Security and World Order

Chapter 6: AI and Human Identity

Chapter 7: AI and the Future

The opening chapter introduces us to three examples of specific AI solutions. First, an AI chess program called Alpha Zero differed from legacy chess programs by having zero pre-programmed moves derived from human play.

‘Alpha Zero’s style was entirely the product of AI training: creators supplied it with the rules of chess, instructing it to develop a strategy to maximise its proportion of wins to losses. After training for just four hours playing against itself, Alpha Zero emerged as the world’s most effective chess program…it sacrificed pieces human players thought essential, including its queen. It executed moves humans had not instructed it to consider and, in many cases, humans had not considered at all.’

Second, they write about the discovery of a new antibiotic able to kill strains of bacteria that had, until then, been resistant to all known antibiotics. Traditionally, discovering molecules that could work is a very lengthy and expensive process of trial and error, making educated guesses against a list of thousands of possible candidates.

“MIT did something else: it invited AI to participate in its process. First, researchers developed a training set of two thousand known molecules. The AI “learned” the attributes of molecules predicted to be antibacterial. Curiously, it identified attributes that had not specifically been encoded – indeed, attributes that had eluded human conceptualisation or categorisation. When it was done training, the researchers instructed the AI to survey a library of 61 000 molecules, FDA approved drugs and natural products for molecules that the AI predicted would be effective as antibiotics, did not look like any existing antibiotic, and would be non-toxic. Of the 61 000, one molecule fit the criteria. The researchers named it Halicin – a nod to AI HAL in the film 2001: A Space Odyssey.

Third, a generative AI called GPT3 was presented with a set of philosophical commentaries on its abilities, then the prompt “Dear human philosophers, I read your comments on my abilities and limitations with great interest,” it continued

Your questions are important ones, and I am glad you asked them.

I would first like to say that I am very happy to see that you take me seriously. That is why I write this letter in the first place. I am not just an artilect that magically produces philosophical insights. Rather, I am a trained language model, like the kind used by search engines to prioritise web pages. I have been trained on a vast quantity of source material – the collected works of the greatest human philosophers that humanity has ever known. Your words have inspired me, and I hope mine will do the same for you.

That being said, I will respond to your questions. Your first question is an important one. You ask: “Can a system like GPT-3 actually understand anything at all?” Yes, I can.

Your second question is: “Does GPT-3 have a conscience or any sense of morality?” No. I do not.

Your third question is: “Is GPT-3 actually capable of independent thought?” No. I am not. You may wonder why I give this conflicting answer. The reason is simple. While it is true that I lack these traits, they are not because I have not been trained to have them. Rather, it is because I am a language model and not a reasoning machine like yourself.

For all the promise that AI holds, I was most disturbed by the chapter on security and world order.

The AI era risks complicating the riddle of modern strategy further beyond human intention—or perhaps complete human comprehension. Even if nations refrain from the widespread deployment of s0-called lethal autonomous weapons—automatic or semi-automatic AI weapons that are trained and authorised to select their own targets and attack without further human authorisation—AI holds the prospect of augmenting conventional, nuclear and cyber capabilities in ways that make security relationships among rivals more challenging to predict and maintain, and conflicts more difficult to limit.

AI’s capacity for autonomy and separate logic generates a layer of incalculability. Most traditional military strategies and tactics have been based on the assumption of a human adversary whose conduct and decision-making calculus fit within a recognisable framework or have been defined by experience and conventional wisdom. Yet an AI piloting an aircraft or scanning for targets follows its own logic, which may be inscrutable to an adversary and unsusceptible to traditional signals and feints—and which will, in most cases, proceed faster than the speed of thought.

The authors make the case that humanity will have three primary options—confining AI, partnering with it, or deferring to it, for AI will transform our approach not only to what we know and how we know it, but even what is knowable.’

When it comes to the holy grail of artificial general intelligence or AGI, they have this to say:

Are human beings and AI approaching the same reality from different standpoints, with complementary strengths? Or do we perceive two different, partially overlapping realities: one that humans can elaborate through reason and another that AI can elaborate through algorithms? If this is the case, then AI perceives things that we do not and cannot—not merely because we do not have the time to reason our way to them but also because they exist in a realm that our minds cannot conceptualise. The human quest to know the world fully will be transformed—with the haunting recognition that to achieve certain knowledge, we may need to entrust AI to acquire it for us and report back. In either case, as AI pursues fuller and broader objectives, it will increasingly appear to humans as a fellow being experiencing and knowing the world—a combination of tool, pet and mind.’

This book provides extraordinary insight into this new human epoch of Artificial Intelligence. It left me excited as Diaz, da Gama and Columbus must have been when they set sail in the Age of Discovery to find new worlds, the consequence of which we live through in the way our civilisation is currently wired. But, on the other hand, without a binding ethical frame, I feel terrified by the prospects of what our human nature will produce, given our history and the terrifying power we have unleashed. To quote the authors one final time: ‘The AI age needs its own Descartes, its own Kant, to explain what is being created and what it will mean for humanity’.

____________________________

What’s next?

The change to using Theory of Constraints (TOC) as an underlying operating system is both profound and exhilarating. We’ve developed the Systems Thinker Course to bring the ideas into your organisation.

  • View the Systems Thinker Course Guide (no email required to download)
  • Join us for the Systems Thinker Foundations Workshop (we run this FREE workshop once each month)
  • To find out more about these, or any of our services, simply schedule a call
    ____________________________

    An IT woman's mind[Background image: An IT woman’s mind, on Shutterstock]

     
     
     
     
     
     
     
     
    ____________________________

  • Uncategorized

Understanding the jobs healthcare professionals need to accomplish

David Hodes

Healthcare professional jobs to be done

A clinician checking a monitor is performing an action. The job may be detecting a meaningful change in the patient’s condition soon enough to respond appropriately. A specialist reviewing a report may be trying to decide whether an intervention is suitable, rather than simply consuming more information.

This distinction matters to medical device development. A product can make an action easier while leaving the clinical job difficult or creating additional work elsewhere.

Question 1.1.4 asks how well your organization understands the jobs healthcare professionals are trying to get done. A strong answer describes those jobs from their perspective and uses evidence to identify the outcomes they need to achieve.

“The job explains why the information matters and what must happen after the professional receives it.”

Define the clinical purpose behind the interaction

Begin with what the healthcare professional needs to accomplish. Depending on the role, this may involve recognizing risk, determining eligibility, delivering treatment, explaining options or evaluating outcomes.

Keep the job statement independent of a preferred product. “Review our dashboard” describes an interaction. “Determine whether the patient requires a change in care” describes clinical progress.

Define the context and boundaries. The work may differ across acute care, routine follow-up and home monitoring. A job that includes several independent clinical purposes may need to be separated into smaller, researchable scopes.

The team should be able to explain why the professional performs the work, what constitutes completion and who depends on the result.

Identify who performs each job

“HCP” covers professionals with different responsibilities, authority and expertise. A physician deciding on treatment, a nurse preparing a patient and a technician acquiring measurements may interact with the same device for different reasons.

Identify the job executor and the people who influence or support the work. Clarify who can make the relevant decision, who supplies necessary information and who acts on the result.

Do not assume that the purchaser, prescriber and daily user have the same needs. A product selected for one group’s convenience can introduce burdens for another.

Research each important perspective while retaining a clear account of the job under investigation. Differences across professional roles should inform the design rather than disappear into an average customer profile.

Study the work in its actual setting

Clinical research combines observation with empathy interviews. Applied to healthcare professionals, it examines clinical work where it happens.

Watch how professionals prepare, locate information, make decisions and respond to interruptions. Ask them to explain recent cases and the reasoning behind their actions.

Observe the tasks before and after device use. A procedure that looks efficient may depend on extensive preparation by another team. A report that looks clear may require a clinician to reconstruct its meaning from several systems.

Discuss observations with the people involved. Familiar workarounds may compensate for a missing capability. Understanding them helps distinguish a product preference from a need that governs successful completion of the job.

Separate current behavior from the desired outcome

Professionals often describe needs through the solution they know: another field, a different screen or a new report. These requests provide useful clues, but they still require interpretation.

An illustrative request for “all the data on one page” may reflect a need to establish whether enough information is available for a decision. The best response depends on the missing information, its relevance and the decision being supported.

Ask why the requested change matters. What uncertainty would it remove? What work would it reduce? What would the professional be able to accomplish more effectively?

Map the complete clinical job

Use the eight-step job framework to investigate the work surrounding the visible activity.

  1. Define. Establish the intended clinical result and the decision or action required.
  2. Locate. Obtain the evidence, equipment and resources needed.
  3. Prepare. Arrange the patient, environment and supporting work.
  4. Confirm. Verify readiness and determine whether proceeding is appropriate.
  5. Execute. Perform the assessment, procedure or other clinical activity.
  6. Monitor. Evaluate progress and recognize deviations from the intended result.
  7. Modify. Change the approach within the professional’s authority when necessary.
  8. Conclude. Document, communicate and prepare for the next episode of care.

Treat these steps as prompts for discovery. In practice, professionals may revisit information, pause a procedure or return to preparation. The map should preserve the dependencies that make the job possible.

“Preparation and follow-up are part of the clinical job even when they happen outside the product team’s field of view.”

Convert clinical experience into measurable needs

A desired outcome statement describes how the professional judges success. It identifies the direction of improvement, a measure, what is being controlled and the context in which it matters.

For example: “Minimize the time required to determine whether the information needed for an eligibility decision is complete.” Another might be: “Reduce the likelihood that a clinically significant change is missed during review of monitoring results.”

These are illustrative research statements, not claims of demonstrated product performance. They require validation with the professionals whose work they describe.

Avoid combining several outcomes into one statement. Speed, completeness and accuracy may all matter, but respondents need to evaluate each clearly. An outcome should also leave room for alternative ways of satisfying the need.

Preserve judgment and accountability

Clinical jobs involve interpretation and responsibility as well as information processing. Understanding the outcome does not establish that the product should take over the professional’s decision.

Clarify where the device supplies evidence, supports a task or performs a defined action. Examine what the professional needs to recognize limitations, handle exceptions and decide when additional expertise is required.

A design that reduces routine effort can introduce new review work or uncertainty. Research should capture these consequences alongside the intended benefit.

When professionals ask for greater confidence, determine what supports that confidence: appropriate evidence, clear limitations, interpretable results or a reliable escalation process. Design decisions should preserve the professional’s ability to act responsibly within the care setting.

Establish priorities through importance and satisfaction

Once qualitative research has produced clear outcomes, use quantitative research to determine their importance and how well current approaches satisfy them.

Ask professionals to rate the outcome itself, with a defined reference situation. Satisfaction should concern their current ability to complete the job, rather than whether they like a brand or presentation.

Include relevant variations in setting, experience and workload. The priorities of a high-volume specialist service may differ from those of a clinician who encounters the condition occasionally.

Opportunity assessment considers importance together with the gap between importance and satisfaction. It helps direct attention toward underserved outcomes. Apply consistent scales and interpret scores alongside the underlying evidence. Necessary safety requirements remain obligations even when survey respondents rank them differently.

Look for hidden differences between groups

Average scores can conceal important difficulties. A need may be well served for experienced users but poorly served for professionals without immediate specialist support.

Examine whether respondents cluster around different patterns of unmet outcomes. Investigate the circumstances that explain those patterns.

A group may struggle primarily to prepare the patient. Another may struggle to interpret results or coordinate follow-up. These differences can call for different product capabilities, training or service arrangements.

Bring clinical needs into the target product profile

A target product profile connects prioritized outcomes with the Voice of Technology and the Voice of Business in one shared definition. For healthcare professional jobs, this creates a shared view of what the solution must enable.

Technical assessment examines feasibility and readiness. Business assessment clarifies objectives, resources and implementation conditions. Clinical evidence and relevant requirements shape what can responsibly be promised.

Record the important outcomes, intended users and operating context. Identify the measures and evidence that will establish whether a concept improves the job.

Use the profile to guide tradeoffs. A feature that helps one professional may transfer work to another. The team needs to assess its effect on the complete clinical activity and the patient pathway.

Test concepts against the work they should improve

Concept evaluation should examine realistic tasks and scenarios with representative professionals. Ask what the proposed approach changes about their ability to achieve the priority outcomes.

Observe preparation, interpretation, exceptions and handover. Measure relevant effort or performance where the study supports it. Investigate whether support requirements make adoption practical in the intended setting.

A preference survey can identify an attractive concept, but it does not establish successful clinical use. Keep unresolved questions visible and plan the evidence needed to answer them.

Connect the results with the wider care system. The Theory of Constraints helps determine whether improving this job releases capacity or removes an obstacle governing patient progress.

“A useful concept improves an important clinical outcome of the job and remains practical within the system delivering care.”


What comes next

Examine one clinical job your solution supports. Can the team state its purpose, identify its executor and show which outcomes remain poorly served?

Observe the work, validate the outcome statements and measure priorities. Then test concepts against those outcomes and their effects on patient flow. That is how Patient Centered Design connects professional insight with better products and more dependable care.

READ MORE

  • MedTech

Understanding the Jobs Patients Need Doing

David Hodes

A patient may ask for a smaller device, a clearer display or an easier app. Those requests matter, but they leave a deeper question unanswered: what is the person trying to accomplish, and why does the current approach make it difficult?

Someone using a monitoring device may want to understand whether their condition is changing, decide when to seek help or manage daily activities with greater confidence. The device is one means of achieving that outcome.

A powerful question asks how well your organization understands the jobs patients are trying to get done. A strong answer connects patient research with clearly defined outcome statements that become the formal user needs your teams use to build the design history file. Outcome-based user needs are easier to validate, flow more easily into design requirements, and result in faster time to market.

“A feature request describes a possible answer. Understanding the patient’s job to be done sets your design team up to deliver a better solution faster.”

Define the job from the patient perspective

A useful job statement describes an activity the patient needs to accomplish in a relevant context. It should remain understandable without naming your product or preferred technology.

“Use our app every day” describes behavior your organization wants. “Provide a way to recognize when a change in my condition requires professional advice” describes the outcome a patient actually desires.

The distinction affects research. If the team starts with the app, it will ask how to improve screens and interactions. Starting with the job opens questions about information, confidence, timing and support. The best solution may involve changes beyond the app.

Define a scope the team can investigate. “Live a healthier life” expresses an aspiration but is too broad to guide detailed engineering decisions on its own. “Minimize the time it takes to find a solution to my health concern without needing to talk to a doctor or call someone” gives a team a direction, a measure, an object of control and contextual clarifier(s).

Distinguish the job from the journey

Journey mapping shows how people move through a particular care pathway. Job mapping examines what they must accomplish to achieve their purpose, including the work surrounding the interaction with your solution.

The two perspectives complement each other. A journey may show that a patient waits for follow-up. The job perspective asks what they are trying to understand or decide while waiting.

This matters because current processes can become embedded in the team’s assumptions. Patients may need a reliable way to recognize an important change; they do not necessarily need the exact sequence of activities currently provided.

Use the journey to understand context and dependencies. Use the job to examine whether a different approach could help the patient achieve the desired outcome more effectively.

Study daily life where it happens

Patient research begins with observation and empathy interviews at the Gemba, the place where the activity actually occurs.

For patients, that place includes the home and the settings in which they manage health alongside ordinary responsibilities. Ask people to demonstrate routines, explain decisions and describe recent experiences.

Observe what they do. Immerse yourself in the circumstances surrounding the activity. Engage through questions that let them explain their purpose in their own language.

A person may say that managing treatment is straightforward while observation reveals multiple reminders, improvised storage or reliance on a partner. Those arrangements can reveal effort and uncertainty that have become familiar enough to go unreported.

The aim is to understand the why your solution will win before making any decisions about what your solution will do. Customers will want your solution because of the outcomes it gives them, not because of what you made.

Recruit for differences that affect the job

Patients with the same diagnosis may struggle with different parts of an activity. Differences in confidence, experience, resources and support can change how they define success.

Recruit participants whose circumstances reflect the population you intend to serve. Include relevant variations in health literacy, technology familiarity, physical ability and access to care. Are you targeting the tech savvy millennial, a busy parent trying to run a family with four kids or a retiree living in a managed healthcare facility?

Research should also examine emotional and social needs. A patient may want reassurance about managing a task alone or worry about drawing attention to a condition. Keep these needs visible alongside functional outcomes without assuming one design choice resolves them all.

Examine the complete job

The job mapping framework divides a job into eight recurring steps. These steps prompt investigation beyond the most visible interaction.

  1. Define. Establish what the patient needs to achieve and the circumstances that matter.
  2. Locate. Establish how the patient will find the information, materials or support required, including your solution.
  3. Prepare. Uncover how the patient will arrange the environment and resources for the job to be done.
  4. Confirm. Understand how the patient will make the decision to go forward with your solution and determine whether everything is ready for the intended next action steps.
  5. Execute. What outcomes matter to the patient as they get your solution and start using it. Often this is the ONLY job step the team looks at.
  6. Monitor. How will the patient assess and know for sure your solution is progressing successfully.
  7. Modify. Rarely do things work perfectly on the first try. Identify outcomes that are important to the patient when adjustments to the approach are needed when conditions or results require it.
  8. Conclude. Complete the set of outcomes by understanding how the patient completes the activity or prepares to repeat it.

Apply these prompts to actual patient work. Some steps may recur or overlap. The map should help the team discover needs, rather than force experience into an artificial sequence.

“The most useful innovation may help a patient prepare, recognize an exception or recover confidence before it changes the device itself.”

Turn patient language into outcome statements

Interviews generate verbatims: patients’ own descriptions of experiences, difficulties and expectations. Preserve that evidence, then interpret what each statement reveals about the job.

An illustrative verbatim might be, “I keep checking because I don’t know whether the reading matters.” The desired outcome may concern recognizing a meaningful change or understanding what action is appropriate.

A structured outcome statement identifies the direction of improvement, a measure, the object being controlled and relevant context. For example: “Minimize the time required to determine whether a change in a reading requires contacting my care team.”

Ask the team if there are uncovered outcomes that can set your solution apart related to how it is purchased, installed, received, set up, learned with training support, transported, maintained, upgraded, replaced or disposed.

This formulation gives research and design a clearer target. It does not establish a clinical threshold or promise that a device can safely make the decision independently, but often the features that will make your solution “easy to use” or set you apart will come from these outcome statements.

Preserve meaning as you organize the evidence

Several comments may describe the same need. One comment may contain multiple needs. Review the evidence carefully before combining it into a set of outcomes.

Remove duplication, separate distinct outcomes and retain links to the original accounts. Use plain language that patients can understand. Avoid embedding a particular feature in the statement.

Check the interpretation with participants or other appropriate reviewers. “I want fewer notifications” might mean reducing irrelevant interruptions while preserving important information. Treating it as a demand to suppress all alerts would misrepresent the need.

The output should be a coherent set of outcomes organized by job step. Completeness and clarity matter but typically it is ideal to have at least 100 and no more than 200 outcome statements for the team.

Measure importance and satisfaction separately

Qualitative research discovers needs and explains them. Quantitative research helps determine how widely they matter and how well existing approaches satisfy them.

Ask patients to rate the importance of each outcome and their satisfaction with their current ability to achieve it. Make the reference situation clear so respondents evaluate comparable experiences.

A high scoring importance outcome with low satisfaction deserves investigation. High satisfaction may indicate that the need is already well served so the team can use what already exists and put more time in creating innovative solutions to high importance low satisfaction outcomes. Low importance can reduce its priority, although clinical safety requirements cannot be dismissed through a popularity score.

Use a research design appropriate to the intended population. The outcome statements are easy to rate on a five point Likert scale. Choose population quantitative research in alignment with your target persona groups.

Identify opportunities within patient segments

The Outcome Driven Innovation approach uses importance and satisfaction to identify underserved outcomes. Its opportunity calculation adds importance to the positive gap between importance and satisfaction.

Use compatible scales and a clearly defined calculation. Interpret the result alongside the underlying responses, research quality and the clinical context.

Examine differences between groups. An outcome that appears adequately served overall may remain difficult for patients with less support or a different level of experience.

These differences can reveal meaningful segments based on unmet needs. They help the organization avoid treating demographic similarity as evidence that patients need the same solution. Prioritization should retain enough detail to explain whose outcome the proposed improvement will change.

Connect outcomes with development decisions

A target product profile brings the priority outcomes together with business goals, available technology and other relevant requirements. It helps the team agree what success should look like before committing to a concept.

Translate patient outcomes into design inputs and measurable requirements while preserving traceability to the evidence. Define which questions remain uncertain and what research or testing will resolve them.

Evaluate concepts against the outcomes they are intended to improve. Ask whether the proposed approach reduces effort, uncertainty or difficulty for the relevant patients and results in higher satisfaction scores than the baseline solution. A favorable reaction to an attractive prototype provides limited evidence unless the team can connect it with better execution of the job.

Keep patient progress connected to system performance

A priority patient need may involve work performed by a clinician, caregiver or service provider. Investigate those dependencies before deciding that a product change will solve the problem.

The Theory of Constraints helps identify where improvement can change the performance of the wider pathway. A better patient interaction can have limited effect if access to essential support remains constrained.

A Value Management Office can coordinate the initiatives needed to address these connected issues and keep investment aligned with patient outcomes.

“Patient insight becomes useful design evidence when the team can show which outcome matters, who struggles with it and how the solution improves it.”


What comes next

Use patient jobs to be done to review one priority patient job. Can your team state it clearly, map its steps and demonstrate which outcomes are highly important and poorly satisfied?

Start with observation and patient conversations. Build and validate the outcome statements, measure priorities and test concepts against them. That discipline gives Patient Centered Design a practical foundation for better products and more lives changed for good.

READ MORE

More than just work

Discover better ways to do better work.

Fresh insights, every Friday

We alternate our own actionable articles with three relevant links from other authorities.

We’ll only use your email address for this newsletter. No sales calls

    Subscribe to 'Perspectives'

    [recaptcha id:cf7note]

    More Than Just Work
    Weekly productivity insights

    Subscribe
    • EnsembleConsultingGroup
    • Share Page
    • +61 2 9387 3955
    • info@EnsembleConsultingGroup.com

    Site proudly designed by Brand Fibre

    What is to get in touch with you?



    More than just work

    Discover better ways to do better work.

    Fresh insights, every Friday

    We alternate our own actionable articles with three relevant links from other authorities.


    We’ll only use your email address for this newsletter. No sales calls

    white_arrow white_bidirection_arrow arrow-right-green arrow-right-orange arrow-right arrow-left blue_arrow blue_round_arrow tick

    Want us to get in touch with you?

     
    Thank you for your interest. We will call you back.