Coming to Terms with Clinical Standardization

I woke with standards on my mind. Why should we have standards? I slid out of my standard King size bed, or was it a California King? What is the difference? I jabbed the pesky fitted sheet back under the mattress and wondered if maybe it was a California King and that is why the fitted sheet does not quite fit. I threw on my standard size suit (at least it was standard for that brand). Was it US or European sizing? And how much lasagna did I eat last night? Do I count by ounces or grams, calories or carbohydrates? I climbed into my automobile (no doubt plenty of standards went into building that complex machine) and followed standard operating procedures (honestly) to safely navigate my way to work.

By setting an intention to observe the impact of standards in my life, I became aware of how often they are present and taken for granted. Which makes sense, as one of the reasons for standards is to make things work together harmoniously, so we have the advantage of moving through portions of our lives on autopilot, and can reserve our attention for those things that are novel and require more brain processing power. Additionally, I noticed the annoyances in life that are also often associated with standards, or rather the lack or inconsistency of standards. I have expectations that I can buy things which will reliably fit my bed or my body. I depend on standards to measure ingredients, temperature and distance, and converting from one standard to another takes time and can result in errors. I count on drivers of other vehicles on the road and producers of the food I consume to follow standard procedures to protect my safety. When these standards are not met, my life is faced with inconvenience, and maybe even injury.

Turning attention to Clinical Standardization reveals similar patterns. Patients have expectations of receiving timely quality care. They want to know what to expect after taking a pill, or what the process of investigating a symptom will entail. They want to know that their tax money is going to good use, and that the healthcare system is spending on efforts that reliably produce improved outcomes. When clinical teams follow evidence-informed guidelines, outcomes are improved and processes are more efficient. When they do not, time consuming circular processes occur and people can end up injured and even dead.

Basic Components of Clinical Standards
Let us look more closely at the components of Clinical Standards. Standardization is composed of the combination of standards of care and data standards.

Standards of care are a desired and achievable level of performance against which actual performance can be compared. Standards of care must be based on evidence-informed best practice as opposed to tradition and anecdote. For example, it is standard of care to administer aspirin to patients arriving in the emergency department with cardiac chest pain. If this does not occur, the care providers may find themselves explaining why not in front of a court.

Data standards, meanwhile, provide consistent technical language and clinical terminology, and allow information to be captured in data points and stored in a repository. This collection of data can be compared to other sets of data of the same standard, and then used in analytics to provide insight. An example of data standards is SNOMED CT, which is a multilingual numerical standard for over 340,000 concepts in clinical medicine that facilitates the exchange of information between different electronic health records, and which can also be mapped to other international standards [1].

For example, if my chest pain patient is diagnosed with an acute myocardial infarction, I might document in my electronic record “acute MI” under the problem list which should guide me into the SNOMED CT code 57054005. In the future we can search the database for those with code 57054005 who have received aspirin (387458008) and discover how well we are meeting the clinical standard. If I had simply free texted the problem “acute MI,” the success of retrieving the information to measure this clinical standard would be very dependent upon my spelling and phrasing of the problem, as well as the spelling and phrasing utilized by my medical colleagues also adding information about their own patients and occurrences into the database.

Value of Clinical Standards
Now let us drill down a little deeper into the components of Clinical Standards. Table 1 represents a suggested breakdown of a clinical information system. Let me preface this with the comment that there are many ways you can dissect a system. I could have broken it down into system modules, system language or other technical features. However, most of us – including me – are not programmed to think in that manner. I have elected to categorize the components with a very clinical perspective from where we can evaluate the benefits realized from Clinical Standardization to date.

Patient Data
First there are basic patient data elements that all providers want to know:

  • Who is the patient?
  • What is the billing information for that patient?
  • What is the emergency contact for that patient?
  • If I wanted to contact the patient, what number or email would I contact?
  • Does the patient have allergies that I should know about immediately before I reach for that aspirin?
  • What medications are they already taking?
  • What health problems do we already know they have?
  • What are some of their basic risk factors, such as smoking history and family history?

The answers to these questions are widely used by all users, regardless of department or profession and are commonly required in the exchange of information between systems and providers. The patient data reflects the summary of care for the patient at a specific point in time.

Multiple countries have established standards for these components to facilitate information exchange. In the United States, the Continuity of Care Record (CCR) is a set of data required for exchange at care transitions as part of meaningful use stage 2. The CCR was created by health care practitioners based on the data elements they collectively believed to be of importance to the care of their patients, including authoring and receiving information, dates, patient identifying information, patient insurance/financial information, advance directive, health status (including problems, family history, social history, allergies, medications, immunizations, vitals, laboratory results, procedures, imaging results), care documentation, care plan recommendations and identification of the care team [2].

A recent review article in Journal of the American Medical Informatics Association (JAMIA) found 24 high quality studies demonstrating a reduction in healthcare utilization and costs as a result of the existence of health information exchange (HIE). This reduction was accomplished through fewer readmissions, less repeat studies and decreased overall number of orders. Additionally, improved quality was demonstrated through improved medication reconciliation, immunization and record completeness, decreased disparities, and improved HIV related quality of care measures [3].

There are future prospects for use of these core data elements for research and population health as well. De-identified data can be mined to identify health trends. For example, medication adverse events and infectious diseases, now limited to manual reporting, could be detected employing big data analytics. Additionally, data can be mined for alerts to primary care providers to identify chronic conditions in need of treatment, such as diabetics not optimized on treatment or children not up-to-date on immunizations [4].

Diagnostic Tests and Pharmacy Orders
From a practical on-the-ground point of view, the standardization of key diagnostic tests can help us function with less confusion. For example, when I order “electrolytes” at one hospital I may get one set of results, but I may get a different set at another hospital. For doctors on the move, it is a challenge to remember that one emergency department kindly throws in the creatinine and another expects you to order it separately. The same holds true with complete blood counts (CBCs). When you order a CBC do you automatically get a differential? Or do you have to order that separately? When you order a differential separately, is it a manual or automatic differential.

I think we can agree that it would be easier to practice if we all had the same expectations for lab terminology and what we can expect from them. Additionally, there are cost differences between lab sets depending on their content and the abilities of the lab providing them. This could add costs to care if unnecessary tests are being ordered simply due to bundling in lab sets.

The same holds true for imaging studies and aliases. I cannot begin to express my surprise at being told that the proper test to order for an ultrasound to look for appendicitis in a young male was “ultrasound of the pelvis, non-OB.” What clinician would ever think to specify that their male patient is not an obstetric patient? The location of the appendix is a completely separate point to debate as, in my training, it was made clear that the appendix is in the right lower quadrant of the abdomen, not the pelvis. Clinical aliases and diagnostic naming conventions are important foundations to efficient and effective use of a health technology system. To prevent ordering errors, the catalogue of orders needs to make sense to both the people ordering the study and the people performing it.

Standard aliases and mapping should be employed in situations where the two parties do not see eye to eye. Although drug formularies may vary from hospital to hospital, the drug identification numbers (DINs), required prescribing fields and unit doses should be standard. Without these standards, medication management from the outpatient to inpatient setting is cumbersome and prone to error. For example, having to enter on admission all of the data elements on every drug a patient is taking can take a pharmacy technician 20 minutes per patient.
If standards are in place that allow importing the drugs from provincial drug registries or family physician EMRs, then this time can be used instead to counsel patients on their medication.

We may be able to agree on the importance of the patient data for a clinical information system (CIS). But is there value in taking the standardization to the next level? What about documentation? There is much opportunity for gathering data in clinical documentation, and I will tell you what sold me on such an effort, then give you some more real life examples.

In 2012 there was a Forbes article that went viral about the American department store, Target. The report was that a man called and complained to a Target store that they were sending his young teenage daughter inappropriate ads for maternity clothing. This prompted a discussion between father and daughter where he discovered she actually was pregnant. The suggestion was that Target is so skilled with market basket analysis – meaning gathering the discrete data elements of purchases in a shopping basket and ascertaining future needs – that they could diagnose pregnancy and “target” advertising accordingly [5].

This story may very well be an urban legend, but any of us who have used Google know that there is tremendous – perhaps even frightening – power in big data analytics. How many times have you looked at the pop up ads on the side of your screen and wondered if the computer was reading your mind?

The very visible power of big data analytics surrounding us from the marketing world, compared to the meager assistance we receive in healthcare, fueled my passion for harnessing big data in healthcare. What if my EMR had suggested diagnoses on the side of the screen as accurate as my Google page? This makes me a huge advocate for gathering discrete data and continuing to work on the power of natural language processing.

Big data power aside, there are real opportunities for improved patient safety and communication through standardizing of documentation, even without the support of artificial intelligence. An example of standardization of assessments is documentation related to pain intensity. If nurses use the Numerical Pain Scale, pain intensity will be reported on a scale from one to ten. If nurses use the Wong Baker Pain Scale, the pain will be reported on a scale of one to five. Now transfer the patient from a location that uses one standard to the next and a score of five could be overlooked or under treated as moderate pain and a score of 10 could be nonsensical.

Now imagine the benefits of physician documentation standardization. You will not go far to find a physician complaining of thirty page discharge summaries that lack a follow up plan, or progress notes that seem to be copy and paste from the last three weeks. On the positive side, at least electronic discharge summaries are now legible and can be transmitted electronically. On the negative side, now nobody wants to actually read them due to the note bloat of electronic charting, and hence no real communication advantage has been gained.

Fundamental to our current situation is the ambiguity of physician documentation standards. In medical school we learned SOAP (Subjective, Objective, Assessment and Plan) notes and consult format, all well supported in college documentation guidelines. However, we did not learn how to take advantage of and properly use the ability to pull information from various areas of an electronic chart into a physician document. The ability appeared and nobody stopped to say, “Should we?”

Unfortunately, looking south of the border does not provide us with a desirable course for physician documentation standards, though perhaps it still has some valuable lessons. In a 2018 article in the Annals of Internal Medicine, the authors turn the spotlight on electronic health record documentation as being a significant contributing factor to physician burnout. They observed that US physicians on average do 4 times more clinical note taking than physicians overseas. Not surprisingly, physicians in places like Australia and Singapore were more positive about the improvements that accompanied the introduction of electronic health records. The authors speculate that regulatory requirements in the US have led to increased documentation burden, and that reform to limit physician documentation requirements could help alleviate the growing epidemic of physician burnout [6].

As we move into the electronic health record world here in Canada, we will need to identify what information is critical for physicians to document, and develop standards based on minimum specifications. These minimum specifications should include specific elements to be included and excluded. For example, family doctors express that they have no desire to see the results of labs and imaging studies in discharge summaries because they already receive that information directly from the lab or imaging department. They do, however, want to see the follow-up plan so they understand what is required of them in the next steps of care for the patient.

Another element to add to the mix is the availability of documentation to patients via patient portals. This introduces the patient as a primary stakeholder in the content of physician documentation. According to the 1993 Canadian Supreme Court decision in McInerney v. MacDonald, the content of the medical record belongs to the patient, though the physical record belongs to the provider.

This being the case, it only makes sense to have the patients participate in defining what information is important to them in a medical record. Establishing a forum to accomplish this is an important task with no current owner [7].

Order Sets
Another category of standardization involves the remainder of the order catalogue, including nursing and allied care provider interventions (dietary, physiotherapy, social work, wound and skin, etc.), the combination of orders into order sets, and care pathways specific to conditions.

There is significant opportunity for promoting evidence-informed guidelines by utilizing order sets that gather the appropriate orders for a condition into one checklist, incorporate clinical decision reminders, and conveniently leave off orders for which there is poor evidence, or evidence against their use.

Speak to any Chief Medical Informatics Officer (CMIO) about order sets and you will hear that order set development and standardization requires significant organization wide resources, physician and pharmacist time, iterative discussions, agonizing maintenance and lengthy approval processes. “Is it worth it?” is a question asked by all involved.

Let’s review the value of electronic order sets as demonstrated in the literature. I performed a Pub Med search from 1995-2018 for “electronic order sets”. The search resulted in 1431 articles. Upon review of abstracts, 69 articles appeared relevant to electronic order sets in the medical profession. General categories of topics included: order set only interventions (13), comprehensive pathways in which order sets were a component (25), general order set development information (15), order set design (9) and clinical decision support including the use of order sets (7).

There were 12 order set only intervention studies with outcomes, seven of which showed positive outcomes. In several of those that showed mixed outcomes, physician behaviour changed based on the intervention, but the clinical outcomes did not significantly change. This suggests that modifications may be needed to the guideline to impact clinical outcomes. Two articles with mixed outcomes were likely due to poor adoption of the order set by the physicians.

Here are a few case studies of benefits from standardized care pathways:

  • Avera Health, which is a US healthcare system consisting of 33 hospitals, decreased sepsis mortality through ED sepsis pathway by 37%and saved $5,080 per case.
  • Stamford Health in Connecticut utilized a catheter associated urinary tract infection (CAUTI) prevention pathway and reduced hospital-wide urinary catheter use by 50%, and associated urinary tract infections by 70%, and achieved $100,000 in savings.
  • Christus Health, consisting of 47 hospitals in the southern US, decreased the overall length of stay between 17-45% based on acuity, and decreased 30-day readmission rates by 30.2% with the introduction of order sets and computerized provider order entry (CPOE) [36].

Canadian hospitals have seen similar positive results leveraging the use of clinical standardization and automation. Some examples of outcomes reported in presentations from Canadian partners include North York Hospital’s demonstration of:

  • Increase in venous thromboembolism prophylaxis from 50% to 96%
  • Reduction in pneumonia deaths by 55% with diagnosis specific order set
  • Reduction in deaths from COPD by 45% with diagnosis specific order set
  • Decreased average turn-around time from antibiotic order to administration by 4 hours
  • Reduction in overall hospital standardized mortality rate

Ontario Shores demonstrated:

  • Increase in adherence to metabolic monitoring by 20%
  • Reduction in number of patients on multiple antipsychotics by 31%
  • Increase in dysphagia management team referrals by 143%
  • Reduction in choking incidences

In summary, order sets and electronic care pathways clearly have the potential to change behaviour of medical practitioners, and standardized evidence-informed medicine has the potential to improve patient outcomes. Our task should be to continue to harness and expand these potentials and make some real world improvements.

The next category for standards includes coordinating the actions of care with referrals, consults, discharge plans, patient education and medication reconciliation. According to a CMA policy statement on streamlining patient flow, two-thirds of specialists are frustrated by incomplete information in referral requests. In another article exploring the numerous barriers for successful referrals, primary
care physicians complained of the idiosyncratic referral requirements for different specialists. One solution agreed to by all parties was to increase standardization in the content and process for referrals [36, 37].

A 2015 literature review found seventeen studies from ten countries regarding the implementation of e-referral solutions. Common elements of successful systems included:

  • collaboratively agreed-upon referral guidelines
  • standardized format for information transfer
  • EMR integration
  • ability to attach medical documents
  • auto-population of relevant patient data
  • auto-completion of clinician identification date
  • acknowledgement of receipt of referral
  • choice at referral

Positive outcomes achieved with e-referral solutions included improvement in communication, access, information exchange, knowledge management, efficiency, documentation quality, and healthcare quality. Additionally, e-referral solutions resulted in the reduction of wait times, no shows, unnecessary visits, erroneous information and administrative activities. Common obstacles, meanwhile, included lack of political support, top-down approaches and technical challenges [38].

Successful improvements in referrals will require political attention, and improved collaboration between hospitals, community physicians, specialist physicians and administrative staff. It is also difficult to imagine success in this endeavour without public funding.

Medication Management
Medication management is another area that requires significant coordination and effort across the system and can have high value returns. For example, according to a 2013 report from the Canadian Institute for Health Information (CIHI), between 1-25% of all hospital admissions and ED visits are drug related, an estimated cost of $35.7 million [39, 40].

Many family physicians complain that patients are discharged from the hospital without a clear understanding of how their medication has been changed. It is not uncommon for patients to add all of the prescriptions they receive from the hospital to the medication they are already taking at home, often resulting in duplicates and subsequent medical issues.

In order to improve upon our current state, we will need to have clear standard processes for medication management involving the transfer and reconciliation of discrete data at all transitions in care. Having one source of truth for medication across the spectrum of care would allow all members of the care team, including patients and family, to adjust medication in real-time, and decrease the burden of discrete data entry and the likelihood for error.

Discharge Planning
Discharge planning is similar in its dependency on coordination across the care spectrum. The process should begin on arrival at the hospital with an assessment of the patient’s supports at home and in the community. This should include identification of, and communication with, the patient’s care team in the family/community, and a review of the patient’s medications and in home physical and spiritual resources. During hospitalization, the multidisciplinary team should focus on identification of the patient’s changing needs, and education of the patient and/or caregivers, especially regarding such matters as proposed changes in their mobility, diet, medication, or occupational functioning.

At discharge, the patient’s home-going medications need to be reviewed, modified, and explained to the patient and their caregivers if applicable. Outpatient supports for diet, mobility, rehabilitation and continuing care need to be aligned and prepared to assume care. This coordination may involve communications between the patient, family, pharmacists, nurses, physicians, dieticians, speech and language pathologists, physiotherapists, occupational therapists, wound and skin experts, social workers, respiratory therapists, long-term care facilities, home health services, and potentially more. This clearly complex process requires significant standardization and expectation setting to ensure that all members of the team have the information they need to make decisions and manage their role in the process.

Common sense says that there is a significant potential to increase patient satisfaction, decrease length of stay, and reduce readmissions with a coordinated standardized discharge process. Reviews of the literature, however, have actually shown mixed results thus far. The latest Cochrane review suggests that there might be a small reduction in length of stay and readmissions with individualized structured discharge plans for elderly people admitted with a medical condition [41]. With the complexity of the process, there is no wonder successful executions have been rare, though more research is clearly needed as well in this area.

Once we have clinical information systems in place to gather data, we will have a greater ability to use the data to visualize what we are doing, and understand where and how we could improve and create processes to ensure that we do it. It is the availability of data and value of possible applications of artificial intelligence that provide us with the possibility of a future where we can prevent predicted disease before it happens. Leveraging insights from each other during this process could result in opportunities for rapid quality improvements and efficiencies.

For example, Humber River Hospital opened a new facility in 2015 and found their expected occupancy was much higher than planned. Instead of building an expansion, they decided to focus their attention on improving the movement of patients through their hospital by creating a clinical command centre to observe and adjust the flow of patients. By leveraging the data of their electronic systems, creating a cohabitation of employees and clear procedures for addressing alerts, they managed to virtually add a whole unit to their hospital. Now imagine using the same model across the Province.

Similar successes are available to other hospitals with the increasing availability of electronic data, and the potential of analytic tools to apply business intelligence to that data. In order to learn how to create an agile analytics competent environment, the HIMSS Analytics model can be applied. This model is potentially applicable across a coordinated health system if clinical standardization has taken place [42].

It would be wonderful if people had the innate ability to use clinical information systems. These days it almost seems as if children are born with an electronic device already attached to their hands. However, even the most tech savvy users need an orientation, education and ongoing updates on how best to use any electronic system, just as they need continuing education on how best to care for patients. If our systems are standardized to the extent that educational material can be used by any medical practitioner in the system throughout the country, then the cost of developing and maintaining those materials would be significantly reduced.

For example, one hospital could spend $125,000 dollars to build e-learning modules for clinicians on a new clinical information system in an effort to reduce classroom training costs. According to the Ontario Hospital Association, there are 141 public hospital corporations in the Province. If each hospital corporation had to build their own education programs, it could cost the Province $125,000 x 141 or a total of $17,625,000 dollars of health care money, not to mention the need for ongoing system updates. Imagine the potential savings that could be realized by reducing this to the number of clinical information systems in the Province instead of the number of hospitals. Now expand that to the country as a whole, assuming 1, 417 hospitals as reported in 2015 by statista. com, and we could potentially save up to $177 million dollars, or potentially use the funds for ongoing adoption support which is desperately needed. Training costs in hospitals could be further reduced if this education were embedded within the curriculum of nursing, medical and other academic programs offered at colleges and universities.

The Trouble with Standards
Now that we are all convinced of the benefits of standardization, let us consider the downsides.

Thinking back to my morning attention to standards, all of the activities I performed are usually done on “autopilot” with very little opportunity for awareness or reflection. When we wander through life on autopilot we miss opportunities to truly experience our reality. Without pausing to ask the question of why I have to tuck in the fitted sheet every morning, I miss the opportunity to pay attention to the sizing nuances the next time I purchase sheets (perhaps selecting “California King” or “deep pocket” sheets). I also miss out on the opportunities to improve my quality of life.

What about introducing a mindful meditation into the morning, or some exercise? Getting stuck in autopilot can limit our innovation and keep us from finding healthier ways of living.

Overly strict and unquestioned standards can limit innovation in the clinical realm as well. The process of continuously seeking consensus and improving standards is a costly endeavour, assuming you can even convince the inherently overwhelmed stakeholders, caught up in their own autopilots, to come to the table. Even if the effort required to regularly re-engineer standardization is successfully mounted, there will remain the potentially greater challenge of distributing and enforcing the standard to a wide and diverse population.

Caution with Evidence Base
To complicate matters even more, there are some fundamental issues with the foundation of standards, the evidence base. Two decades ago we saw an explosion in evidence-informed medicine. Randomized control trials and observational studies increasingly replaced tradition and anecdote in providing the foundation for our actions in medicine and in the public health realm [43].

Today “evidence informed medicine” is used as the secret password for most healthcare initiatives. Let’s take a moment to bring some awareness to our magical beliefs in the phrase and apply a critical eye to the current state of evidence informed medicine.

First, you get what you pay for. Research is costly, and those who pay set the spotlight on what is observed. That spotlight can be turned off as quickly as it is turned on if the results do not suit the sponsor. A review of US Food and Drug Administration (FDA) registered trials of antidepressants found that 37 of 38 positive findings were published, but only 11 of 33 negative ones were. Those negative result trials that were published often were worded in a generally positive manner [44]. This brings to question if it is “evidence informed medicine” or “industry informed medicine”? Even assuming a trial is not biased by who is paying the bill, it does not mean that it is clinically relevant. Just because a hypothesis is supported does not mean an outcome is improved or a patient experiences a benefit.

Another area for consideration is that the complexity of personal health is not adequately addressed by our traditional models of research. The scientific method is most applicable to understanding a single factor and its relationship to a disease or outcome. Although this was helpful in the 19th and 20th centuries as “germs” were identified, it is less applicable to the problems that plague us today, such as complex immunologic diseases, genetic disorders and cancer. A new model of research is needed focusing on modelling and simulation of multiple variables [45]. How are we going to develop these models and simulations and free ourselves from industry based research? The greatest opportunity seems to reside in the potential for big data generated from clinical information systems.

Besides the quality of data and the complexity of research, there is no doubt that the sheer volume of evidence available has become unmanageable using our traditional methods. Observe the exponential increase in the volume of medical research publications in graph 1.


Laakso and Bjork (2012) Anatomy of open access publishing: a study of longitudinal development and internal structure. BMC Medicine, 2012;10:124.

As interesting as this graph is, I want to point out that it ends in 2011, and since then it appears that the upward trend has continued. It would be entertaining to calculate the amount of reading one would have to do to stay on top of all of this growing wealth of evidence. One study from 2004 calculated that 7,287 articles were being published each month pertaining to primary care. It would likely require 627.5 hours per month to evaluate the usefulness of these articles alone [47] and that calculation was at the pace of publication 14 years ago. Add to that estimate the time needed to evaluate the changes to workflow, order sets and documentation that would need to occur as a result of evidence supporting a change in practice. Clearly this amount of work cannot be expected of an individual physician, nor is it entirely clear which other actors in the medical profession should be expected to take on this role. Machine learning seems the only potential solution. Of course, machine learning will require data, which will require standardization and clinical information systems.

Standards Impeding Patient-Centred Care
Well established standard care pathways have their downsides too. If they lead us to “autopilot” and to overlook the individuality of our patients or the possibility of a misdiagnosis, then they will result in a poorer quality of care than we currently experience. For example, a colleague was lamenting to me that a computer generated alert for sepsis prompted the initiation of a sepsis protocol in the emergency department on a patient who had a concurrent seizure disorder. The patient had been admitted multiple times with high lactates, placed on the sepsis pathway and administered large doses of antibiotics. The hospitalists never found any positive cultures or signs of infection after admission, and had the strong suspicion that the perpetually elevated lactate was due to seizures and not sepsis. Failure to consider alternative diagnoses because of a push to sepsis pathways resulted in inappropriate care for the patient. Though pathways can have their benefits, they do not mean we should turn off our brains and our critical thinking skills. We need to exercise our ability to wake up from autopilot and make well informed choices.

In summary, standards have their benefits and their pitfalls, all of which should be considered in creating a plan about how best to move forward. The definition and extent of standardization is not a science, nor a specific identifiable point or goal, but rather a convergence of intentions. Finding the sweet spot will require some trial and error.

We do know that the lack of international standards in a global market place can lead to confused or dissatisfied customers and vendors. How many of us have heard stories of patients complaining of their healthcare experience? How often do you hear stories of satisfied patients? Increasing healthcare standards to ensure clear expectations could go a long way in changing attitudes and improving quality of care.

As we move forward we should keep the following guidelines in mind:

  1. Utilize standard nomenclature to create a common language between different systems. SNOMED CT quality indicators are a standard that could provide an acceptable universal language. This will provide opportunities for data analytics that hold the promise of future quality improvement.
  2. Build the clinical systems with standard documentation/order catalogues and order sets to the greatest extent possible to ensure a common data set of exchangeable information.
  3. Develop standard translations of clinical guidelines by people knowledgeable in clinical information systems, and educate authors of clinical guidelines to deliver their standards within the framework of clinical information system functions.
  4. Engage end-users in standards creation and systems design
    to ensure it meets their needs and increases the likelihood of adoption. (And yes, this means pay them as well!)
  5. Ensure that education regarding the standards and changes in the system are made available to the end-users both effectively and cost effectively to improve the chances of adequate adoption.
  6. Refine standards regularly to keep up with evidence and best practices.

Clinical Standardization will be a lengthy process and an iterative and constantly evolving experience. How will we know when we have finished Clinical Standardization?

When our customers are healthy and happy!

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