In the healthcare industry, demands for better services at lower costs are at the forefront of many conversations both within and without the walls. Many of the mandates that government sets forth also stipulate this. How are organizations supposed to make changes to a business that is dealing with accidents and ailments that have their own unique circumstances? There is a particular tool that these groups now have at their disposal, which provides a better understanding of the community where they are located and the needs of the people found within; this is Population Health Management software. Healthcare providers can make data-driven decisions, plan for the future and improve care all while containing costs.

image courtesy of freedigitalphotos.net/DavidCastilloDominici

image courtesy of freedigitalphotos.net/DavidCastilloDominici

Population Health Management is defined as the outcomes and distribution of healthcare within a specified group. These groups may be as large as a nation, or a particular community; they may be more exact by selecting persons with disabilities or financial difficulties. However, when a healthcare organization is providing services in a defined area, they want to know the types of services that are most needed, where are the best locations for hospitals and clinics to serve the most number of patients, and where specialty treatments physicians would most conveniently located.

The task of tracking down all this kind of information, creating a report that details the population that is being helped, and doing it in a timely manner is almost impossible. Educated guesses are still just based on making a decision without all the information in hand. This is where and why Population Health Management (PHM) software is crucial in changing decisions from guesses to fact-based and data-driven.

We all participate in adding information to healthcare data systems every time we visit the doctor or end up in the hospital; statistics and notes go into our electronic health record (EHR), which enables the treating expert to know our health history and to add to it. This health information is not only collected for our own personal benefit, but is pulled out and used to understand everyone around us. In other words, when you get the flu and end up in the doctor’s office, information is saved and analytics applied to bring to light health patterns in your area. With a picture of patterns that have occurred over years, and your input, it is easy for decision makers within a healthcare organization to predict when cold and flu season will probably start next year. This is a small example, but the ability to prepare, yet not over prepare, aids in the effort to provide better care and not have waste or overages on the financial side.

The predictive and preparative knowledge gained by understanding and applying population health management strategies also has an effect on managing patients’ risks and reducing negative consequences. As seen in this Becker’s Hospital Review article, PHM turns previously time consuming tasks into much more time sensitive reports and data, which translate directly into effective and efficient care.

image courtesy of freedigitalphotos.net/imagerymajestic

image courtesy of freedigitalphotos.net/imagerymajestic

PHM isn’t simply loaded onto a bunch of computers and it is ready to use. There is a lot of time, changes and other investments that are required to implement the system. With as much data that is being created and saved, the need for an Enterprise Data Warehouse (EDW) is a must. Along with knowing that implementation isn’t a series of single projects that once completed provide a new, self-contained system. Part of this is due to the fact that every organization has different requirements and unique obligations that must be met. These must be understood so as to integrate them into the system as a whole, and ultimately reach a point where efficiencies are more the norm.

Truly, it is quite amazing that each one of us is contributing to a huge picture that improves health services down the road. We all live as part of a community and many of the regularities found within communities are repeated. These repeats of patterns and the understanding of how to prevent, prepare and predict health issues is exactly what Population Health Management is all about.

For anyone who doesn’t know what big data is, you’d better get caught up quickly, because it is the direction many industries and companies within large industries are headed. A basic definition, as stated in a Forbes articles defines big data as: “…a collection of data from traditional and digital sources inside and outside [a] company that represents a source for ongoing discovery and analysis.” So, how do mainstream industries use big data compared to how the healthcare industry uses it?

image courtesy of freedigitalphotos.net/bluebay

image courtesy of freedigitalphotos.net/bluebay

Big data is the means by which business practices can truly measure what is happening at every level and department in the organization. The ability to measure provides a fundamental understanding, which leads to better and more accurate decisions. Within the retail market, this kind of familiarity leads to enhanced responses to customers’ wants and needs in a timelier manner than traditionally discovered with analytics.

This is much the same when looking at big data in healthcare: measuring the positive results with patients, working to raise patient satisfaction levels, and meeting/exceeding resolutions to departmental budget shortcomings become transparent. When you have knowledge about what needs to change, you can thus make the changes necessary, and provide a better care environment for patients.

Many companies seek to understand actions their customers take, especially while on the internet. This does not simply tie a customer to his or her purchases, but also includes other items they clicked on, the different pages visited on the website and promotions or reviews that may have influenced their navigations throughout. With this data and the firm understanding of what the customers may be looking for, a company can then offer advertising directed specifically for each customer. Much of this individualized attention happens in real-time while the consumer is still online.

The concept of real-time assessment is happening more and more in a healthcare situation: doctors have access to a patient’s electronic health record (EHR), which provides a comprehensive explanation as to what the patient has been through. Being able to coordinate current care with previous events will better ensure that adverse events are avoided. Customer actions aren’t followed by clicks, but by visits within a healthcare organization, and include all medical notations. Doctors are then able to present the most advantageous approach to helping the patient.

Technology is moving ahead at breakneck speed for every industry. Data systems and analytics that were sufficient and may have been considered cutting edge five years ago are now far from adequate for the amount of data being collected or the demand for that data to be utilized to promote customer experiences. The need to have ample room to store and access the data means moving up to a database or in many cases a data warehouse. Additionally, software to manage the data is part of the package required to handle all the information that is being saved.

One of the largest industries for data production is healthcare. We are all contributing to the growing knowledge base within a healthcare system, and that information is substantial. When you think about the different kinds of data that is being gathered, you might begin to understand the sheer depth of fact and images that stored. Most of the data comes in three different forms:

  • Billing and clinical transactions
  • Digital capture of diagnostic images
  • Documentation and notations
image courtesy of freedigitalphotos.net/arztsamui

image courtesy of freedigitalphotos.net/arztsamui

Obviously, the type of data being stored and accessed is quite different between most businesses and a healthcare organization. However, the amount of information being generated can be considered comparable to these other fields and in some respects must be managed much more securely due to the fact that personal, medical and financial information is being cached.

Although healthcare requires a different type of big data system, the fact that big data is a key component to helping care specialists improve medical treatment and to do so in a timelier and more efficient manner shows that both business and healthcare benefit from rendered information uncovered. Big data is a broad-spectrum word that has many implications in any field to which it is applied.

This may have come out of left field and not seem like the most conventional partnering in the healthcare industry, but this may prove to be the new model for making healthcare a safer and more efficient industry.

If you haven’t heard, MedCity News recently announced the combining of technologies with the partnering of Allina Health and Health Catalyst. The Minnesota-based Allina Health has more than 90 clinics and 12 hospitals that will benefit from the one of the highest rated data warehousing and analytics companies in the realm of healthcare software.

What makes this collaboration unique and thus newsworthy is the fact that this is a shared-risk deal with Allina sending its data warehousing, clinical analytics and specific personnel to Health Catalyst. Then Health Catalyst must prove measurable and significant reductions to inefficiencies and manage both financial and patient risk levels, which translates to money saved for patient and organization and improved patient satisfaction with overall care.

This ten-year contract will enable Allina to become a front-runner to a new model at a time when fee-for-services payments with insurance companies are going away, and the need to locate wasteful spending and practices delivers a more robust and enhanced system of business. If Health Catalyst doesn’t meet data-driven improvements, they risk upwards of 20 percent of their fees that they could collect. Thus achieving specified objectives will demonstrate their ability to handle this new structure to a business relationship.

Have you ever had to answer the question: On a scale of one to ten, what is your pain level? Maybe at admission to the hospital or new patient paperwork they ask what medications and current/prior health issues you’ve had. There is a lot of insight that can be gleaned from just those two groupings of questions. This insight, along with other pertinent information can help health professionals to better diagnose, assess and treat. What you might not know is that this practice is called risk stratification.

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image courtesy of freedigitalphotos.net/voraorn

To understand better how risk can be lessened or controlled in a health care environment, each patient must be evaluated to see what is happening. This occurs whether the visit is to a doctor’s office, clinic or emergency room. The immediate pressure to gauge a situation may not be as pressing outside of the emergency room, but stratifying the risk to each patient aids in the overall healthcare needs, services and coordination needed both in the short- and long-term circumstances. So, how do doctors go about assessing the risk a patient is under?

Identifying risk levels comes down to knowledge, history and communication. Knowledge is the professional understanding of the human body and how it both functions and can be fixed/healed. The history is our own point of view of what has happened to us, whether it be passing events or ongoing issues. What I mean by a point of view is that when prior diagnoses and treatments have transpired, if we remember the sequence of even differently or erroneously, this may play a part in future health management. This may also be the case when recalling family history of disease; if we misunderstood what someone suffer with or never knew the medical account.

Communication may be one of the most important steps or tools that is used to realize what is happening. Both the doctor and patient need to be willing to listen, disclose details and follow up with any additional questions as required for full perspective. When one or both parties miss out on vital components within a conversation, the likelihood of mistakes escalates dramatically. For instance, when a patient hasn’t divulged all current symptoms, critical elements to a quick conclusion may be passed over and the length of treatment greatly extended.

Once all information is collected, and a specific assessment can be made on the patient, this is where a better understanding of risk stratification comes into play. Sensitivity to factors that affect the overall risk level that a patient is categorized into includes:

  • comorbidities
  • mental health
  • poverty
  • social changes
  • inability to self-manage
  • likelihood of readmission
  • tendency to frequently seek out medical support

The higher the risk level, the more likely that patient requires personalized managed with his or her care so as to prevent adverse outcomes. To explain further, many individuals have a tendency to become overwhelmed with changes to lifestyles and routines, and as such either purposefully or unknowingly mismanage his or her own care. The need to monitor, regulate and follow up on care must be seen as essential. Within the confines of a hospital, these steps are supervised and recorded. However, when home care is called for, patients and procedures can fall through the cracks. It may be as simple as forgetting to take prescribed medicine at specified times, or as disastrous as introducing unsterile equipment to a susceptible patient.

image courtesy of freedigitalphotos.net/DavidCastilloDominici

image courtesy of freedigitalphotos.net/DavidCastilloDominici

By managing the amount of risk patients are subjected to, and allocating appropriate resources when they are needed and to whom, the probability of negative outcomes is lessened. This management can be proactive, reactive or a combination of both. A proactive approach is to get ahead and prevent problems from aggravating a situation, while a reactive approach is situationally driven and require putting finger in the dike instead of preventing the leaks in the first place. However, it is impossible to prevent incidents from happening, especially when dealing with a high-risk patient. Thus, being able to prevent the manifestation of some symptoms while managing others as they surface is critical.

As much as we may not want to correlate managing the cost of healthcare and providing improved care, it is a very real subject within risk management. In a day and age of lowering overall costs yet increased expectations of positive results and technological advances, the resulting fall out can include malpractice lawsuits and increased cost to insurance. When proactive and even predictive methods are implemented, the ability to control risks to patients and financial losses broadly speaks to the need of risk stratification techniques being openly utilized.

It goes without saying that when you make certain purchases or research particular products the reputation and quality of such are factored into your decision. Yet, within the realm of healthcare, we take for granted that our doctor or local hospital meet and exceed quality standards around the nation. Unfortunately, this may not be the case, but not necessarily because of the professionals within the businesses. Quality within a service industry may very well come down to the overall standards established as an individual entity.

photo courtesy of jscreationzs/freedigitalphotos.net

photo courtesy of jscreationzs/freedigitalphotos.net

By no means does this imply that any healthcare professional seek to have a lesser model to work towards or endanger patient health and privacy, yet the quality controls practiced within the organization may unintentionally lead to this result. An overall picture of the positives and negatives may only be painted when there is sufficient data collected, and compiled so as to reveal reality without biases.

The collection and compilation of information seems like it should be straightforward, especially in a world amassed with computer records. An unfortunate characteristic within the healthcare industry is that software programs dedicated to these dynamic needs has not been at the forefront of progress as it has been within other businesses and industries. This game of catch-up has many players and is providing a wide variety of programs, which take in all the necessary details and can generate an image, whether literal or by means of reports that indicates many different answers to questions of how to run a business better. Some of these more clarified images include:

• Inefficiencies
• Loss in timely actions
• Errors in diagnoses and/or treatments
• Poor performance at any level or department
• Need for continuity of further services
• Increase costs to facility due to duplications or lost information

Though this list could be much greater and finely detailed, the general idea that many areas of interest are at stake for losses of time and money. Thus, there is a basis for making changes to combat problematic situations and to be truly productive. Implementing aspects such as data warehousing and identifying process improvement will begin the fine-tuning that will lead to true, provable change.

Obviously, this doesn’t happen overnight, nor does it come about without challenges. Some obstacles that can be predicted before any implementation has occurred include:

• Resistance to change as a general attribute
• Changes require time to implement and to learn
• Processes can be initially slower
• Concerns for privacy may be expressed

However, these may not be all the struggles that are revealed. Other problems may include:

• Easier detection of poor performance
• Individuals who struggle with new technology
• Change of duties or responsibilities
• Changes in business emphases
• Resistance from outside sources (e.g. insurance providers)
• In significant changes, patient feedback may be affected

photo courtesy of StuartMiles/freeditigalphotos.net

photo courtesy of StuartMiles/freeditigalphotos.net

In a day and age of technological advances and greater need for analytics within healthcare data, the improvements that have been made and are continuing to be made are astounding. To manage or control the quality of care received, it is necessary to employ unbiased knowledge as a means to a better end. This end is a better overall treatment for patients at a lower cost for all involved. Quality control in healthcare equals enhanced medical healing.

To understand what business intelligence (BI) is, we should start by choosing an industry first, and and then dig into application.  Gartner.com defines BI as an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

Healthcare Business Intelligence

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BI isn’t a stand-alone concept or practice, especially when employed within the healthcare industry.  There are three main components that when utilized alongside BI to provide the most effectual data.  The first component is the data storage technology, or the enterprise data warehouse (EDW).  This is a system that converts stored data, past or present, into meaningful or useful information enabling better decision making.

Once all the data is organized in a manner that can be extracted, the second component is called upon.  The need to change the aggregated details into a visual format is known as data visualization and is one of the more powerful tools available.  Most of us can’t look at a spreadsheet with its rows and columns of information and truly make sense of it all.  However, when translated into a graph, chart or diagram, the comparisons become easy to digest and diagnose.  A visual analysis allows decision makers to begin to answer questions such as:

  • What happened?
  • How often did it happen?
  • What is the problem?
  • What do I need to do to fix the problem?

Now that a picture has been created regarding what has already occurred, the third component is then put into action.  Being able to drill down and apply discovery methods is where information about the future is contained.  Many refer to this as business analytics, which is the predictive aspect to intelligence.  When you are able to use the answers from the questions from the past to find out what the future might hold, you are moving into a realm of better business making decisions.  Answering questions along the lines of:

  • Why is this happening?
  • What happens if we keep moving in the same direction as a company?
  • What are the possible outcomes and financial benefits?

Many healthcare professionals will draw upon dashboards, which provide a high-level view of the organization.  This other visual component is part of the business analytics side of the equation because it can supply answers to the any different “why” questions, and thus leads to projections in potential understanding of the business as a whole.

IT in the Business of Healthcare

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There is a natural progression that strengthens the course of healthcare, both within and outside the walls of a doctor’s office, clinic or hospital.  When you know what has happened, you can create reports.  When you know what is happening right now, you can monitor and make real-time changes and decisions.  When you know why something has happened, you can analyze it for better awareness.  And, when you know what is possible in the future, you can predict when time, money and resources should be accurately dedicated.  Business Intelligence is the process that makes it all possible.

Have you ever put off buying something you knew you needed, and you had every excuse in the book?  They were really good excuses, but there usually comes a point where either out of necessity or emergency the purchase is made, and then you wonder why you didn’t do it sooner.  This seems to be the case of investing in and implementing healthcare information systems (IS) into clinics and hospitals.  Many of the excuses given by practitioners and other health professionals include a financial burden, the old ways still work well, changes – especially this large – can take up too much time and resources, and the flat out rejection of new technology.

Managing Healthcare Information

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Whether it is realized by decision makers or not, a functional information system can be the tool that should be purchased sooner rather than later, and it shouldn’t be entered into lightly.  The goals and focus of your practice have to be understood so as to utilize any software to its fullest potential.  Just because there are the best of intentions for better business actions does not mean that installing and employing an IS will smooth sailing.  In fact, there are many challenges that should be addressed and not overlooked or swept under the rug.  The adaptation of new technology isn’t an all or none scenario, but has levels of usage.  For example, a physician may prefer not to carry around a tablet to access medical research and records, not because he or she is not comfortable with the tablet, but the tablet may be cumbersome or be a distraction when meeting with a patient.  However, the nursing staff may find that a tablet is very convenient and aids in time management.

Some practitioners are accustomed to turning to journals and published work to find new guidelines and medical results; moving to an Internet-based search may be too much out of the norm.  When you take into consideration that many of the medical journals have decades of study and investigation behind the findings.  Changing an age-old, accepted method of progression for one that is much more instantaneous can prove to be a big hurdle to jump.

Managing Healthcare Information

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Another barrier that can impede implementation is the fear of litigation.  Unfortunately, our society is so quick to skip any sort of negotiations and run to a lawyer when medical mistakes occur.  So, the tried and tested ways of past are much easier to rely on over those that are technology-based.

One of the modern moves that medicine is making is towards personalized medicine.  This is the use of a patient’s physiological makeup and medical history to provide a more effective and exacting treatment.  The direct correlation between having up-to-the-minute information and analytical capability provided within an IS is crucial.  This is also the case when moving to a more evidence based medicine (EBM) means of treatment.  EBM relies finding links between common treatments  and large scale proven treatments.  The commonality is sometimes only found with the help of software that can recognize patterns within vast numbers of cases.

Modern Healthcare Information Systems

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Having the technology of an IS has been shown to relate directly with the rise in quality of care provided and patient satisfaction.  The patient doesn’t need to see that a medical professional is carrying around a tablet or some other piece of technology, but as practitioners are able to more specifically provide needed care, avoid errors or prolong hospital stays, everyone’s best interests are accounted for: patient is helped along quicker and healthcare professionals reduce expenses.  Although adaptation isn’t a one size fits all case, the steps taken to implement and utilize a healthcare information system will prove to be beneficial for all involved.

A very basic definition of data mining is the searching through large amounts of computerized data to find useful trends or patterns.  At one point this might have been plotted on a simple Excel sheet or graph, but in the realm of today’s standards, the amount of data and nuances involved far exceed minimal plotting.  Especially in an industry as large and data-rich as healthcare, the discovery of patterns requires a lot more processing power.

Clinical analytics means examining vast amounts of information in order to uncover recurring trends, which in turn produce predictive or future forecast data.  Just as when watching a weather forecast, the computers that make the predictions for five or ten days out take into account the patterns in the highs and lows currently happening, and then find previous models that held similar flows and calculates what should happen.  Though this is quite simplified, this example shows the value of data mining from years of previously stored and analyzed information.

clinical analytics and data management

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Being able to predict the future of patients and their healthcare opens the doors to being able to identify and deal with high-risk patients more appropriately, rather than continually admitting them into costly hospital beds.  Also, being able to ascertain the patients with severe or chronic illnesses who might be susceptible to readmissions can be headed off with other preventative measures instead of another hospital stay.  Armed with the ability to distinguish when patients fall into certain patterns, doctors and other health professionals would be able to manage staff, space, resources and the overall costs expended due to effective and efficient planning.

Healthcare and the many different fields of medicine are one of the larger industries creating a majority of data generated each day.  This data is compiled together and shared as part of the information exchange that takes into account demographics, such as age, gender and zip code, along with patient’s needs, and plots out general and diverse patterns, especially within communities or populations.  These patterns are then applied by professionals when treatment is sought in order to categorize the patient into a more personalized course of healing.

Data mining in healthcare doesn’t benefit just one group of people, but works to help everyone that is involved at the many different levels of treatments; doctors and other staff have more information at their fingertips to correctly and quickly diagnose a patient.  The patients may not be subjected to unnecessary testing or periods of unknowing because of the actionable data.  Hospitals can run more efficiently due to the fine-tuned productivity found within the patterns.  Even the prevention of fraud and fraudulent behavior can be identified from previously recognized trends.

clinical data management and healthcare analytics

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The most unfortunate aspect in all of this is the healthcare industry is playing a game of catch up within the realm of data mining.  Most other businesses and industries have fewer points for which they are trying to model.  The sheer complexity of data that is kept in healthcare has been a hindrance to making or finding the right software to implement.  Additionally, the privacy that needs to be upheld on medical files has slowed the adaptation of data mining technology.  But, persistence and a better working knowledge of restrictions and requirements that needed to be respected, a breakthrough for the healthcare industry.

The basic definition of data mining helps to describe the minimal possibilities for any business that utilizes it.  However, the enormous possibilities available when implemented to its fullest potential, there is almost limitless growth.  This isn’t just within healthcare but for anyone, yet healthcare may be the spheres in which data mining might have the most significant ability.  At one time or another all of us will be a patient, whether for that yearly physical or for something much more dire.  If you are like me, I want all those who are helping me to have as much knowledge available to them, and if that includes previous patterns determined from data collected over many years and patients, I am happy to a beneficiary.  Even if we think we are absolutely unique, there are some features that make us ordinary under the hood (so to speak).

 

The demand for receiving more at a lower cost is a daily mantra.  That goal doesn’t just affect individuals, but industries as well. The healthcare industry is certainly one that has that aim.  Of course, just saying the words doesn’t make everything fall into place.  Much understanding and work needs to be put into action before change occurs.  This is where healthcare analytics is called upon to head up the transformation.

The data that is generated throughout medical offices, specialists and healthcare professionals is both vast in nature and deeply complex.  Organizing the information is just scratching the surface; the need to discern multifaceted patterns is where the strength of knowledge and action can take place, and where analytics is housed.

However, there are steps that must be taken before analysis is implemented, simply stated: data must be collected, shared, and then analyzed.  At present, many offices are collecting data electronically known as the electronic health records (EHR).  The data sharing is happening thanks to the health information exchange (HIE).

Because these terms may be new to you, let’s go into a little more in depth.  An EHR is a patient’s file that contains medical history, but isn’t in a traditional paper format, and is instead stored as a computer file.  This enables the healthcare provider to share the information with other providers, which eliminates human or timely errors that might otherwise occur from transfers of hard copies.

HIE is the secure electronic exchange of a patient’s EHR between healthcare providers as a means of improving the, “speed, quality, safety and cost of patient care.”  When a patient or provider isn’t called upon to transfer a medical file in person, by fax or mail, yet is accessible to appropriate entities, privacy can be better maintained, and there is a lesser likelihood of unintentional mistakes.  The exchange happens in two ways: the first is a healthcare professional forwarding specific information on a patient to another provider.  The second way is a healthcare professional being able to look up the information on a patient, such as in an emergency room visit, and know fundamental information in order to treat that patient most effectively.

Once data is collected and shared, the next step is to analyze it, which is where the true value of information is realized.  But this doesn’t come without a combined support found with business intelligence (BI) and data warehousing.   Technically speaking, many consider analytics to be a section within BI, yet analytics is such a significant subset, that it is partitioned separately from many of the other business requirements.  Analytics is described as the, “data, statistical, and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.”

Real life application of analytics has far reaching capabilities.  Looking at an individual patient who goes into a hospital for emergency care, he/she would no longer be a blank page to all the attending professionals, but with the aid of the HIE, he/she would receive much more accurate and timely treatment.

As unique as we all like to believe we are, humans are creatures of patterns.  Patterns that are created by how our bodies are built; how they are injured and heal; how our minds process stress and pain; and how to predict the future.  All the data that has been collected is analyzed to find the patterns that may not be plausible for a human to identify, but these facts and figures point out when someone fits into parameters of previous patients that showed related symptoms.  Because it is impossible for doctors to memorize every possible condition known, having real-time analytic answers provided will ensure better care.  This method of managing patient treatment can eliminate the need for some testing or medicines in order to discover the root of the issue.

Also, by reducing unnecessary costs on procedures, the overall cost of healthcare can be greatly reduced.  Obviously, there will be basic tests that should always be conducted, but by the process of elimination, a health professional may not be required to conduct avoidable services if a more precise and predictive hypothesis is presented to them.  Essentially, it is taking some of the guessing out of the game.

Analytics can also be used to understand what is going on within a community.  Known as population health management, it is the ability to predict health crises and other visits or hospitalizations.  This is accomplished by way of care teams that provide improved access to care, enable patients to better self-manage their health and reduce the frequency of emergency room visits.  This would then cut costs to both patient and professional, and not divert necessary personnel to the handling of more trivial matters.

Overall, being able to predict the future of where and when staff should be available, how to best provide care for all patients and to do so in the safest and most timely manners, and meet the basic standard of getting more for less.  This applies to people no matter whether they are sitting in a doctor’s office, a specialist’s office, or a hospital.

We live at a time that is completely unprecedented in all of human history: data available at the touch of a button, global communication in a matter of moments, medical procedures and remedies to handle many of life’s wicked twists and turns, and a plethora of other things that make life easier or better. With various devices and data at our fingertips, the realization of personal exposure has become an ever-growing issue. How many places or companies have fundamental information about us in their records? Whether this is from the purchase of a home or vehicle, or maybe from the established medical history at a doctor’s office, we could be susceptible to data mismanagement.

Data governance is defined as the process of managing not only the security but also the quality, usability, consistency and availability of information. There is no doubt that privacy and security of an individual’s records is of the utmost importance, but have you ever thought about the significance of how your data could be used to make your life easier? When an establishment is able to use your information to process payments more efficiently or let you know when it is time to make an appointment, everyone could be a beneficiary.

Looking at the big picture a little more particularly, every day, it is estimated that we create 2.5 quintillion bytes of data. One sector that holds vast amounts of data which needs extensive governance is in the healthcare industry. I’m sure that we have all been through similar sequence of appointments and referrals, such as: family doctor, x-ray technician, specialist to read x-ray, specialist for specific care, and follow up with family doctor. With each visit, there are new forms to fill out, but basically the same questions asked and answered. All of our information is entered into different databases and only accessible at the individual locations. If medical data is forwarded to another doctor or specialist, it must be entered into the new recipient’s database.

The sheer amount of data that is duplicated is part of what adds up to so many bytes of data being created each day. However, if these databases were unified, there would be a greater level of efficiency and less incidence of error. One area of caution that should immediately come to most people’s minds is that of privacy. If information is so readily available to all doctors, specialists and healthcare professionals, how can there be true governance?

Data governance is tightly associated with the strategy that the appropriate data be accessible by the appropriate people. In an efficient association of healthcare experts, a patient’s fundamental information would be entered at one location only. A data file would be created, yet available for the x-ray technician to load images into, and then reachable for the specialist to enter care and prognoses. Because the principal facts have not been recorded by different individuals, there is a smaller likelihood of errors. There are also less chances that subsequent additions to the file will be misfiled, misplaced or altogether lost.
It is understood that an x-ray technician doesn’t need to know the prescriptions that are listed in the medical file, so this information would not be accessible to that party. Thus, privacy is protected and efficiency is still being practiced. This is just the tip of the iceberg, so to speak, of data governance as a means of assurance and improvement in the healthcare sector.

An important aspect of this discussion is the ownership and, consequently, stewardship of all the collected data. Protection of medical reports is not only essential between different departments and specialists, but also from outside invaders. Training, monitoring and security hierarchy must be common focus areas to maintain the utmost isolation within and without the system.

Open access between health professionals to a person’s medical information should not be the only focal point of discussion. Efficiency, privacy and protection must accompany data governance throughout the life cycle of any system of storage and access. There is an evolution that is happening with data, and in this evolution, there are new definitions that have to be established, new protocol to put in place, and growing pains that will be felt. With careful planning, the possibility of seamless communication and more cost-effective production and services can be in the very near future.