14 Factors Driving Quality Improvement in Healthcare
Quality improvement is an ever-evolving area of healthcare. It reaches every aspect of the healthcare industry including patient care, facility management, and financial processes, just to name a few. With a growing population, distaste for the healthcare system at an all-time high, and rising medical costs, finding ways to keep up with the physical, fiscal, and technological demands of patient care and facility administration while focusing on quality improvement can seem a daunting task.
Fortunately, understanding the major factors driving quality improvement can help healthcare facilitators make better decisions, employ the most beneficial systems, and successfully provide the level of customized care their patients deserve.
The factors influencing quality improvement in healthcare are many, but they tend to fall into three main categories: patient-related factors, provider-related factors, and environmental factors. These three categories were put forth in a 2014 International Journal of Policy Management article by Ali Mohammad Mosadeghrad as a way to catalog his findings. While Mosadeghrad was focusing on quality improvement in an exclusively Iranian context, these categories provide a useful organizational structure for quality improvement in a more general context, as well, so we will use them here.
Patient-related Factors Driving Quality Improvement
Patient-related factors are perhaps the most obvious or apparent factors to consider when studying quality improvement. After all, patients are the ultimate recipients of everything the healthcare system does, produces, and decides, so the patients themselves have an important hand when it comes to informing the system of the ways in which it can improve quality and create a more effective and efficient system.
While there are multiple patient-related factors that influence quality improvement, five main sub-factors seem to be the most influential: patient illness type, patient cooperation, patient education, patient beliefs, and patient’s socio-economic status.
Patient Illness Type
Responding to the needs of patients with various illnesses can pose a challenge to the healthcare community as a whole. Though there are facilities that treat a narrow margin of the population due to their focus on a particular illness or group of illnesses, the individual complications, and presentations that can occur can still be challenging. The way in which healthcare facilities and administrative organizations view, take data on, and respond to the needs of patients is heavily influenced by the types of illnesses present in any given facility’s population.
For example, certain illnesses require treatment with or are diagnosed with specific medical technology. If a facility isn’t aware of the diagnostic makeup of their population, incorrect decisions can be made regarding technology acquisition, staffing, and other crucial areas.
Understanding the diagnostic makeup of any given population can be a major factor that drives quality improvement. Progress made toward better serving a given population and making more effective treatment options available can greatly increase patient well-being and overall health.
The extent to which patients are willing to cooperate directly affects quality improvement. Doctors and caregivers rely on accurate information, and patients’ willingness to be forthright can sometimes hinder the quality of care. For example, if a patient is told not to drink alcohol while taking a certain medication, their failure to follow that instruction can lead to serious consequences. Sometimes, patient cooperation isn’t possible, such as during a medical emergency in which the patient is unconscious, and if reliable databases and patient management systems aren’t in place, it can be difficult for doctors to make the right decisions. On the plus side, when patients cooperate with physicians and the system and become active players in their healthcare, quality improvement can be greatly improved. Creating systems based on machine learning and data analytics can help bridge the gap between patient and caregiver by helping healthcare facilitators make better decisions and provide more personalized care. This personalized care leads to higher morale among patients and makes patients more likely and willing to participate in their own healthcare, which can positively influence quality improvement intervention efforts.
A patient’s education level, both generally and about their own health, is also a factor where quality improvement is concerned. Patients who have limited educational resources may not know the questions to ask or which symptoms are important to bring up. An uninformed or uneducated patient can also mean an incomplete and incorrect diagnosis. Data-related processes can help inform healthcare facilitators and organizations about the population they serve, making room for more accurate and relevant educational programs. These programs can put patients in a more powerful position in that they can discuss their symptoms and concerns more accurately and provide physicians with the information they need. This increased education and knowledge base among patients will ultimately lead to great quality improvement and patient health.
If patients have negative attitudes towards or incorrect assumptions about the healthcare system or any part of it, that can hinder quality improvement. Patients who expect to receive less-than-adequate care, encounter problems, or experience negative feelings or emotions about healthcare are often less likely to cooperate with physicians and caregivers and might not seek out care at all. One of the chief complaints among patients nowadays is the lack of personal attention they feel they receive. Patients in the United States are also reporting high levels of distrust in the healthcare system which, as Katie Armstrong et al. uncovered back in 2006 and published in the Journal of General Internal Medicine, correlates with low levels of self-reported health. Data analytics systems with machine learning capabilities have shown promise and in many cases have been able to provide the information providers need to make patients feel important, cared for, and valued. This fusion of AI and healthcare administration has made it possible for doctors and healthcare facilitators to learn about their patients and populations over time. AI has allowed physicians to learn which medications perform the best, which patients are more prone to get infections, how various conditions respond to certain treatments, and more. This information has allowed doctors to provide better, more personalized care that makes the patient feel valued and safe, which boosts trust in the healthcare system and does a great deal to aid quality improvement efforts.
Patient’s Socio-economic Status
Rising health costs have adversely impacted many patients. Most of these patients exist in specific socioeconomic demographic groups, and many of them have restricted access or no access to insurance and quality healthcare. Community programs and government assistance programs can help, but sometimes even that doesn’t help enough to allow these patients to obtain regular care. Inconsistent visits can leave a noticeable gap in the patient’s chart, if there is one, and the quality of care can be impacted. However, machine learning and AI can provide healthcare facilitators with information on various populations as a whole and can also lead to lower costs. This information can be anything from which diseases and ailments are most common, which tests should be run, and health trends in various populations over time. While nothing can come close to the accuracy of an individual file, in areas where socioeconomic disadvantage has made consistent individual records possible, population data can provide a wealth of information that gives healthcare providers a data-driven starting point to provide more efficient and effective care.
Provider-related Factors Driving Quality Improvement
Patient-related factors play heavily into quality improvement interventions. However, patients are only a third of the overall picture. Providers are another third. A lack of competence or inadequate resources on a provider’s part can be just as damaging to quality improvement efforts as lack of patient cooperation or limited access to healthcare. As such, it’s vital to consider provider-related factors when discussing the major factors driving quality improvement in healthcare.
If providers aren’t able to provide competent care, quality improvement efforts can be nearly impossible. A system that relies on incompetent caregivers cannot expect to achieve an improvement in the quality of the care it provides. Fortunately, databases, machine learning programs, and AI, when combined with an effective healthcare management system, can provide for a more effective and capable system of care by giving physicians and facilitators the information they need. Additionally, the increased efficiency that these systems provide tends to create more time for training and professional development, which also adds to the level of competency among healthcare providers. A caregiver can only be as competent as the information they have, the training they receive, and the skills they acquire, and quality improvement interventions aimed at these areas will undoubtedly result in improved quality of care and competency across the board.
Provider’s Access to Information
As mentioned above, it’s critical that healthcare providers have adequate access to information. The most obvious example of this is access to patient records, but there are other forms of information that are just as important and can help physicians provide better care. For instance, population data on a certain region might indicate that a certain virus is spreading quickly. If a provider has access to this information, they can make preventative care decisions that can save patients from becoming ill and can save money for the patients, too. Other forms of information that providers need access to include professional development, information about new technologies, network-wide information, community information, financial information, and more. Utilizing technology and AI to put the information providers need at their fingertips, no matter where they are, is a critical step we can take as an industry towards successful quality improvement.
Provider Mindset and Motivation
Healthcare providers work incredibly hard and do amazing work for their communities. However, even doctors and nurses aren’t immune from the fatigue and discouragement that can come when other parts of the healthcare system aren’t functioning as they should. Government regulations, working conditions, job-related frustration, and extended hours can all play into provider mindset and can cause discouragement and frustration. Many studies have indicated that job burnout and work-related stress is a real issue for many physicians and other healthcare workers, as indicated in this study published by the Premier Safety Institute. When healthcare providers are working with a frustrated mindset, motivation to perform at their highest level can fade. This can lead to a lower quality of care, whether perceived or actual and can cause patients to feel let down or unimportant. However, improving the healthcare management systems at play in a hospital or organization can drastically improve the working conditions, team atmosphere, scheduling, and financial strains that can often contribute to low morale among healthcare providers. This boost in morale tends to lead to higher motivation levels among healthcare staff, a better-perceived experience on the part of patients, and improved customer service, all of which can lead to quality improvement.
Many healthcare providers feel that ongoing education and professional development is lacking. When providers feel that they aren’t receiving the information they need, confidence levels can decrease and the problem of lower morale and motivation that we just discussed can become more prevalent. It’s critical that providers of all levels and occupations, from administrative employees to surgeons to hospital executives, get the training they need. Using systems and databases that remember when training has occurred, who’s up for license renewals and professional development courses, and where the scheduling can tolerate the absences necessary for that professional development can greatly improve efficiency. This can lead to more opportunities for professional growth and training and, ultimately, to quality improvement across the board.
Environmental Factors Driving Quality Improvement
Finally, environmental factors can add substantial amounts of stress to an already challenging work environment. From the healthcare system as a whole to leadership and management issues to resource availability and facility management, environmental factors can weigh heavily on the day-to-day activities of healthcare providers and their staff.
The healthcare system as a whole is a macro-environment of sorts. It’s the industry in which all healthcare takes place, and there are times when the state of affairs in the overall system can work its way into local and regional networks. For instance, if new regulations are passed that make it more difficult for nurses to provide care, that systematic change affects all providers in the system. It’s important to factor in the overall environment when determining which changes should be made and how to best enact quality improvement interventions in a healthcare setting.
Leadership and Management
Hospital and healthcare leadership is under tremendous strain from all sides. They have to deal with some of the most complex issues facing American healthcare providers today. For instance, they have to deal with ever-changing healthcare regulations and the decisions made by policymakers. Additionally, they have to find a way to provide the best care at the best prices possible while still maintaining a bottom line. There are demographic concerns to consider, as well, including the so-named “Silver Tsunami”. America has become an aging population, and, as Mike Taigman wrote in an EMS 1 column, this wave is “having a powerful impact on strategic capacity planning throughout healthcare.” Epidemiology is a concern, as well, because for the first time the healthcare system is no longer dealing with single-disease issues but is facing an ever-increasing number of patients who have multiple diseases, many of them chronic or autoimmune in nature. Fortunately, predictive analysis and AI can help tremendously in this area by providing accurate and timely data on health trends, population makeup, most effective treatments for various combinations of illnesses, and so on. However, not every healthcare provider has access to this technology and those who don’t often feel the strain. That strain inevitably becomes a major obstacle to quality improvement efforts.
Resources and Facilities
Contextual factors, such as localized issues and facility-specific influences, often play heavily into issues regarding quality improvement. A 2017 article in the Systematic Reviews Journal by Emma Cole et al. discusses at length the effects that contextual issues can have on quality improvement efforts. While it’s difficult to understand and adjust for every contextual situation, creating predictive software that can gather data over time and provide analytics both in the micro-environment of a single facility and in larger networks can help alleviate some of the stress that resource and facility specificities can create.
Additionally, and more simply, the issue of basic access to the resources and facilities necessary to carry out healthcare functions is a challenge to many. Understanding which resources, facilities, and services would most greatly benefit the community within which such facilities exist or might be built can help target the scope of services, saving money and improving patient care in the long run.
Partnership Development and Teamwork
If it’s true that you’re only as strong as your weakest link, then healthcare systems, in particular, should be cognizant of partnership development and teamwork. However, forming micro-environments such as teams of professionals or networks of complementary facilities can be hampered when no available connectivity platform exists.
As indicated in this article by HRH Global Resource Center, teamwork among healthcare professionals is becoming increasingly important due to many factors such as the increasingly specialized and complex nature of clinical care. As such, AI-powered systems that increase connectivity and facilitate teamwork can be of great help to quality assistance.
Data Accuracy and Distribution
Finally, the more access healthcare professionals have to data the better. This is a straightforward fact that everyone in the healthcare industry understands. Widespread, accurate data (in the right hands) that is protected, secure, and available to the healthcare professionals who need it, when they need it, is the best way to facilitate substantial quality improvement in patient care. Systems that are created to help facilitate this data distribution, protection, and accuracy can only aid quality improvement intervention efforts.
It’s clear that the world of healthcare is changing, but that’s because the world itself is changing. The clinical makeup of patient populations is no longer a largely single-disease population. Many patients have more than one illness, present with chronic illnesses, or are living with autoimmune diseases. Socioeconomic factors make access to healthcare more difficult for some populations than it is for others. The public perception of healthcare as a whole is changing, too, and it’s the responsibility of the healthcare system to meet these challenges head-on. Healthcare management solutions must take into consideration the patient, the provider, and the environment when creating software and systems that seek to aid quality improvement and overall success in the healthcare industry. It’s a tall order, but it’s not impossible, and if healthcare professionals take these factors into consideration, successful quality improvement intervention is a definite possibility.