Physician Dissatisfaction with EHRs

EHRs: Why are physicians and patients dissatisfied with them?

Electronic health records (EHRs) are supposed to make things easier for doctors, improve health outcomes for patients, and create a better experience for everyone. However, most research indicates the opposite. There is a high level of EHR dissatisfaction among practitioners and the impact on patient experience has been underwhelming. So, what has gone wrong?

When EHR system use became mandated, clinicians were expected to experience initial growing pains as they were forced to learn new skills. However, as comfort levels grew, their perceptions were expected to change over time, resulting in better communication and care. Unfortunately, doctors are still complaining about EHRs even after several years of widespread implementation and use. In fact, research shows that EHRs have become a major contributing factor to physician burnout.

Physician Dissatisfaction with EHRs

Too much manual data entry causes physician dissatisfaction with EHRs.

The aforementioned Mayo Clinic Proceedings study also found that as many as 84.5% of physicians are using EHRs and the majority of them are not satisfied. Most physicians feel that EHRs are inefficent and require too much manual data entry where time is spent on clerical tasks rather than patient interactions.

Likewise, patients are also not satisfied with EHRs as they notice doctors spending more time looking at the computer screen during their visits. Research shows that gaze time (amount of time the doctor looks at the patient) is directly related to patient satisfaction.

Apart from data entry issues, a RAND study identified many other reasons for EHR dissatisfaction among physicians. For example, most physicians agreed that EHR interfaces were not intuitive, thus hampering their workflow instead of augmenting it. They also complain that EHRs are not implemented well enough to facilitate the proper exchange of information. Many physicians feel overloaded with irrelevant information.

Doctors also noticed that templates provided with EHR systems degraded the quality of their reports. Even more worrisome is that most physicians found that EHRs are not improving over time. 

Undoubtedly, these studies indicate the need for a system update and technology that frees doctors from having to spend time on routine clerical or data entry tasks. This technology would ideally enable clinicians to focus on their primary responsibility – carefully listening to, observing, and getting to know their patients so they can provide the highest level of care.

This is where RightPatient can help by providing an AI system that automatically identifies patients when they arrive and then engages with them to collect useful information that is pushed into the EHR system. This enables clinicians to understand much more about a patient’s condition while reducing their data entry burden. With RightPatient, doctors receive concise, relevant, and real-time information regarding their patients to save time, increase efficiency, and improve the patient experience.

RightPatient AI optimizes patient wait times.

Making the most of patient wait times

The U.S. healthcare system has long suffered from the problem of excessive patient waiting times. In 2015, 32% of visits to the ED resulted in patient wait times of up to an hour. Obtaining an initial outpatient appointment with a physician can take a month or more. The fact is that waiting times can be unbearably long for patients and doctors are often helpless in solving the issue.

Long waiting times can have a negative impact on a patient’s health by causing delays in consultations. Furthermore, wait times reduce patient satisfaction scores with healthcare service providers. Research has shown that patient satisfaction scores were affected across almost every aspect of care delivery when waiting times were long, with patients reporting lower levels of confidence in the care provider. Longer waiting times not only impact overall patient satisfaction, they also negatively affect the way that patients perceive the information, instruction, and care provided by their caregivers and physicians.

Clinics have adopted various methods to improve satisfaction while a patient is waiting for an appointment. These typically involve providing information regarding different disease conditions, tips on practicing a healthy lifestyle, etc.; essentially, they their best to make waiting areas comfortable and informative. Additionally, some clinics use office staff to gather information from the patient. However, in many cases, the information provided by the clinic may not be relevant to the patient. Similarly, gathering information about the patient through staff is an expensive activity with limited benefits.

As we have seen, patient wait times can have a negative impact on both patients and their clinicians. However, what if there was a way to utilize these waiting times more productively? Can patients be engaged in a more meaningful way while they are waiting? This is precisely where RightPatient can help.

RightPatient can help to improve the patient experience and optimize wait times through its autonomous check-in process. When patients arrive for scheduled outpatient visits, RightPatient automatically recognizes them and engages through an AI-driven chat session. This enables patients to learn about their conditions as important clinical information is collected, which is automatically fed into the EHR. The clinical team can review this information prior to the consultation, saving time and increasing efficiency by eliminating the need for manual data entry into the EHR system. Physicians can then spend this time interacting directly with the patient to bolster satisfaction and clinical outcomes.

RightPatient enables doctors to spend more time focused on what they want to be doing – listening to patients, addressing their emotional and physical well-being, and spending less time worrying about data entry into health records. Satisfied and engaged patients also respond more favorably to more personal interactions with their caregivers, creating a win-win environment.

opioid abuse epidemic

Reducing opioid abuse by knowing the right patient

The US is enduring a massive opioid abuse epidemic. Not only are they widely prescribed, but prescription opioids are now more widely abused than street drugs. If we look at the anatomy of the opioid crisis, it is genuinely frightening. In 2016, 116 people died each day due to opioid overdose, resulting in more than 42,000 fatalities in a single year.

The question is, why is this happening? How are 11.5 million individuals misusing prescription opioids? How is it that each year, 2.1 million people misuse opioids for the first time? It seems that, at present, there is no clear path to stunting this epidemic. Opioid abuse is already costing the US economy more than half a billion dollars annually.

How did we get to this point?

Since the 1990s, the pharmaceutical industry started pushing opioids and assured doctors that these drugs were safe. Consequently, doctors began widespread prescription of these drugs. However, blaming the pharmaceuticals industry and doctors alone ignores many other pertinent factors.

There have been many changes regarding the prevalence of various diseases over the last three decades. Slowly and steadily, medicine has become dominated by chronic and painful health conditions. It is estimated that one-third or the U.S. population or 100 million Americans are living with a chronic and acute pain condition. Among them, one-fifth are living with moderate to severe pain. Considering these statistics, it follows that opioids would be widely prescribed. However, 8-12 percent of those prescribed opioids result in patients developing an addiction.

Opioid misuse is not just limited to those living with painful conditions. Many of the prescribed opioids end up in the wrong hands. Many addicted to opiates hide their identity or medical conditions and visit various clinics under different aliases. For doctors, it is challenging to identify the right patient.

How can we reverse the epidemic?

To bend the trend downwards, efforts must be implemented at every level. At the community level, we must educate the public and raise awareness about the health risks of opioid abuse. Policymakers should advance legislation to address the problem. Above all, there is a need to change the way medicine is practiced; healthcare providers must take higher precautions at the clinical level.

Clinicians cannot and should not deprive people in pain from drugs that can bring them needed comfort. However, big data and technology can assist them in differentiating between the right patient and the wrong one. This is where RightPatient can play a vital role. Powered by artificial intelligence, the platform can help clinicians to thwart medical identity fraud and ensure that a patient’s complete and accurate medical history can be retrieved.

By recognizing the correct patient, clinicians can better understand the validity of patient complaints along with a patient’s disease history. When and where was the patient last prescribed an opioid? Did the patient rightly identify himself/herself?

RightPatient can be one way to prevent opioid abuse.

RightPatient augments population health investments

How Can You Protect Your Investment in a Population Health Solution?

Healthcare in the U.S. is going to see a paradigm shift in the next five years that will move it from a fee-for-service (FFS) payment model towards a value-based model. Simply said, those who produce better results and improve patient quality of care at lower costs will reap higher dividends. This shift will require better use of technology and significant changes to many platforms and their capabilities, including more investment in big data, analytics, and patient matching systems. These investments in population health management technologies will provide the real-time information needed to make more informed decisions.

Population health solutions play a critical role in moving healthcare from a treatment-based to a prevention-based model. These platforms enable providers to better prepare for patient-reported outcomes, provide data regarding social determinants of health and activity-based costing, and match extracted data outcomes with the right patient.

Current state of U.S. healthcare

The U.S. spends more on healthcare per capita than any other nation in the world but fails to produce better results for life expectancy and other health outcomes. Moreover, U.S. taxpayers fund more per capita on healthcare (64%) than those in other countries, including those with universal health programs.

These facts suggest that encounter-based medicine might be contributing to sub-optimal results in the U.S. and there is a need for change. That change is prompting the rise of population health management and data analytics technologies.

The population-based model is based on aggregating patient data across various health information resources, forming a comprehensive, longitudinal health record for each patient, and leveraging analytics to produce insights that clinical teams can use to improve care and lower costs. In addition to health and financial data derived from electronic health records (EHRs) and medical claims, information such as a patient’s socio-economic status, personal support network, and habitat conditions can be useful in building preventative care strategies.

For example, a patient diagnosed as prediabetes would be classified as high-risk in an encounter-based model. However, this does not take into consideration the patient’s lifestyle and behavioral patterns. Many prediabetics can avoid developing diabetes by modifying habits such as diet and exercise. Patients who smoke, abuse drugs, or have a sedentary lifestyle are much more at risk of developing the disease. Identifying these genuinely high-risk patients requires access to accurate data that is linked to the correct record. 

Challenges in moving to a population health solution

At present, a tremendous amount of patient data is available but it is not unified – it exists within different institutions and across various platforms. Thus, the available information is very difficult to match with the right patient (if not impossible in some cases) and such data has little practical value. Population health solutions need a system that can match patients with their available data and provide information on the best recommendations for preventative care, helping to improve outcomes and save resources.

Therefore, the most important variable in extracting value from a population health solution is ensuring that a patient’s captured data is matched to the correct record. Better data warehousing and mining capabilities will serve no purpose if healthcare providers lack the ability to match the output with the right patient. At present, not only do patient identification issues exist within a single healthcare institution, but these issues become even worse when patient data is exchanged across multiple systems, with error rates rising to 60%.

Failure to properly identify a patient means loss of historical medical history, social indicators, financial information, medications, allergies, pre-existing conditions, etc. – vital information that puts the patient and healthcare provider at greater risk. These data integrity failures can significantly dilute the efficacy of population health initiatives.

In fact, the transition from fee-for-service to value-based healthcare is only going to work if healthcare entities invest in patient matching technology alongside their investments in big data and analytics platforms. These investments should go hand-in-hand since patient matching errors can have such a substantial impact on data quality.

Population health management is among the top six categories in healthcare that are attracting investments from venture capital firms. Other segments include genomics and sequencing, analytics and big data, wearables and biosensing, telemedicine, and digital medical devices.

Thus, the industry is investing in technologies that will play a significant role in value-based care and population health management. However, the success of any population health initiative depends on the right patient being identified every time so that medical records and the corresponding patient data are not mixed-up. Considering the data fragmentation that exists in healthcare and lack of standards around patient identifiers, AI-based systems like RightPatient® are the only way to ensure reliable identification of patients across various data platforms and maximized investment in population health management.

value-based-care-right-patient

Value-Based Care: A Patient-Centered Approach Requires Knowing Your Patient

Aspirin, penicillin, monoclonal antibodies, interventional cardiology, and genome editing have undoubtedly revolutionized medicine. However, while all of these have been breakthroughs in the field of medicine, not much has changed in the way that doctors do their jobs. Patients visit their doctors, the doctors diagnose, they recommend tests, they prescribe drugs, and they are compensated according to the volume of work done or the number of procedures performed.

If medicine is to progress in the 21st century, things have to change at every level, including the way that doctors work and receive compensation, the way they identify the right patient, and the way that patients are treated.

The long-awaited system that is going to change the way doctors work and are compensated will soon become a reality. This new system is called value-based care.

Value-based care is about compensating doctors according to outcomes. This encourages more personal attention to patients and transitions the healthcare system from cure-based to preventive medicine. It is a system in which doctors receive a higher level of compensation for either better outcomes from procedures or enabling patients to avoid health-related problems altogether.

There are several benefits of a healthcare system where the right patient gets the right kind of care.

Value-based care can save patients a lot of money. Putting aside the historical projections of healthcare inflation, the U.S. is also facing major epidemics of chronic, non-communicative diseases like diabetes, high-blood pressure, and cancer. It is no secret that many of these ailments are preventable with timely intervention and/or the correct behavior. Value-based care creates an environment where doctors can help patients to avoid these diseases by intervening at the right time. A doctor would identify the right patient to design a prevention plan before a disease can manifest where things become more complicated and expensive.

Once the right patient, a patient with a high risk of developing a chronic illness, has been identified, the doctor would be encouraged to spend more time with her, teaching her to take better care of herself so that complications can be avoided. There would be a reward system for identifying the right patient and taking timely preventative measures. It would also result in higher patient satisfaction.

A value-based care system would also lower drug costs. Historically, manufacturers decide the price of their medications without taking into consideration the value that a particular drug has in terms of its effectiveness and overall patient wellbeing. A value-based system would also encourage the development of personalized medicine where treatment plans and even pharmaceuticals can be tailored to specific patient needs.

The backbone of the value-based care system would be patient identification and data mining. Many are already demonstrating why medicine should incorporate more data-based modeling to augment physician decision-making.  Data mining helps doctors and the healthcare industry as a whole to better understand the outcomes of various therapeutic approaches. Ultimately, it can help to create the right kind of individualized solution for the right patient.

Unfortunately, realizing optimal results from data mining and value-based care has its challenges, especially as healthcare organizations start mining data that has been accumulated over long periods of time. On average, at least 8% of hospital patient records consist of duplicate data. Thus, an intelligent way to sort out these duplications and identify the right patient is desperately needed.

It is stated that value-based care is about the right patient getting the “right care, in the right place and at the right time.” Instead, the maxim should be, “RightPatient® enables the right care, in the right place, at the right time.”

RightPatient® guarantees that a patient medical record is never mixed up with another record and the hospital ecosystem will always recognize the patient with the help of cognitive vision. Mistakes from common patient names, fraud, human error and other issues are always prevented.

As we all know, chains are only as strong as their weakest link. In many hospitals or medical institutions, there is an urgent need to strengthen this weakest link throughout the entire system – overcoming the errors of false identity and data duplication with RightPatient. Only then can the benefits of value-based care and data mining be fully realized.

How Opioid Abuse Exposes Hospitals

How Opioid Abuse Exposes Hospitals

Whenever I’m talking to a healthcare provider about RightPatient, the topic of “frequent flyers” inevitably arises. For those who might not be aware, frequent fliers are patients that use different aliases to obtain healthcare services. It’s estimated that between 2-10% of patients arriving at the emergency department (ED) provide some kind of false or misleading information about themselves. Typically, these patients are lying about their identity to obtain prescription medications, and most of these are for opioids.

Since these patients lie about their identity or demographic information, hospitals often end up writing off a considerable amount of money for their services – up to $3 million annually on average. Aside from these financial losses, frequent fliers also pose other risks to providers that are associated with patient safety and quality of care. Why? Because they also frequently lie about prescription drug use or addiction.

What’s worse is that this behavior is not limited to frequent fliers. Any patient can lie about their addiction. Many of these patients lie about their addiction to opioids, specifically. As we all know by now, the U.S. has a serious problem with opioid addiction, a crisis that killed over 33,000 Americans last year. This crisis has no rules or boundaries, and does not seem to select for a particular demographic. Anyone is susceptible to getting hooked on opioids because they are so addictive.

The opioid epidemic has far-reaching consequences that extend beyond the health of the patient; however, in the ED, this is the primary concern of a clinical team. Considering the circumstances, this question seems relevant – “how can healthcare providers ensure high quality of care when patients lie about their identity and/or drug use?”

RightPatient can play an important role in helping to answer this question. Our AI platform can accurately recognize the patient and offer key clinical insights by detecting patterns in the patient’s appearance over time. Clinicians won’t need to rely on the words lies coming out of a patient’s mouth, patients with no ID, or expensive tests. RightPatient automatically knows who the patient is and whether or not they are at risk of opioid abuse.

ED nurses who suspect a patient of abusing opioids will typically search the patient’s belongings to make sure they aren’t prescribed something that could cause an adverse event or even kill them. Unfortunately, the human eye, clinical intuition, and patient reliability have many shortcomings. Luckily, RightPatient can augment clinical diagnostics with cognitive vision to help fight the opioid epidemic and save a lot of lives and money in the process.

healthcare personalized data

Data Personalization in Healthcare

Personalization in healthcare – everyone talks about personalized medicine, how about data personalization?

One size does not fit all. Thus, medicine is seeing a shift from a standard model of care to a personalized model of care. The emergence of cloud computing, wearables, machine learning, and continuous progress in data management has made the delivery of personalized medicine more possible. Personalized data along with predictive analytics would change the way medicine is practiced today. It has the power to create a proactive health system that can help to address diseases at their earliest phase. Personalized data will help to craft medical solutions “especially for you,” rather than a single solution for all. It would also help consultants to free up time so that they can concentrate on developing a lasting and trusting relationship with patients.

When the human genome was first decoded, there was tremendous excitement about the ability to predict diseases and provide personal health solutions; however, soon it became evident that our overall health cannot be determined by analyzing small snippets of our DNA, as valuable as they might be for understanding specific risks. Medical or health-related decisions cannot be made in the absence of better personal data or a more holistic understanding of the person being treated.

With the help of personalized data, it would be possible to shift from the so-called model based on diagnosis and treatment to one of early disease detection and even a predictive model of medicine, along with personalized solutions.

Traditional medicine has depended not only on the phenotype and genotype data but other variables as well – a personal relationship with the patient, understanding patient lifestyle issues, surroundings, life events, social and family conditions, and much more. Data personalization can help to bring back that edge to automated systems through the customization of data. Data personalization is about delivering the right information about the patient, to the right person, at the right place, at the right time, in the right way. More than ever before, this is now conceivable due to better availability of personal data, personal devices, services, and applications.

Data personalization would make it possible to create a reasoning engine that has the ability to predict and make recommendations by using personal data of the patient provided by various resources.

Data personalization can take personal medicine many steps forward by adding the human touch and predictive analytics.

Perhaps in making medicine personal and predictive, personal information is what seems to be missing. If included in the algorithm, it would surely make predictive analytics more accurate and dependable. Personalized data would help to serve patients in the best possible way by shifting focus from merely disease determination to prevention, timely intervention, and better treatment.

Data personalization should not be taken as something new in medicine; in fact, it is a more natural way of providing health services, and closer to the traditional practice of medicine as it is about integrating the psychological, behavioral, and other measures that have become possible due to improvements in technology.

Combining the human biology with existing knowledge of epidemiology and clinical medicine would result in more personalized care. It is more like giving a human touch to the technology – something that has been a characteristic of traditional medicine, supporting the notion that doctors know their patients far better when a closer relationship is established. Thus, personalized data can augment that missing human factor in modern practice.

As more electronic personal data becomes accessible, systems become more intelligent. Having better learning capabilities and better availability of personalized data would revolutionalize the way we provide healthcare.

 

using healthcare analytics to make smarter decisions

Healthcare Analytics: Reshaping the Future of Healthcare

using healthcare analytics to make smarter decisions

The collection and interpretation of healthcare analytics is fundamentally changing modern healthcare delivery. (Photo courtesy of pixabay.com)

The following guest post on healthcare analytics was submitted by Yeshwanth HV. 

As healthcare enters the digital age, the practice of medicine will change for the better. It will move away from the clasp of largely reactive decision-making, which was inaccurate and expensive to say the least, and into the realm of evidence-based medicine; thereby becoming more proactive, connected and personalized. In simple words this means that the days of long trips to hospitals that culminated into a series of referrals followed by questions and answer sessions, and tests that were repeated over and over again will be a thing of the past. Patients will start receiving treatments and be prescribed medications that are customized as per their unique needs. With comprehensive medical information about the patient along with a repository of knowledge base that includes every aspect of treating patients with similar medical conditions, care teams will be able to devise accurate healthcare plans that can mitigate any harm to life or safety of patients.

If you think that this form of care is too “futuristic” and can only exist in animations shows such as ‘The Jetsons,’ let me get you acquainted with the reality. The truth is this form of care is already happening and will eventually be integrated into every routine healthcare protocol.

The ‘magic’ that made this possible

Yes, hospitals and other healthcare practices have scrutinized operational and financial data since ages, but the magic started happening when they started to track and analyze healthcare data. When healthcare related data is gleaned from a variety of sources – starting from EHRs and disease registries to direct patient surveys and even digital health devices used by individuals – providers can obtain a well-rounded view, which enables them to analyze every patient, understand their needs and proactively reach out to provide personalized care. When diligently used, the intelligence gained from analytics can move beyond improving healthcare outcomes and give a new lease of life to a hospital’s bottom line.

Factors driving this move towards analytics

The first reason is the cost. It is no secret that ‘reactive’ healthcare is a lot heavier on the pocket when compared to ‘proactive or preventive’ healthcare, which is essentially conceived with an objective to keep individuals out of costly healthcare settings such as emergency rooms.
The other crucial factor that is encouraging this trend is the shift from fee-for-service model to accountable, value-based care models that essentially link quality of care and reimbursement. For healthcare providers, this move means that their survival depends upon the usage of analytics to streamline financial and operational performance of the organization.

How does it work?

Not long ago, providing evidence-based treatment meant that hospitals had to follow a series of well-tested care protocols. However, with greater access to healthcare data and advancements in analytics, we have entered a new era of evidence driven care. By accumulating and analyzing data from diverse feeds over an extended period of time, care providers can understand the exact reasons for bad outcomes and therefore realign their strategies to provide most effective care to individual patients as well as to a particular section of patient populations.

Healthcare providers can also leverage analytics to recognize patterns in a population’s health and precisely estimate individual risk scores. Based on these scores, they can priorities the work of individual healthcare team, allowing them focus more time on the most vulnerable individual.
What’s more healthcare analytics, whether based on risk assessment, EMRs or claims data, can categories patients prior to service and tackle a potential concerns before they pose any real threat to the patients. It can also quantify everything – from emergency room visits, treatment outcomes and readmissions to wait times and utilization of expensive services – and offers a level of transparency that is good for both healthcare outcomes and for business. For instance it can help providers to set up internal benchmarks to gauge quality and cost performance, and provide a detailed understanding of how well they stack up against their counterparts. It also can help hospitals to swiftly make crucial decisions pertaining to reducing costs, optimizing resources, improving care quality and enhancing their competitive positions.

The benefits of healthcare analytics on the patient side are also equally compelling. By arming patients with timely and relevant information, and enabling them to have an extensive understanding, healthcare analytics has opened up a new era of customized healthcare.

Conclusion: Changes are coming; be ready to embrace it

In developed nations the usage of healthcare analytics is growing at a rapid pace. As a result of this, very soon the roles of patients, physicians, hospitals and other healthcare organizations will see some drastic changes in the coming years as mentioned below-

• Patients will become better informed and assume more responsibility for their own care

• Physicians will assume more of a consultant role than a decision maker and will advise, warn and help individual patients. They will start witnessing more success as care becomes more accurate and proactive. And they will have more time to interact with patients and build long lasting relationships

• Hospitals will start witnessing fewer unnecessary hospitalizations, resulting in revenue losses initially. However, overtime, admissions will become more meaningful, the market will adjust, and accomplishment will rise

All in all, changes are coming. Be proactive and ready to embrace the new world order that will take healthcare to the next level.

Author Bio:

Yeshwanth HV is a healthcare writer employed by MedBillingExperts, a leading provider of healthcare business process outsourcing services such as medical billing, medical coding, medical records indexing and healthcare analytics services to medical practitioners and healthcare organizations worldwide. Dedicated towards the healthcare industry, he has authored several blogs and articles that have received rave reviews in the industry. Prior to MedBillingExperts, Yeshwanth worked with CIO Review and has authored several bylined pieces for the quarterly editions of the magazine.