Monday, September 27, 2021

How to successfully outsource chronic care management at a small medical practice September 20, 2021

A great deal has changed in healthcare delivery since 2015 when CMS began to formally recognize the importance of Chronic Care Management (CCM) and reimburse it under its own distinct billing codes. Health technology has made remarkable advancements, the size of the senior population continues to grow tremendously, and the global pandemic has resulted in years of digital transformation taking place in a matter of months. These advancements couldn’t have come soon enough, as 6 in 10 adults have at least one chronic condition today, accounting for 90% of U.S. health expenditures annually.


Still, many small physician practices are hesitant to work with third-party organizations to implement formal CCM or remote patient monitoring (RPM) programs. Many are apprehensive about opening sensitive patient medical charts to a third party, and others are concerned about the operational challenge of managing a remote staff that they neither hired nor trained. Further, physicians may also be wary of allowing remote care managers to contact patients they’ve never personally met and unsure how patients will react to this change.

While all of these are valid concerns, there is a strong argument to be made that the benefits of implementing CCM programs far outweigh any potential onboarding or integration snags. Incorporating a dedicated CCM program can provide many operational and financial benefits to practices regardless of size, thus improving outcomes for patients overall.

Operations:


Successfully implementing a CCM program starts with laying the operational groundwork: integrating new staff and installing healthIT software. Committing time and resources into integrating remote care managers early on is critical to reaping the benefits long term. By treating remote care coordinators as true extensions of your staff, adapting to the culture, tone, and workflow of your practice, they eventually become integral parts of practice operations, and in some cases, may even be willing to work in the office a few days a week to get additional face-to-face time with practice staff and patients. This frees up existing staff to manage other important tasks.

Another element critical to a successful CCM program is ensuring seamless integration or cooperation with your practice’s existing EHR software. Adding another health IT system into your practice is not always easy, but given that staff members spend 90% of their day in our EMR, finding integrated EHR and CCM solutions can save a great deal of time, money, and resources.

Patients:


After explaining the role of care coordinators and getting patients to answer that initial call from an unfamiliar number, the connection between patients and care coordinators can become a lifeline for many. Whether they are supporting medication adherence by reminding patients to fill prescriptions, following up on issues addressed during in-office visits, or providing mental health check-ins, care managers can fill important gaps between in-office visits. After following up with the same patients each week or month, these remote coordinators often build as strong of relationships with patients as we do as providers.

Additionally, with the help of connected RPM technology, care managers are able to stay even more closely connected to high-risk patients, remotely collecting analytics such as a patient’s blood pressure, oxygen and glucose levels, or even sleep quality, then relay that information to the provider. Using these real-time analytics to inform a patient’s care can have a dramatic impact on outcomes and increase patient satisfaction.

Supporting the Practice:


With a CCM program, practices can put themselves in a stronger financial position by earning revenue for the services many are already providing, such as primarily patient phone calls. Today, over 80% of patient communication is over the phone, with even small family practices averaging between 1500-1600 calls per week.

Without CCM, these calls can represent an immense burden for a small administrative staff, as well as a missed source of revenue from these billable actions. By implementing a formal CCM program and enlisting the support of trained care coordinators, burdensome administrative calls become opportunities for substantive care management, with coordinators collecting additional health information on the practice’s behalf and inputting it directly into the patient’s record for the physicians to view.

During the pandemic, CCM became a lifeline for many practices like mine, helping to fill gaps in care while our staff dealt with other issues related to COVID-19. Even as we emerge from the pandemic and into healthcare’s evolving business and regulatory environment, CCM can enable even the smallest of practices to not only breakeven but come out ahead, all while improving overall patient care.


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Sunday, September 26, 2021

How analytics helps at the point of care

The amount of patient and medical data generated by providers, payors, labs, researchers and connected devices has increased exponentially in recent years as healthcare organizations embraced digital technologies. That’s good news because more data can theoretically lead to better care and greater efficiency.


The unfortunate reality is that providers and other healthcare organizations easily can be overwhelmed by the firehose of raw data emanating from connected devices, lab tests, and patient visits to primary care providers and specialists.

To unlock the clinical and operational value of data from disparate sources, many healthcare organizations are turning to advanced analytics platforms that are able to detect patterns which can inform clinician treatment decisions and process improvements that reduce costs. This is critical as providers move toward value-based care (VBC) revenue models that reward outcomes and better cost and utilization control. Four important areas where analytics can drive VBC include: addressing Social Determinants of Health, understanding patient-generated data, bringing payor data to the point of care, and applying best evidence into practice.


Using Advanced Analytics to Address Social Determinants of Health


Physicians are able to provide better care when they understand all factors that impact the lives of their patients. This type of holistic view includes an awareness of a patient’s neighborhood and surrounding environment, their access to transportation, their ability to purchase healthy foods, their employment status as well as many other social determinants of health (SDOH). In fact, these social determinants have been found to be the most important indicators of health, and clinicians must have access to this information to improve health outcomes and address health disparities.

A 360-degree view of the patient is also an essential tool for providers who are moving to alternative payment models that reward better patient outcomes and lower the costs of care. In these new models of care delivery, SDOH data is required to understand the overall healthcare needs and risks within a defined population.

Fully leveraging SDOH for patients and providers requires the use of advanced analytics to help sift through volumes of data for actionable information. SDOH and advanced analytics combined can:
  • Identify non-clinical factors that drive health inequities and impact outcomes
  • Identify interventions that can ameliorate health disparities
  • Close care gaps by addressing health behaviors
  • Accelerate the transition to value-based care models

Making Sense of Personal Device Data


I have seen health systems build digital platforms that allow them to accept patient-generated health data. For example, many patients now rely on devices such as Apple Watches and electronic scales to monitor their own health and are excited to share their data and progress with a healthcare provider.

As you might imagine, the amount of raw data generated by these personal devices can be overwhelming! It is impossible for a primary care physician to wade through the large volumes of raw device data to find pertinent health information for multiple patients per day. Analytics tools are the answer; these tools must be used to assist providers in detecting patterns and trends that indicate a patient may have a health problem that needs to be addressed.


Understanding Payor Metrics and Using Payor Data


Many providers receive individualized reports about their performance from a number of different payor organizations. The problem is that each payor often has different reporting standards and the reports come in different formats and sources. For example, some payors may send patient information in a hard copy letter while others use an online portal.

Patients may change their jobs and health insurance providers often, which means that an individual’s information from the payor may not be up to date, may be formatted in disparate ways, and may even have different metric definitions. Many providers set aside these payor reports and metrics because they are so difficult to translate at the point of care.

A best practice in using payor data and reports is for health systems to negotiate for standard metrics that can be interpreted at a system level and to use advanced analytics to capture and integrate payor data at a system level to enhance their clinical data.


Keeping Up with Medical Research


Every week there are dozens of important research papers published in medical journals that include information that should be applied in a primary care practice. This is particularly true with precision medicine where new impactful research seems to be released almost daily. In this modern age of medicine, it’s a full-time job to keep up with and apply the latest evidence.

Decision support tools within electronic medical records (EMR) may assist with this process; however, these tools may take many months or even years to be updated with the latest evidence. Furthermore, decision support tools within the EMR may not help to quickly identify patients with specific conditions or gene variants who could benefit from emerging evidence about new therapies.

With advanced analytics tools, healthcare providers can improve the way that they integrate evidence into practice and care for their populations.


Applying Analytics Before, During, and After the Point of Care


Having relevant patient data at the point of care is indisputably critical to practicing effective medicine. The process for integrating analytics into the point of care can be challenging, though there are four approaches that must be used to be effective.

First, physicians require data for pre-visit planning. In addition to a list of all patients being seen at the practice that day, physicians need details of these patients’ care needs to ensure the practice has all of the necessary supplies available and have a plan in place for how care should be delivered that day.

Second, at the time of care delivery, clinicians need information at their fingertips to ensure that the patient is receiving all the care they need. This information is often delivered within the EMR and should mirror the information used in the pre-visit planning phase.

Third, the care team needs information after the visit to understand their effectiveness in managing the care plan and to identify any errors made during the patient’s visit. This information is needed quickly so that all members of the care team can reflect and better understand any factors that could have led to an error in care delivery.

Finally, the care team needs analytics that look at the entire patient population to understand who may need care or had a change in their condition and requires additional attention. The care team can then engage with patients to ensure that they are aware of any needed services and have access to a provider. For example, if analytics tools identify a patient with poorly controlled diabetes who has not been seen in the last nine months, a care team member can contact her to re-engage with her provider right away.

Now more than ever, there is more patient and medical data available to clinicians. Yet this data does little good if it can’t be accessed and understood by physicians and other team members before, during and after the point of care. Advanced analytics is the key to unlocking the clinical and operational value of this data.


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Saturday, September 25, 2021

Development of Myopia in Children Linked to COVID-19 Pandemic

As a result of the COVID-19 pandemic and increased lockdowns, leading to increased use of online courses in education, there have been concerns on the impact on children of increased digital screen time worsening the global burden of myopia.

Xiao Yang, MD, PhD, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, and a team of investigators aimed to investigate changes in the development of myopia in young schoolchildren during the COVID-19 outbreak period in China.

In this study, results showed an increase in the development of myopia during the outbreak period in young schoolchildren, with the proportion of children at-risk of developing myopia also increasing during this time period.


Methods


Ahead of the present study, an ongoing prospective study was initiated in November 2018 to determine distributions and annual changes in refraction and axial length (AL) in schoolchildren.

Accordingly, this study analyzed a subset of the data to evaluate the association between environmental changes during the COVID-19 period and the development of myopia. Through November - December 2018, all grade 1 and 2 students from 12 primary schools in Guangzhou, China were enrolled, with follow-up exams performed annually.

Thus, the subjects were organized into the exposure group (n = 1572) which included students in grade 1 in November and December 2018 who were then assessed from grade 2 (November - December 2019) to grade 3 (November - December 2020) during the COVID-19 pandemic.

On the other hand, the non exposure group (n = 1472) consisted of students in grade 2 in November and December 2018 assessed from grade 2 (November - December 2018) to grade 3 (November - December 2019) in the period prior to the pandemic.

Each visit consisted of the evaluation of uncorrected visual acuity using the Early Treatment Diabetic Retinopathy study chart, accompanied by cycloplegic autorefraction performed using a desktop autorefractor.

In addition, investigators defined myopia as a spherical equivalent refraction (SER) of -0.50 D or less, with emmetropia defined as SER greater than -0.50 D and less than +2.00 D, and hyperopia defined as +2.00 D or greater.

Main outcomes in the study included changes in cycloplegic SER, axial length (AL) elongation, and myopia incidence from grade 2 to grade 3.


Results


Within the study, a total of 2679 eligible students were included in the analysis, with a mean age of 7.76 years and 1422 (53.1%) males.

Of that number, 1207 students were included in the exposure group examined in November and December 2019 and 1472 were in the non exposure group examined in November and December 2018.

Following 1 year, 2121 of the 2679 grade 2 students (79.2%) were reexamined, with 7 students reported receiving orthokeratology treatment and unsuccessful cycloplegic refraction, leaving 2114 students in grade 3. This included 1054 students in the exposure group and 1060 in the non-exposure group.

In grade 2 students, SER and prevalence of myopia were not different between the 2 groups, while the mean AL was 0.11 mm (95% CI, 0.05 - 0.16) shorter in the exposure group.

Then, data show from grade 2 to grade 3, students in the exposure group experienced a larger 0.36 D (95% CI, 0.32 - 0.41, P <.001) myopic shift of SER and 0.08 mm (95% CI, 0.06 - 0.10, P <.001) greater AL elongation, in comparison to the non-exposure group.

Further, the incidence of myopia was 7.9% (95% CI, 5.1 - 10.6, P <.001) higher in the exposure group, compared to the non-exposure group.

Additionally, the mean SER in grade 3 in the exposure group was 0.35 D (95% CI, 0.25 - 0.45) more myopic compared to the non-exposure group. The prevalence of myopia in grade 3 (n = 219 of 1054) was 7.5% higher (95% CI, 4.3 - 10.7) in the exposure group, compared to the non-exposure group (n = 141 of 1060).

Data also show the proportion of children without myopia and with SER greater than -0.50 D and less than or equal to +0.50 D increased from 31.1% (n = 286 of 919 students) to 49.0% (n = 409 of 835 students).


Conclusion


Investigators concluded the period of outbreak of COVID-19 showed accelerated development of myopia in young Chinese schoolchildren, as well as increasing the risk of developing myopia in children without myopia.

They noted their concern that the incidence of myopia may remain high, even after the COVID-19 pandemic outbreak period.

“Moreover, behavior changes, including reduced time outdoors and increased digital learning, may persist beyond the period of the pandemic, heightening the risk of a prolonged acceleration in the progression of myopia,” investigators wrote.

The study, “Rates of Myopia Development in Young Chinese Schoolchildren During the Outbreak of COVID-19,” was published online in JAMA Ophthalmology.


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