Tools and Capabilities Required for Value-Based Care

Tools and Capabilities Required for Value-Based Care

Mark Weisman, Chief Medical Information Officer, TidalHealth System

Mark Weisman, Chief Medical Information Officer, TidalHealth System

“We are moving towards value-based care” is a great set of buzz words. We have all used them, especially if we need to impress the board. A line such as “We are positioning ourselves to be at the forefront of value based care through the utilization of artificial intelligence, telehealth and blockchain” contains enough buzz words in it to guarantee a promotion. Try it out and send me a thank you note when they make you CEO. However, the technology that is really important in value-based care is complicated and costly. Doesn’t mean we shouldn’t do it but the financial incentives are still very much in favor of fee for service in most parts of the country. I believe that is why we continue to see very few areas making a meaningful change in the cost of care. I am providing this list of tools required to perform in a value-based care model so there is an understanding about the upfront costs to jumping in.

1) Access to data: A well run Health Information Exchange (HIE) can be incredibly helpful, but doesn’t solve all our interoperability issues. In your area, if the major lab and independent imaging centers are not on your EHR then there can be significant time and money needed for integration efforts. Then there are claims data and social determinants of health data to bring in. Managing patients without a more complete understanding of their conditions and utilization patterns is very challenging.

2) Data aggregation capabilities: If you are successful in getting access to all this data, it now has to be normalized and ideally collated into a meaningful package that a provider can quickly ingest. No duplicate patient records and you can’t put data into the wrong chart. Many health IT shops shy away from this activity because it is hard, can be high risk, and frequently requires human labor to verify the data machine learning is not confident in its assessment. Google showed a demo of the tools they are working on in response to the Ascension article in the Wall Street Journal and their capabilities are worth watching. The ability to get data out of pdf files and other unstructured parts of the medical record will be a huge leap forward but is not commonplace today.

3) Registries: To do effective population health analytics, it is most efficient to have all that data from #1 and #1 above populate registries to track the chronic conditions. Usually available in the EHR but most have to be configured and maintained. To effectively deploy clinical decision support in real time to providers it is necessary to know if the patient in front of you has the disease in question and that data needs to come from your registries. In theory, that data could come from the HIE but more commonly comes from your registries.

4) Data visualization: Now that you have your data nicely organized in registries, the end users need to gain insights. Most systems that are serious about population health have data analysts who are subject matter experts to drive these tools because they are not as intuitive as the vendors make it seem. Then you have to get these insights in front of clinicians to impact the delivery at the point of care. A common mistake is to underestimate the challenges of getting the data in front of the right people for action.

5) HCC coding tools: Health systems that are successful at value based care have figured out the HCC game. How to play this game is beyond the scope of this article, but very smart systems underperform simply because they are not capturing the severity of illness of their patient population. They appear to be spending more money than they should based on what is documented in the charts, but if the documentation more accurately depicted how sick their population truly was, their spending would appear to be in line with expectations. Providers need tools to identify the HCC codes used last year, identify conditions were never coded but are obvious based on their medication history, and NLP tools to find the little gems of wisdom hiding in the text documents of our charts.

6) Pharmacy cost management: Patients and providers will work together to lower the out of pocket spend on medications if they both understand the costs involved. At the time of prescribing, a patient would make an informed decision about using a generic versus a name brand if they could. However most providers cannot tell you the cost of the medication to the patient when they arrive at the pharmacy due to the complexity of pharmacy benefits. Price transparency software that provides guidance towards lowering prescription costs at the point of care (not at the pharmacy) are essential. Real time prescription benefits across a full range of your population is critical to pharmacy cost containment.

7) Utilization Review software: Providers will hate it, but there is a lot of waste in healthcare and utilization review is one tool in the toolbox to control costs. A better option is clinical decision support tools at the time of ordering to steer providers away from unnecessary tests or to alert them that a similar test was done at another facility recently. The software is only one part of the expense and there are usually teams of clinicians needed to support the use of these tools effectively.

8) Risk stratification algorithms: We all have limited resources and need to focus on the right people in our population. Predictive algorithms for readmissions, progression to palliative care models, and risk for progression to severe chronic disease (kidney failure, diabetes, coronary artery disease), requires configuration, testing, and then implementation into the provider workflow (which is the hard part). Some healthcare systems are hiring data scientists to create their own algorithms which will produce a more accurate tool for your particular needs compared to a generic model purchased from a national vendor. The decision to build versus buy must be undertaken thoughtfully because data scientists are in high demand and therefore costly.

9) Provider network management: Providers need to know who are the low cost, high value providers in their market and have the ability to suggest these providers preferentially over high cost, low quality providers. Referral management software and analytics around leakage and referral patterns is necessary to control costs. Figuring out who is a low cost/high value provider can be challenging without broad access to data from multiple hospitals or claims data from Medicare.

10) Analytic tools that evaluate access, supply, and demand: If demand outstrips supply for primary care, access becomes horrible and patients will use the emergency room as their primary care provider. This is expensive and inefficient. Most systems develop and maintain their own dashboards to monitor metrics like 3rd next available appointment, average wait time for a new patient, schedule utilization, and provider productivity.

11) Remote patient monitoring and telehealth: These tools are frequently touted as solutions for readmission reduction and chronic disease management, although the data is somewhat mixed concerning the success of the initiatives. There is nothing wrong with the tools. The challenge is developing effective interventions and using predictive tools to focus on patients that can actually be helped. The tools today are not perfect, but when combined with dedicated nurses it can be effective.

12) Effective communication software: This includes provider to patient, provider to provider, and across the care team communication. The current disjointed processes for moving a patient from ambulatory to inpatient and then to rehabilitation is fraught with error, duplication of work, and poor customer experience. The process for getting a specialist referral isn’t much better. Our current systems have far too high an error rate for making sure the right piece of data reaches the right provider at the right time. Very few systems have mastered this incredibly complex yet important skill. If your main communication tool is a fax machine, you are not adequately prepared.

13) Financial transparency software: Providers can become disengaged or worse if they feel the health system is hiding money. Sharing cost savings is a common incentive in value-based contracts and providers want to know about their performance. Transparency is key and can drive provider behavior, especially when their own cost of care data is displayed against aggregated data from providers in their specialty.

14) Quality of Care gap closure software: Ideally done via an integrated CRM solution for proper coordination of preventative care. These outreach tools must be connected to the clinical systems so clinical staff can focus their outreach efforts on the patients that are actually missing their tests or vaccines. Also, you do not want to receive irate comments on social media about how insensitive your healthcare system is by sending out automated messages concerning screening mammography to patients with breast cancer that underwent bilateral mastectomies. Cost saving preventative care with well-designed software that facilitates targeted campaigns to increase influenza vaccination rates, colon cancer screening rates, and diabetic eye exams can be achieved.

15) Care pathways: A critical piece for reducing the cost of care is to reduce the variability in providing that care. There are best practices for managing of chronic diseases and unnecessary deviation can have cost and quality consequences. These are tools that help reduce the variability in care and will drive waste out of the system. These can be home made, but it is probably far more efficient for them to be purchased and quickly deployed. This also transfers the significant review and maintenance tasks associated with these tools to a vendor.

This list covers the IT infrastructure needed for value based care and it doesn’t include the operational teams needed to execute on the initiatives above. The cost is millions and there is no guarantee for a return on the investments. The potential for “shared savings” delivered to the participating system depends on the health of the underlying population, the ability to deploy and utilize the IT infrastructure above, and a host of operational components. Value based care is the right direction for our country, but the financial incentives in healthcare still supports a fee-for-service model for most systems. You should now have an understanding that the upfront costs are significant and definitely slow the pace of adoption of value-based care in the USA.

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