Get Ready for Data-Driven Medication Management

Episode Highlights

Ken Perez:For today's discussion let's make some big assumptions. Let's say there's consensus on industry data standards, strong partnership between healthcare providers and IT vendors, and their support for the needed integration of systems and increased interoperability.

What can that lead to, in terms of greater data intelligence utilization?

珍妮弗·泰恩（Jennifer Tryon）:That would be a game changer. It would open the door to the information that we need as healthcare leaders, but that we can't access today. Data intelligence is a critical component to making the improvements that we envision organizations achieving as they move towards the Autonomous Pharmacy.

数据智能的许多问题可以通过应用高级分析，而不是在一个护理位置，而是在整个护理连续性中解决。我认为这里的目标是从与药物相关的数据中推动可行的见解，以使卫生系统能够积极地应对挑战，更有效地应对挑战。

What are the different levels of data intelligence capability?

艾伦·弗林：At the foundational level is descriptive analytics used for reporting. Examples are drug purchase reports, drugs dispensed, medication administration, and movements of drug products through the enterprise. These reports help us to better manage the pharmacy enterprise overall. But they're just not sophisticated enough for the decisions that we're facing.

The next level up is predictive analytics. We're on the cusp of a big, strong increase in the use of predictive analytics, what I sometimes refer to as prediction in practice. We already use some predictive models in the drug supply chain to achieve better forecasts for purchasing and distribution. In the clinical world, predictive models are used to predict the potential for adverse drug events. Some solutions can predict with reasonably high accuracy whether a new prescription fits with the profile of the patient for whom that prescription has been written. This kind of predictive analytics could be a major advance in medication safety.

在这个最高水平上，一旦我们非常擅长预测事情，我们实际上可能会添加特定的动作以更高度地自动化药房中的某些任务。我们可能会从自动驾驶汽车方面想到这一点。我们依靠实时分析来转动汽车，加速汽车，以阻止汽车。所有这些事情会自动发生。药房中有一些有些工作流程的实例，一旦我们在预测分析方面变得更好并进入规定性分析，我们实际上将拥有能够自动为我们做更多的系统。

What are some pharmacy examples of descriptive, predictive, and prescriptive analytics?

艾伦·弗林：One example comes from drug product reordering.

Using descriptive analytics, it’s what most health system pharmacies do today. We have historical costs and par values to guide reordering. But we know it's suboptimal and difficult to manage. This approach can lead to stock outs and other problems, sometimes creating more work.

Predictive analytics will allow us to improve when we can aggregate lots of data from multiple sites, and ultimately get much better at predicting what's going to happen with the inventory at any given site over time. Those kinds of new tools that rely on much larger datasets are coming to the fore, they are very much wanted.

With prescriptive analytics, once we get very good at understanding and predicting the changes that are going to take place in an enterprise-level inventory view of the world, then we'll actually be able to automate reordering. That’s connecting the action to the prediction, and that's what prescriptive analytics is all about.

珍妮弗·泰恩（Jennifer Tryon）:For me, the ultimate scenario for prescriptive analytics is when you know your systems are working so well you don’t even have to think about them. So you can be forward thinking with patients or a major initiative instead of worrying about technology issues or finding the data.

一些药物供应链样品以及我们如何从更高的自动化工作流中受益：

• 在远程诊所等偏远地区增加药物库存控制。通常，这些诊所不会得到太多的监督。这可能会导致库存做法不佳，例如超越，紧急购买和潜在的浪费。规定性分析可以在那些特定的门诊诊所中不断支持适当的药物清单，以治疗特定于诊所的患者人群所需的数量。

• Reducing nationwide drug shortages. This could happen through early detection of the issues and prioritization of where the limited drug supply should be distributed. And then ensuring appropriate alternatives are available in each of the health systems, in each of the sites, to prevent discontinuity and interruptions in patient care.

• 潜在地稳定卫生系统的药物成本。通过具有一致，可靠的批量预测，药房领导者可以更好地定位与药品分销商进行谈判，而不是使用基于批量购买或企业（Bulk Buys或Overbuys）的不优化购买实践的销量预测。

您如何设想将规定性分析应用于临床药房？

艾伦·弗林：I'm very excited about the potential for applying medication dosing analytics to help make decisions about what dose to administer to a patient. And with enough information to do precise dosing, we'll be able to do this for more people, more of the time. And ultimately, I think that will improve care and prove the value that people get from taking medications.

Today our descriptive analytics systems provide very basic decision-support alerts. Typically they're triggered by some upper and lower numeric boundary for a dose that's thought to be too high or too low. But we know this approach creates a lot of noise, a lot of alert fatigue. And it frustrates caregivers who are putting in many prescriptions for many patients. So it's just not sophisticated enough and we need to do better.

Moving to predictive analytics computer-based models can help to predict what a medication dose should be for a given individual based on the science of the drug; factors that are specific to that individual, things like their kidney function; and sometimes genetic factors as well.

这些因素会随着时间而变化，因此需要使用这些算法来计算这些剂量。预测分析还将为每个剂量预测提供强度等级，并表达了对计算机系统根据对他人服用的历史记录提出适当剂量的信心。

所有这些都可以完成。我们知道这是可能的。但是，自治药房的想法是在更广泛的规模上做188金宝慱官网送188到这一点。好处可能是，临床医生可以使用这些工具在常规上预测最佳剂量，而收到类似信息的药剂师可以评估处方作为其工作的一部分。

Looking out further into the future, the computer can take on the dosing task after an indication and other features about patient have been settled. Once those values are in, I imagine we will see some automated dosing routines in the future – after a drug therapy has been selected, after appropriate information has been provided to the system. We're not there yet, and it will take a while to get there, but it's certainly possible to do that. When we do, some dosing could be fully automated.

Piggybacking on Allen's comments, how could predictive dosing change the role of pharmacists in your health system?

珍妮弗·泰恩（Jennifer Tryon）: I think predictive analytics is still too far off into the future to say for sure how it's going to impact health system pharmacy. But it certainly could be transformative as we move closer to those models and make some significant advances. Some of the signs of this technology are already starting to impact pharmacy supply chain. For example, with drug distributors experimenting with technologies to remove the variability from the supply chain, or to improve efficiencies, or to reduce waste, to eliminate drug shortages, and hopefully to reduce overall drug costs.

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The Future of Pharmacy Podcast is produced and distributed by Pharmacy Podcast Network. The views and opinions expressed in this podcast are those of the authors and do not necessarily reflect the official policy or position of any other agency, organization, employer or company. Assumptions made in the analysis are not reflective of the position of any entity other than the author(s). These views are always subject to change, revision, and rethinking at any time and may not be held in perpetuity.