A remote hypertension management program clinical algorithm

远程高血压管理程序临床算法

阅读:1

Abstract

INTRODUCTION: Hypertension is the leading risk factor for death, affecting over one billion people worldwide, yet control rates are poor and stagnant. We developed a remote hypertension management program that leverages digitally transmitted home blood pressure (BP) measurements, algorithmic care pathways, and patient-navigator communications to aid patients in achieving guideline-directed BP goals. METHODS: Patients with uncontrolled hypertension are identified through provider referrals and electronic health record screening aided by population health managers within the Mass General Brigham (MGB) health system. Non-licensed patient navigators supervised by pharmacists, nurse practitioners, and physicians engage and educate patients. Patients receive cellular or Bluetooth-enabled BP devices with which they monitor and transmit scheduled home BP readings. Evidence-based medication changes are made according to a custom hypertension algorithm approved within a collaborative drug therapy management (CDTM) agreement with MGB and implemented by pharmacists. Using patient-specific characteristics, we developed different pathways to optimize medication regimens. The renin-angiotensin-aldosterone system-blocker pathway prescribed ARBs/ACE inhibitors first for patients with diabetes, impaired renal function, and microalbuminuria; the standard pathway started patients on calcium channel blockers. Regimens were escalated frequently, adding thiazide-type diuretics, and including beta blockers and mineralocorticoid receptor antagonists if needed. DISCUSSION: We have developed an algorithmic approach for the remote management of hypertension with demonstrated success. A focus on algorithmic decision-making streamlines tasks and responsibilities, easing the potential for scalability of this model. As the backbone of our remote management program, this clinical algorithm can improve BP control and innovate the management of hypertension in large populations.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。