Department of Medicine at the Perelman School of Medicine, University of Pennsylvania, 303 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
Palliative and Advanced Illness Research (PAIR) Center at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
J Gen Intern Med. 2019 Sep;34(9):1841-1847. doi: 10.1007/s11606-019-05169-2. Epub 2019 Jul 16.
Development of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated.
To assess the clinical impact of triggering palliative care using an EHR prediction model.
Pilot prospective before-after study on the general medical wards at an urban academic medical center.
Adults with a predicted probability of 6-month mortality of ≥ 0.3.
Triggered (with opt-out) palliative care consult on hospital day 2.
Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS).
The control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P < .001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P = .02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P = .03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P = .01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P = .13), code status changes (42 [30] vs 39 [29]; P = .81), or consult requests for lower risk (< 0.3) patients (48/1004 [5] vs 33/798 [4]; P = .48).
Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.
开发电子健康记录 (EHR) 预测模型以改善姑息治疗的提供正在兴起，但尚未评估此类模型的临床影响。
评估使用 EHR 预测模型触发姑息治疗的临床影响。
在城市学术医疗中心的普通医疗病房进行的试点前瞻性前后研究。
预计 6 个月死亡率≥0.3 的成年人。
在入院第 2 天触发（可选择退出）姑息治疗咨询。
咨询次数、预先护理计划 (ACP) 文档记录、家庭姑息治疗和临终关怀转介、代码状态更改以及预咨询住院时间 (LOS)。
对照期和干预期各为 8 周，分别有 138 次和 134 次入院。组间特征相似，6 个月死亡率的平均（标准差）风险为 0.5（0.2）。77（57%）次触发的咨询被初级团队接受，在干预期间，每个常规护理周期请求 8 次咨询。与历史对照相比，咨询增加了 74%（22[16%] vs 85[63%]，P<.001），中位数（四分位距）预咨询 LOS 减少了 1.4 天（2.6[1.1, 6.2] vs 1.2[0.8, 2.7]，P=.02），ACP 文档记录增加了 38%（23[17%] vs 37[28%]，P=.03），家庭姑息治疗转介增加了 61%（9[7%] vs 23[17%]，P=.01）。对照组和干预组在临终关怀转介（14[10] vs 22[16]，P=.13）、代码状态更改（42[30] vs 39[29]；P=.81）或对低风险（<0.3）患者的咨询请求（48/1004[5] vs 33/798[4]；P=.48）方面没有差异。
使用 EHR 死亡率预测模型针对医院为基础的姑息治疗是一种有前途的改善重病患者护理质量的方法。需要更多的证据来确定这种方法的普遍性及其对患者和护理人员报告结果的影响。
