Unnecessary preoperative testing poses a risk to patient safety, causes surgical delays, and increases healthcare costs. We describe the effects of implementing a fully EHR-integrated closed-loop clinical decision support system (CDSS) for placing automatic preprocedural test orders at two teaching hospitals in Madrid, Spain. Interrupted time series analysis was performed to evaluate changes in rates of preoperative testing after CDSS implementation, which took place from September 2019 to December 2019. We included 228,671 surgical procedures during a 69-month period from January 1st, 2018, to October 1st, 2023, of which 78,388 were from the preintervention period and 150,283 from the postimplementation period. We observed a significant reduction (p < 0.001) of 83% (−83.4% to −83.1%) for chest X-ray orders, 54% (−54.7% to −54.2%) for ECG orders, 50% for blood type testing (−50.5% to −50.1%), and 29% (−29.5% to −29.0%) for preoperative blood test order sets, leading to overall cost-savings of €1,013,666. No increase in postoperative adverse events or same-day cancellations was observed. Our results demonstrate that an EHR-embedded closed-loop CDSS can reduce avoidable preoperative testing without increasing surgical cancelations, postoperative adverse events, or early re-interventions, thus improving the quality of care and healthcare expenditure.

Background: The spreading adoption of value-based models of healthcare delivery has incentivized the use of patient-reported outcomes and experience measures (PROMs and PREMs) in clinical practice, with the potential to enrich the decision-making process with patient-reported data.Methods: This perspective article explores PROs and the shared decision-making (SDM) process as components of value-based healthcare. We describe the potential of PROMs and PREMs within the decision-making process and present a digital framework for informing the shared decision-making process using aggregated data from a healthcare system PROMs and PREMs program, including early results from implementation in hospital network in Madrid, Spain.Results: The proposed digital framework incorporates aggregated data from a hospital network PROMs and PREMs program as part of a digital patient decision aid (PDA) for patients with lymphoma. After the first hematologist appointment, participating patients access the PDA to review relevant information about clinical and patient-reported outcomes for each of the possible options, assign a personal order of priority to different outcomes, and then select their preferred course of action. Patients’ answers are automatically uploaded to the EHR and discussed with hematologists at the next appointment. After beginning treatment, patients are invited to participate in the network PROMs program; participants’ PROMs data are fed back into the PDA, thus “closing the circle” between the decision-making process and patient-reported data collection.During the first 14 months after launching the decision aid in October 2022, of 25 patients diagnosed with follicular lymphoma at the four participating hospitals, 13 patients decided to participate. No significant differences in age or sex were observed between groups. Average SDM Q-9 score for patients filling in the questionnaire (n = 6) was 36.15 of 45 points.Conclusion: Various obstacles toward widespread implementation of SDM exist such as time constraints, lack of motivation, and resistance to change. Support and active engagement from policy makers and healthcare managers is key to overcome hurdles for capturing patient-reported data and carrying out shared decision-making at healthcare system level. Early results of a digital framework for PRO-enriched SDM seem to be beneficial to the decision-making process.

Objective Reducing backlogs for elective care is a priority for healthcare systems. We conducted an interrupted time series analysis demonstrating the effect of an algorithm for placing automatic test order sets prior to first specialist appointment on avoidable follow-up appointments and attendance rates. Design Interrupted time series analysis. Setting 4 academic hospitals from Madrid, Spain. Participants Patients referred from primary care attending 10 033 470 outpatient appointments from 16 clinical specialties during a 6-year period (1 January 2018 to 30 June 2023). Intervention An algorithm using natural language processing was launched in May 2021. Test order sets developed for 257 presenting complaints from 16 clinical specialties were placed automatically before first specialist appointments to increase rates of diagnosis and initiation of treatment with discharge back to primary care. Primary and secondary outcome measures Primary outcomes included rate of diagnosis and discharge to primary care and follow-up to first appointment index. The secondary outcome was trend in ‘did not attend’ rates. Results Since May 2021, a total of 1 175 814 automatic test orders have been placed. Significant changes in trend of diagnosis and discharge to primary care at first appointment (p=0.005, 95% CI 0.5 to 2.9) and ‘did not attend’ rates (p=0.006, 95% CI −0.1 to −0.8) and an estimated attributable reduction of 11 306 avoidable follow-up appointments per month were observed. Conclusion An algorithm for placing automatic standardised test order sets can reduce low-value follow-up appointments by allowing specialists to confirm diagnoses and initiate treatment at first appointment, also leading to early discharge to primary care and a reduction in ‘did not attend’ rates. This initiative points to an improved process for outpatient diagnosis and treatment, delivering healthcare more effectively and efficiently.

Understanding patient and caregiver experience is key to providing person-centered care. The palliative care approach includes holistic assessment and whole-person care at the end of life, that also involves the patient's family and loved ones. The aim of this study was to describe the way that family caregivers experienced patients' deaths during their loved ones' last hospital admission, comparing inpatient palliative care (PCU) and non-palliative care (Non-PCU) units. A qualitative case study approach was implemented. Family caregivers of terminally ill patients admitted to the Infanta Elena Hospital (Madrid, Spain) between 2016 and 2018 were included using purposeful sampling. Eligible caregivers were first-degree relatives or spouses present during the patient's last hospital admission. Data were collected via in-depth interviews and researchers' field notes. Semi-structured interviews with a question guide were used. A thematic inductive analysis was performed. The group of caregivers of patients admitted to the PCU unit and the group of caregivers of patients admitted to Non-PCU were analyzed separately, through a matrix. In total 24 caregivers (12 from the PCU and 12 from Non-PCU units) were included. Two main themes were identified: caregivers' perception of scientific and technical appropriateness of care, and perception of person-centred care. Scientific appropriateness of care was subdivided into two categories: diagnostic tests and treatment, and symptom control. Perception of person-centred care was subdivided as: communication, emotional support, and facilitating the farewell process. Caregivers of patients admitted to a PCU unit described their experience of end-of-life care as positive, while their Non-PCU unit counterparts described largely negative experiences. PCU provides a person-centered approach to care at the end of life, optimizing treatment for patients with advanced disease, ensuring effective communication, establishing a satisfactory professional relationship with both patients and their loved ones, and facilitating the farewell process for family caregivers.