Though health systems are integrating technology at a faster pace than ever before- the electronic medical record (EMR) has been adopted across the board[1]; Covid-19 accelerated the use of telemedicine and remote patient monitoring[2] — the adoption of new artificial intelligence and machine learning (AI/ML) technologies to make care more proactive has been limited.
Yet, embracing AI more fully and bringing clinical decision support tools to the bedside could have immense upside for health systems. Organizations have already invested significant time, effort, and money into establishing the EMR, and every moment more potentially actionable clinical and patient data is added. Despite this, hundreds of thousands of preventable events[3] occur every year. And, physician and nurse burnout is on the rise, as clinicians struggle with data overload.
Especially with new developments in AI/ML technology, health systems that haven’t embraced clinical AI are missing out on tools that can save lives. It’s time to take the next step and add a clinical AI platform to your 2022 strategy, to provide safe, high quality, more sustainable care in a cost-efficient way.
Here are five reasons why health systems need to include clinical AI in their 2022 strategy:
1. Clinical AI improves patient outcomes
There are an estimated 400,000 preventable deaths annually in the United States. By placing accurate clinical signals directly into the hands of physicians and nurses earlier than they would otherwise have recognized them, clinicians can catch life-threatening issues and complications sooner, and health systems will be able to improve patient outcomes. For example, a large five-site prospective study, showed significant improvements in sepsis care (1.85 hour faster antibiotic treatment) when using an AI/ML-powered clinical decision support platform[4].
2. Clinical AI maximizes the EMR to its full extent
The implementation of the EMR has been a multi-billion dollar investment for health systems. But, without a clinical AI layer, the data often isn’t being gathered or analyzed in a way that impacts practice patterns and improves care delivery. A clinical AI platform enhances the EMR investment by using all available data — structured and unstructured patient and clinical information, along with third party data — to deliver actionable, real-time insights to physicians and nurses exactly when they need them.
3. Clinical AI augments physician decision-making to help reduce burden
Clinical AI tools that are integrated within the EMR and existing workflows, complete with context and transparency around why the tool delivered the patient insight, enable physicians and nurses to make faster and more informed care decisions. For example, when applied to pressure injuries, clinical AI can point nurses to the highest risk patients first, saving time during rounds and prioritizing those who need care more urgently.
4. Clinical AI can increase care equity
Implicit biases can impact health care practice resulting in worse outcomes. For example, studies have found that minority patients are much less likely to receive guideline adherent care for sepsis[5]. The right clinical AI can improve health care equity as it can flag insights and critical moments that a clinician might not see, leading to better, more equitable patient outcomes. AI tools must understand and account for potential sources of bias in the tools themselves, proactively looking for and evaluating for bias in models, and monitoring results over time.
5. Clinical AI allows health systems to take on more risk confidently
As more health systems move to value-based care models, it is critical to find ways for systems to strategically take on more risk. Every year, $205 billion is spent on reactive care[6] that is disorganized and ineffective. Data show that one-third of hospital readmissions[7] and one-sixth of ICU admissions[8] are also preventable, dissatisfying patients and costing hospitals thousands daily. A clinical AI platform can enable proactive care across the care continuum, by leveraging all of the patient data in the EMR along with third party data to better identify which interventions will work on which types of patients.
While health systems have made significant technological progress over the past few years, too many are unaware of the benefits they are missing through clinical AI. With the potential to enhance their EMR, improve patient outcomes, lower costs, and reduce burden on physicians, clinical AI should be a necessity in any health system’s 2022 strategy. To learn what should be included in a clinical AI solution, click here to download The Essential AI Tool checklist, developed together with leading clinicians and AI/ML experts.
[1] https://healthinformatics.uic.edu/blog/electronic-health-record-use-at-an-all-time-high/
[2] https://mhealthintelligence.com/features/how-covid-19-affects-the-telehealth-remote-patient-monitoring-landscape
[3] https://journals.lww.com/journalpatientsafety/Fulltext/2013/09000/A_New,_Evidence_based_Estimate_of_Patient_Harms.2.aspx
[4] https://www.bayesianhealth.com/2021/07/14/measuring-the-adoption-and-impact-of-bayesian-healths-ai-platform/
[5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315577/pdf/healthcare-06-00133.pdf
[6] https://jamanetwork.com/journals/jama/article-abstract/2752664?guestAccessKey=bf8f9802-be69-4224-a67f-42bf2c53e027&utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_content=tfl&utm_term=100719
[7] https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2498846
[8] https://www.atsjournals.org/doi/10.1513/AnnalsATS.201905-366OC?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed