{"id":32206,"date":"2018-11-13T14:39:59","date_gmt":"2018-11-13T19:39:59","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/newyork-presbyterian-becoming-the-hospital-of-the-future\/"},"modified":"2018-11-13T14:39:59","modified_gmt":"2018-11-13T19:39:59","slug":"newyork-presbyterian-becoming-the-hospital-of-the-future","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/newyork-presbyterian-becoming-the-hospital-of-the-future\/","title":{"rendered":"NewYork-Presbyterian: Becoming the Hospital of the Future"},"content":{"rendered":"

How is NewYork-Presbyterian using Machine Learning?<\/strong><\/h2>\n

Physicians are required to handle large amounts of data, from macro-level physiology to micro-level lab and imaging studies. The growing amount of patient data and the increasing requirements for doctors to document notes on electronic medical records has made it very difficult for doctors to manage all this information while still providing optimal care for their patients [2]. This has resulted in doctors spending more time on computers than in face-to-face interactions with their patients [3]. At NYP, machine learning is being used to monitor patients and to alert doctors and nurses when data is clinically-relevant, which is allowing doctors to spend more time with patients.<\/span><\/p>\n

\"\"<\/a>
Figure 1. On average, doctors spend twice the time on electronic medical records, not treating the patient [1].<\/figcaption><\/figure>NYP is a large hospital system that takes care of roughly 810,000 patient stay-days per year. When you multiply that times the amount of decisions that need to be made on any given day, that can amount to billions of decisions. According to Chief Transformation Officer at NYP, Dr. Peter Fleischut, the value of machine learning is not in making diagnoses, but rather in streamlining clinicians\u2019 workflows.<\/span><\/p>\n

To do this, NYP created the Clinical Operations Center, or CLOC, which is an off-site support building where nurses use machine learning to monitor patients at the hospital. It works by having automated patient monitors which send data in real-time to CLOC. Nurses at CLOC monitor the data and machine learning is used to separate non-priority data from priority data. When an intervention is required, a notification is sent to the hospital staff. NYP has reported multiple benefits of this remote monitoring system, including: 1) doctors can spend more time with their patients, 2) alarms dropped from 30,000 per week to only 100, 3) it reduced the number of redundant tasks, and 4) it reduced the number of staff needed on-site [4].<\/span><\/p>\n

\"\"<\/a>
Figure 2. NYP’s command center CLOC monitoring station [4].<\/figcaption><\/figure>\n

What issues are they trying to solve?<\/strong><\/h2>\n

In the short-term, NYP\u2019s goal is to \u201cleverage machine learning at 100% capacity\u201d to continue optimizing clinicians’ workflows. CLOC is already showing the benefits of machine learning in the daily clinical monitoring of patients. However, this monitoring system has been implemented in only 30 medical units at NYP. The hospital leadership has a vision for expanding this system across more hospital units [5]. <\/span><\/p>\n

In the medium-term, NYP is exploring new uses for machine learning beyond remote patient monitoring. Their focus is primarily in the operations space, which is unlike many health care companies that are using machine learning for diagnostics and decision-making tools. To do this, NYP established the \u201cNYP Innovation Center\u201d and hired a team that works on machine learning projects. The overarching goal is to use machine learning to extract insight from hospital data, and work with senior leadership to deliver live, automated, and actionable information to clinicians [6]. <\/span><\/p>\n

As the use of machine-learning grows, NYP will certainly face issues with the technology. First, there is a potential for bias. There is a risk that machine-learning will reflect doctor\u2019s biases since they are inherent to the data we provide them. Second, there is a fiduciary patient-doctor relationship. The introduction of machines will call into question whether machines are also part of that relationship and whether data can truly be kept confidential. Finally, we question the overarching goal of machine-learning: is it to improve health care or to generate more profit? Or both? [7].<\/span><\/p>\n

Recommendations for Growth<\/h2>\n

At a high-level, NYP uses machine learning to address operational inefficiencies within the hospital. Their first application is in patient monitoring and alarm systems, but there are certainly other areas where this technology can create efficiency in patient throughput and clinician workflow:<\/span><\/p>\n

    \n
  1. Surgical scheduling systems<\/strong><\/span><\/span>: By using machine learning, NYP can create optimal operating schedules such that operating rooms spend less time being idle during patient turnarounds. <\/span><\/li>\n
  2. Patient documentation<\/strong><\/span>:\u00a0Currently, clinicians spend a significant amount of time interviewing patients and documenting notes on electronic medical records. Machine learning can be used to not only document notes, but also to recommend next steps in the treatment plan. <\/span><\/li>\n
  3. Expand into diagnostics<\/strong><\/span>:\u00a0This will be important as they compete to remain a leader in digital health and innovation. The industry is trending towards big data and improved diagnostics, especially in radiology and pathology.\u00a0<\/span><\/li>\n<\/ol>\n

    Unanswered Questions<\/h2>\n

    As machine learning continues to expand, the questions that remain are: 1) Can we maintain the patient-doctor relationship while still upholding confidentiality, 2) Can we effectively remove bias from the decision-making process, and 3) What is the fundamental goal of machine learning in medicine: to maximize profits or to shift clinicians\u2019 time back to the patient?<\/span><\/p>\n

    (Word Count: 780)<\/p>\n

     <\/p>\n

    Sources<\/strong><\/h3>\n

    [1] https:\/\/lowninstitute.org\/news\/blog\/ehrs-desperately-need-reboot-new-studies-show\/<\/span><\/p>\n

    [2] https:\/\/www.nejm.org\/doi\/full\/10.1056\/NEJMp1606181?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed<\/span><\/p>\n

    [3] http:\/\/annals.org\/aim\/fullarticle\/2546704\/allocation-physician-time-ambulatory-practice-time-motion-study-4-specialties<\/span><\/p>\n

    [4] https:\/\/www.healthcareitnews.com\/news\/inside-look-newyork-presbyterians-ai-command-center<\/span><\/p>\n

    [5] https:\/\/www.nyp.org\/news\/NYP-Named-Top-10-Most-Innovative-Company-in-AI-by-Fast-Company<\/span><\/p>\n

    [6]\u00a0http:\/\/www.innovatenyp.org\/silvermanfellowship\/<\/span><\/p>\n

    [7] https:\/\/www.nejm.org\/doi\/full\/10.1056\/NEJMp1714229?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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