1 00:00:00,640 --> 00:00:03,199 Philips is a health tech leader focused on 2 00:00:03,199 --> 00:00:06,000 innovation that improves the health and well-being of 3 00:00:06,000 --> 00:00:09,460 people. Our health care technology and informatics solutions 4 00:00:09,599 --> 00:00:12,559 help care teams diagnose, treat, and manage more 5 00:00:12,559 --> 00:00:16,244 patients with greater precision, speed, and confidence across 6 00:00:16,244 --> 00:00:19,225 the care journey. With Philips, clinicians are empowered 7 00:00:19,285 --> 00:00:21,765 with streamlined insights in the moments that matter 8 00:00:21,765 --> 00:00:24,904 for every patient. Better care for more people. 9 00:00:25,204 --> 00:00:25,704 Philips. 10 00:00:29,089 --> 00:00:30,769 Hello, and welcome to the Becker's Health Care 11 00:00:30,769 --> 00:00:33,329 podcast recorded live with the 9th annual health 12 00:00:33,329 --> 00:00:36,210 IT digital health and RCM conference. I'm joined 13 00:00:36,210 --> 00:00:38,850 today by Divya Pathar, chief data and AI 14 00:00:38,850 --> 00:00:41,109 officer at NYC Health and Hospitals. 15 00:00:41,695 --> 00:00:43,295 Divya, to get us started, can you please 16 00:00:43,295 --> 00:00:44,575 tell us a little bit about yourself, your 17 00:00:44,575 --> 00:00:45,875 background, and your role at your organization? 18 00:00:46,335 --> 00:00:48,335 Absolutely. And, first of all, thank you for 19 00:00:48,335 --> 00:00:51,054 this opportunity. My name is Divya Pathak. I'm 20 00:00:51,054 --> 00:00:52,814 the chief data and AI officer at New 21 00:00:52,814 --> 00:00:55,134 York City Health and Hospitals, the largest public 22 00:00:55,134 --> 00:00:57,155 health care delivery system in the country. 23 00:00:57,670 --> 00:00:59,769 I have over 18 years experience 24 00:01:00,070 --> 00:01:01,929 in data, AI, and analytics 25 00:01:02,229 --> 00:01:04,229 in health care, and I've worked with with 26 00:01:04,229 --> 00:01:05,769 variety of diverse stakeholders 27 00:01:06,629 --> 00:01:09,369 across health systems, academic medical centers, 28 00:01:09,909 --> 00:01:11,769 payers, digital health, and pharmaceutical 29 00:01:12,069 --> 00:01:12,569 companies. 30 00:01:13,875 --> 00:01:16,354 Perfect. Well, AI adoption is exploding in health 31 00:01:16,354 --> 00:01:18,674 care right now. In your view, what's the 32 00:01:18,674 --> 00:01:21,414 most significant or promising application of this technology 33 00:01:21,474 --> 00:01:23,394 right now, and how is this informing your 34 00:01:23,394 --> 00:01:25,015 organization's innovation strategy? 35 00:01:26,600 --> 00:01:28,680 I agree. AI adoption in health care is 36 00:01:28,680 --> 00:01:31,020 rapidly advancing, and we are seeing a significant 37 00:01:31,079 --> 00:01:33,180 impact on both the clinical and the nonclinical 38 00:01:33,319 --> 00:01:35,180 areas even within health and hospitals. 39 00:01:35,640 --> 00:01:38,680 On the clinical side, AI powered decision support 40 00:01:38,680 --> 00:01:41,775 systems and personalized care models are enhancing the 41 00:01:42,094 --> 00:01:44,755 diagnostic, the treatment plans, and patient outcomes 42 00:01:45,454 --> 00:01:45,954 by 43 00:01:46,334 --> 00:01:48,974 integrating complex data sources like the medical records, 44 00:01:48,974 --> 00:01:51,775 the imaging, and the genomic data that provides 45 00:01:51,775 --> 00:01:54,734 us a vast opportunity to actually understand the 46 00:01:54,734 --> 00:01:55,635 patient population 47 00:01:56,109 --> 00:01:57,969 and also serve the patients better. 48 00:01:58,750 --> 00:02:01,069 Ambient listening technology is another one that we 49 00:02:01,069 --> 00:02:03,890 hear quite a bit, recently that's actually 50 00:02:04,269 --> 00:02:05,090 has multiple 51 00:02:05,390 --> 00:02:08,370 efficiencies in terms of reducing the documentation burden, 52 00:02:08,750 --> 00:02:10,289 improving the patient experience, 53 00:02:11,264 --> 00:02:13,204 leading to better scheduling optimization 54 00:02:13,664 --> 00:02:14,724 for the patients, 55 00:02:15,025 --> 00:02:18,064 and most importantly, reducing the clinical burnout. And 56 00:02:18,064 --> 00:02:19,745 we're seeing a lot more focus on that 57 00:02:19,745 --> 00:02:20,724 in our systems. 58 00:02:21,185 --> 00:02:22,004 On the nonclinical 59 00:02:22,305 --> 00:02:25,344 side, AI is driving major efficiencies and revenue 60 00:02:25,344 --> 00:02:26,245 cycle management 61 00:02:26,590 --> 00:02:27,329 by streamlining 62 00:02:27,629 --> 00:02:28,129 streamlining 63 00:02:28,430 --> 00:02:30,609 billing claims and optimizing revenue. 64 00:02:31,549 --> 00:02:34,750 Additionally, we've started using AI to support workforce 65 00:02:34,750 --> 00:02:37,870 management, provide predictive maintenance, and improving the overall 66 00:02:37,870 --> 00:02:40,844 operational efficiencies on the non clinical areas. To 67 00:02:40,844 --> 00:02:42,925 me, I think these advancements are not just 68 00:02:42,925 --> 00:02:45,405 shaping the industry, but also shaping New York 69 00:02:45,405 --> 00:02:47,504 City Health and Hospital's innovation strategy 70 00:02:47,805 --> 00:02:49,424 as we're trying to focus on 71 00:02:50,125 --> 00:02:52,944 care delivery, improved care delivery, and operational efficiencies 72 00:02:53,164 --> 00:02:55,104 and effectiveness across the whole system. 73 00:02:55,550 --> 00:02:57,550 So on a daily basis, health care leaders 74 00:02:57,550 --> 00:02:59,710 are managing greater volumes of data and more 75 00:02:59,710 --> 00:03:01,790 devices across a growing number of care settings 76 00:03:01,790 --> 00:03:02,449 and populations. 77 00:03:02,990 --> 00:03:05,550 In this complex environment, what clinical data integration 78 00:03:05,550 --> 00:03:07,790 tools or practices are you seeing drive improvements 79 00:03:07,790 --> 00:03:09,790 in patient outcomes and operations? And do you 80 00:03:09,790 --> 00:03:11,444 have an example or 2 you can share? 81 00:03:11,925 --> 00:03:13,925 So if you look at today's health care 82 00:03:13,925 --> 00:03:14,425 data, 83 00:03:15,125 --> 00:03:18,004 according to a recent survey, it constitutes to 84 00:03:18,004 --> 00:03:18,985 almost 32% 85 00:03:19,605 --> 00:03:21,465 of world's data volumes. 86 00:03:21,925 --> 00:03:23,765 So that's huge. So we're living in an 87 00:03:23,765 --> 00:03:24,905 explosion era. 88 00:03:25,300 --> 00:03:25,620 And, 89 00:03:26,180 --> 00:03:28,280 thanks to all the variables and other additional 90 00:03:28,340 --> 00:03:30,740 devices that's Bluetooth enabled, that increases just the 91 00:03:30,740 --> 00:03:32,199 volume on a daily basis. 92 00:03:32,580 --> 00:03:34,580 So it's important for us to understand the 93 00:03:34,580 --> 00:03:36,819 digital presence of this data that's focused on 94 00:03:36,819 --> 00:03:39,459 a patient's well-being and wellness. Mhmm. And one 95 00:03:39,459 --> 00:03:41,205 of the most impact ful tools that we 96 00:03:41,205 --> 00:03:42,025 have embraced within health and hospitals is 97 00:03:42,405 --> 00:03:44,248 the cloud based data platform, Snowflake Mhmm. Which 98 00:03:44,248 --> 00:03:45,809 is allowing us to have seamless integration of 99 00:03:45,809 --> 00:03:47,444 this diverse data sources, including the EHR, variables, 100 00:03:47,444 --> 00:03:50,485 and imaging data. And we're also adding genomics 101 00:03:50,485 --> 00:03:53,305 to it. So these platforms not only enable 102 00:03:54,830 --> 00:03:56,930 and we're also adding genomics to it. So 103 00:03:57,069 --> 00:03:59,710 these platforms not only enable real time analytics, 104 00:03:59,710 --> 00:04:01,169 but also enable more 105 00:04:01,629 --> 00:04:04,509 personalized care delivery Mhmm. That's required to our 106 00:04:04,509 --> 00:04:05,009 population. 107 00:04:05,389 --> 00:04:07,889 Another example I would give from our organization 108 00:04:08,110 --> 00:04:09,409 using this data platform 109 00:04:09,985 --> 00:04:12,944 is how we integrate the patient, the, data 110 00:04:12,944 --> 00:04:15,844 across various settings to enhance chronic disease management. 111 00:04:16,704 --> 00:04:19,185 By leveraging data and predictive analytics, we've been 112 00:04:19,185 --> 00:04:21,685 able to identify high risk patients earlier, 113 00:04:22,319 --> 00:04:23,539 enable timely interventions, 114 00:04:23,919 --> 00:04:26,879 and improve the long term outcomes Mhmm. Leading 115 00:04:26,879 --> 00:04:29,919 to reduced readmissions and improved outcomes for those 116 00:04:29,919 --> 00:04:32,399 patients. So this approach not only enhances patient 117 00:04:32,399 --> 00:04:34,479 care at health and hospitals, but it's also 118 00:04:34,479 --> 00:04:36,959 an important one to also optimize the resource 119 00:04:36,959 --> 00:04:39,694 allocation across the system. Perfect. Well, how can 120 00:04:39,694 --> 00:04:42,415 health care organizations better support IT and clinical 121 00:04:42,415 --> 00:04:44,254 teams as they carry out innovation efforts? And, 122 00:04:44,415 --> 00:04:46,175 what are some of the common pitfalls here? 123 00:04:46,175 --> 00:04:47,615 I think we are living in an evolving 124 00:04:47,615 --> 00:04:50,115 era where we're constantly learning how to adapt 125 00:04:50,399 --> 00:04:51,300 to the changing, 126 00:04:51,600 --> 00:04:53,459 situation and both in terms of the technology 127 00:04:53,519 --> 00:04:55,439 and the workforce. So I think it is 128 00:04:55,439 --> 00:04:56,979 important for healthier organizations 129 00:04:57,759 --> 00:05:00,399 knowing the uncertainty we are living in to 130 00:05:00,399 --> 00:05:01,699 foster stronger collaboration, 131 00:05:02,160 --> 00:05:03,459 provide the right resources 132 00:05:03,854 --> 00:05:05,935 and support of the talent needed, and more 133 00:05:05,935 --> 00:05:08,595 importantly, encourage a culture of innovation and continuous 134 00:05:08,654 --> 00:05:09,154 learning. 135 00:05:09,535 --> 00:05:12,495 Clear communication between IT and clinical teams is 136 00:05:12,495 --> 00:05:16,095 essential to ensure that the technology solutions align 137 00:05:16,095 --> 00:05:18,595 with the work streams and the clinical needs, 138 00:05:19,189 --> 00:05:20,889 and providing adequate training, 139 00:05:21,349 --> 00:05:24,389 and change management support is also critical. So 140 00:05:24,389 --> 00:05:25,610 there's a lot of cultural 141 00:05:25,990 --> 00:05:28,709 aspect to also embracing AI Right. Right. In 142 00:05:28,709 --> 00:05:29,209 ecosystems. 143 00:05:30,389 --> 00:05:32,964 So with your experience, what's your top piece 144 00:05:32,964 --> 00:05:35,285 of advice for other health care leaders as 145 00:05:35,285 --> 00:05:37,764 they prepare for further advancements in technology and 146 00:05:37,764 --> 00:05:39,144 greater demands for care? 147 00:05:39,764 --> 00:05:41,764 My top piece of advice would be embrace 148 00:05:41,764 --> 00:05:42,425 the randomness, 149 00:05:42,805 --> 00:05:44,514 prioritize agility and adaptability in both in the 150 00:05:45,990 --> 00:05:47,610 technology and the workforce development. 151 00:05:48,389 --> 00:05:50,310 And why is this important? Because as we 152 00:05:50,310 --> 00:05:52,870 look at advancements in AI, data integration and 153 00:05:52,870 --> 00:05:55,849 also telehealth, it's rapidly shaping the care delivery 154 00:05:55,909 --> 00:05:57,529 models. We're moving from 155 00:05:57,925 --> 00:06:00,004 brick and mortar style, and thanks to COVID, 156 00:06:00,004 --> 00:06:03,125 we reimagine healthcare. We've moved to more virtual 157 00:06:03,125 --> 00:06:06,105 delivery models. So it's important to enable that 158 00:06:06,245 --> 00:06:09,225 culture of continuous learning and cross functional collaborations. 159 00:06:10,259 --> 00:06:12,819 And, it's also important to be mindful of 160 00:06:12,819 --> 00:06:15,379 the growing complexity in the care settings and 161 00:06:15,379 --> 00:06:16,279 the data management. 162 00:06:17,060 --> 00:06:19,300 Additionally, I would say that investing in robust 163 00:06:19,300 --> 00:06:22,599 data governance, which is foundational to everything AI 164 00:06:23,154 --> 00:06:25,555 and security measures is very important for health 165 00:06:25,555 --> 00:06:26,294 care organizations. 166 00:06:26,915 --> 00:06:28,294 So balancing the technology 167 00:06:28,915 --> 00:06:32,935 advancements with strong governance and clinical clinician engagement, 168 00:06:33,314 --> 00:06:35,495 I think, is is a good sustainable 169 00:06:35,875 --> 00:06:38,970 growth path for bringing and adopting health care. 170 00:06:38,970 --> 00:06:41,129 Yeah. Well, Divya, thank you so much for 171 00:06:41,129 --> 00:06:42,730 joining us today on the Becker's Health Care 172 00:06:42,730 --> 00:06:44,730 podcast. I really appreciate the conversation. You have 173 00:06:44,730 --> 00:06:46,490 an absolutely lovely rest of your day. Thank 174 00:06:46,490 --> 00:06:47,709 you so much for this opportunity. 175 00:06:48,019 --> 00:06:48,519 Pleasure.