1 00:00:00,719 --> 00:00:04,000 Exciting things are happening at Becker's Healthcare. Stay 2 00:00:04,000 --> 00:00:06,240 ahead of industry trends with the new Becker's 3 00:00:06,240 --> 00:00:08,419 CFO plus Revenue Cycle podcast, 4 00:00:08,800 --> 00:00:11,119 your go to source for insights from top 5 00:00:11,119 --> 00:00:14,154 healthcare finance leaders. Tune in wherever you get 6 00:00:14,154 --> 00:00:15,054 your podcasts. 7 00:00:15,914 --> 00:00:18,475 And don't miss the tenth annual health IT 8 00:00:18,475 --> 00:00:22,154 plus digital health plus RCM conference, happening September 9 00:00:22,154 --> 00:00:24,474 30 to 10/03/2025 10 00:00:24,474 --> 00:00:25,214 in Chicago. 11 00:00:25,755 --> 00:00:26,879 Join thousands of 12 00:00:27,279 --> 00:00:30,079 executives, engage with industry leaders, and explore the 13 00:00:30,079 --> 00:00:31,619 future of health care innovation. 14 00:00:32,320 --> 00:00:36,719 Learn more about our upcoming events at beckershospitalreview.com. 15 00:00:36,719 --> 00:00:37,539 See you there. 16 00:00:38,000 --> 00:00:40,000 This is Laura Dierda with the Becker's health 17 00:00:40,000 --> 00:00:40,659 care podcast. 18 00:00:40,984 --> 00:00:42,824 I'm thrilled today to be joined by Divya 19 00:00:42,824 --> 00:00:43,324 Patek, 20 00:00:43,784 --> 00:00:46,104 chief data and AI officer at New York 21 00:00:46,104 --> 00:00:48,585 City Health and Hospitals. Divya, it's a pleasure 22 00:00:48,585 --> 00:00:50,265 to have you on the podcast today. So 23 00:00:50,265 --> 00:00:52,585 I'm excited for our conversation because I I 24 00:00:52,585 --> 00:00:54,265 know there's so many cool things that you're 25 00:00:54,265 --> 00:00:56,700 doing at New York City Health and Hospitals 26 00:00:56,759 --> 00:00:59,320 and certainly a a very important community that 27 00:00:59,320 --> 00:01:01,719 you're serving there. So, I'm excited to dive 28 00:01:01,719 --> 00:01:03,479 in. But before we do, I'm wondering if 29 00:01:03,479 --> 00:01:04,840 you could tell me a little bit more 30 00:01:04,840 --> 00:01:05,340 about 31 00:01:05,640 --> 00:01:07,319 New York City Health and Hospitals and what 32 00:01:07,319 --> 00:01:08,540 makes the system unique. 33 00:01:09,255 --> 00:01:11,094 Thank you, Laura, for inviting me to this 34 00:01:11,094 --> 00:01:11,594 podcast. 35 00:01:12,135 --> 00:01:14,055 My name is Divya Pathak. As you mentioned, 36 00:01:14,055 --> 00:01:15,895 I'm the chief data and AI officer for 37 00:01:15,895 --> 00:01:17,674 the New York City health and hospitals, 38 00:01:18,215 --> 00:01:20,875 the largest public health system in our country. 39 00:01:21,640 --> 00:01:24,920 And I provide executive leadership and strategic direction 40 00:01:24,920 --> 00:01:26,460 to the data and AI organization, 41 00:01:27,319 --> 00:01:29,900 to support our network of hospitals 42 00:01:30,520 --> 00:01:31,500 and its partners 43 00:01:31,880 --> 00:01:35,000 with data, digital tools, and AI capabilities and 44 00:01:35,000 --> 00:01:35,500 insights 45 00:01:36,005 --> 00:01:38,185 that drives quality and performance improvements, 46 00:01:38,965 --> 00:01:40,105 enterprise operations, 47 00:01:40,564 --> 00:01:42,344 and enhances patient outcomes. 48 00:01:43,045 --> 00:01:45,784 And to speak about our health system, 49 00:01:46,405 --> 00:01:49,045 our network includes 11 acute care hospitals and 50 00:01:49,045 --> 00:01:52,189 a network of over 30 community based clinics, 51 00:01:52,969 --> 00:01:54,269 long term care facilities, 52 00:01:54,569 --> 00:01:55,790 and trauma centers 53 00:01:56,489 --> 00:01:58,810 in the five boroughs in New York City, 54 00:01:58,810 --> 00:02:00,969 allowing us to offer a full spectrum of 55 00:02:00,969 --> 00:02:01,870 health care services. 56 00:02:02,954 --> 00:02:05,534 Beyond just medical care, we focus on improving 57 00:02:05,674 --> 00:02:07,614 the overall well-being of our patients 58 00:02:07,994 --> 00:02:10,875 by addressing the social determinants of health, such 59 00:02:10,875 --> 00:02:13,435 as access to housing, food, and other critical 60 00:02:13,435 --> 00:02:13,935 resources. 61 00:02:14,715 --> 00:02:17,114 What makes us unique is our unwavering commitment 62 00:02:17,114 --> 00:02:18,334 to providing comprehensive 63 00:02:18,689 --> 00:02:19,830 care to all individuals 64 00:02:20,849 --> 00:02:21,669 irrespective of their 65 00:02:22,290 --> 00:02:23,189 socioeconomic background, 66 00:02:23,490 --> 00:02:26,469 racial, ethnic status, and legal status. 67 00:02:27,169 --> 00:02:29,669 And as a safety net provider, we see 68 00:02:30,129 --> 00:02:31,430 a diverse and often 69 00:02:31,844 --> 00:02:35,205 underserved population ensuring that essential health care is 70 00:02:35,205 --> 00:02:38,405 available to everyone in New York City, including 71 00:02:38,405 --> 00:02:40,025 the most vulnerable communities, 72 00:02:40,805 --> 00:02:42,164 and I'm very proud to be part of 73 00:02:42,164 --> 00:02:42,664 it. 74 00:02:42,965 --> 00:02:44,644 Well, that's amazing to hear. You know? And 75 00:02:44,644 --> 00:02:46,409 as you mentioned, just such a a great 76 00:02:46,409 --> 00:02:48,009 mission that you have there in New York 77 00:02:48,009 --> 00:02:49,469 City to serve so many, 78 00:02:49,769 --> 00:02:52,109 different unique and diverse populations. 79 00:02:52,650 --> 00:02:53,930 It really I know it makes a big 80 00:02:53,930 --> 00:02:55,849 difference for the health and and safety of 81 00:02:55,849 --> 00:02:56,509 the community. 82 00:02:56,889 --> 00:02:59,254 Now I'm curious. What, could you tell us 83 00:02:59,254 --> 00:03:01,094 about an accomplishment that you're most proud of 84 00:03:01,094 --> 00:03:02,155 from the last year? 85 00:03:02,775 --> 00:03:04,634 Sure. So there are several, 86 00:03:05,335 --> 00:03:08,215 but to speak of it, I'm very proud 87 00:03:08,215 --> 00:03:08,715 of 88 00:03:09,094 --> 00:03:11,435 a collaboration with revenue cycle services 89 00:03:12,590 --> 00:03:14,669 department at health and hospitals. And one of 90 00:03:14,669 --> 00:03:15,650 the key initiatives 91 00:03:16,030 --> 00:03:18,669 we focused on is in building an AI 92 00:03:18,669 --> 00:03:20,689 driven one stop benefits application. 93 00:03:21,629 --> 00:03:23,650 Given the diverse and underserved 94 00:03:24,110 --> 00:03:24,610 population, 95 00:03:25,870 --> 00:03:27,490 this project has been more 96 00:03:27,905 --> 00:03:29,764 meaningful and deeply impactful 97 00:03:30,465 --> 00:03:31,365 in our community. 98 00:03:31,985 --> 00:03:34,224 And to speak to it, what we have 99 00:03:34,224 --> 00:03:37,444 developed is a holistic financial counseling model 100 00:03:37,905 --> 00:03:41,985 that proactively addresses patients' financial needs, aiming to 101 00:03:41,985 --> 00:03:43,205 improve their health, 102 00:03:43,770 --> 00:03:45,069 well-being, and equity. 103 00:03:45,689 --> 00:03:48,169 And by integrating AI and advanced data and 104 00:03:48,169 --> 00:03:51,469 analytics, our financial counselors can now conduct proactive 105 00:03:51,530 --> 00:03:54,349 screenings for financial assistance and public benefits 106 00:03:54,969 --> 00:03:56,430 prior to patient appointments, 107 00:03:56,905 --> 00:03:59,305 and this reduces their waiting times. And this 108 00:03:59,305 --> 00:04:01,405 approach not only streamlines the process, 109 00:04:01,864 --> 00:04:04,924 but also maximizes the financial support a vulnerable 110 00:04:04,985 --> 00:04:05,965 patient will receive, 111 00:04:06,584 --> 00:04:08,745 ultimately helping them to alleviate some of the 112 00:04:08,745 --> 00:04:11,144 burdens they face while navigating the health care 113 00:04:11,144 --> 00:04:11,644 system. 114 00:04:12,180 --> 00:04:15,219 And it's a truly rewarding project personally as 115 00:04:15,219 --> 00:04:17,240 well, and that makes a direct impact 116 00:04:17,779 --> 00:04:19,160 to the lives of our patients. 117 00:04:20,100 --> 00:04:22,039 And, we're continue continuing 118 00:04:22,419 --> 00:04:23,539 to to more such, 119 00:04:24,339 --> 00:04:26,039 efforts in support of our population. 120 00:04:27,125 --> 00:04:28,725 Well, it's amazing to hear. You know? What 121 00:04:28,725 --> 00:04:30,665 a great idea of being able to proactively 122 00:04:30,884 --> 00:04:33,685 identify and then approach those patients who may 123 00:04:33,685 --> 00:04:36,245 need a little bit more financial support, and 124 00:04:36,245 --> 00:04:38,004 have a plan in place. I can imagine 125 00:04:38,004 --> 00:04:39,925 that it's such a relief for so many 126 00:04:39,925 --> 00:04:41,944 who are are trying to seek needed care 127 00:04:42,209 --> 00:04:44,370 not to have to worry as much about 128 00:04:44,370 --> 00:04:46,290 the financial burden that it could present for 129 00:04:46,290 --> 00:04:47,350 them and their families. 130 00:04:48,129 --> 00:04:50,449 Absolutely, Laura. And in fact, it's not just 131 00:04:50,449 --> 00:04:52,050 on the patient side too. Right? A lot 132 00:04:52,050 --> 00:04:54,290 of these benefits that's provided by our city 133 00:04:54,290 --> 00:04:55,589 government goes unnoticed 134 00:04:55,889 --> 00:04:57,670 or not even made aware of. 135 00:04:58,095 --> 00:05:00,035 So we we take incredible 136 00:05:00,495 --> 00:05:02,735 effort to make sure that there is a 137 00:05:02,735 --> 00:05:05,395 lot of proactive support to these patients 138 00:05:05,855 --> 00:05:07,714 and the appointments and their 139 00:05:08,495 --> 00:05:09,475 length of stay, 140 00:05:09,814 --> 00:05:11,935 during their time in health and hospitals is 141 00:05:11,935 --> 00:05:12,435 seamless, 142 00:05:12,814 --> 00:05:13,055 and, 143 00:05:13,909 --> 00:05:15,669 they provide the you know, we provide the 144 00:05:15,669 --> 00:05:16,169 compassionate 145 00:05:16,470 --> 00:05:18,409 service that they need the most. 146 00:05:19,349 --> 00:05:21,269 That's great to hear. You know, and and 147 00:05:21,269 --> 00:05:24,149 certainly, you know, wonderful to have that type 148 00:05:24,149 --> 00:05:26,389 of support technology to support your teams and 149 00:05:26,389 --> 00:05:28,069 then mission to go out there and do 150 00:05:28,069 --> 00:05:30,824 that. Now I'm curious. Where do you see 151 00:05:30,824 --> 00:05:32,745 the big growth opportunities for you and your 152 00:05:32,745 --> 00:05:34,604 team in the next twelve months or so? 153 00:05:35,224 --> 00:05:35,544 So, 154 00:05:36,584 --> 00:05:39,224 one of the recent focus areas is the 155 00:05:39,224 --> 00:05:41,164 establishment of our AI organization, 156 00:05:41,784 --> 00:05:44,204 under the portfolio of data and AI. 157 00:05:44,920 --> 00:05:47,800 And this organization is relatively new, and we 158 00:05:47,800 --> 00:05:51,019 are slowly ramping up our internal AI capabilities 159 00:05:51,720 --> 00:05:52,699 across our enterprise. 160 00:05:53,399 --> 00:05:55,480 In doing that, we're embracing AI with a 161 00:05:55,480 --> 00:05:58,379 very strategic blend of ambition and pragmatism, 162 00:05:59,324 --> 00:06:01,884 recognizing its potential to improve the experience for 163 00:06:01,884 --> 00:06:03,104 our patients and clinicians 164 00:06:03,884 --> 00:06:05,745 and driving operational efficiency, 165 00:06:06,604 --> 00:06:08,704 but also to make sure we enable equitable 166 00:06:08,764 --> 00:06:11,164 care delivery for New York City patients, which 167 00:06:11,164 --> 00:06:12,384 aligns with our mission. 168 00:06:13,300 --> 00:06:15,139 And in the next twelve months, I see 169 00:06:15,139 --> 00:06:15,639 significant 170 00:06:16,420 --> 00:06:19,460 growth in scaling our AI driven solutions, again, 171 00:06:19,460 --> 00:06:23,240 with guardrails across both clinical and nonclinical areas 172 00:06:23,460 --> 00:06:26,040 and fostering a culture of innovation and continuous 173 00:06:26,100 --> 00:06:26,600 learning 174 00:06:27,245 --> 00:06:29,644 within our AI and health care teams. It's 175 00:06:29,805 --> 00:06:31,504 it is the biggest growth opportunity, 176 00:06:32,444 --> 00:06:32,764 and, 177 00:06:33,324 --> 00:06:35,345 we're following the trend in health care. 178 00:06:36,284 --> 00:06:37,884 That makes a lot of sense. You know, 179 00:06:37,884 --> 00:06:40,620 certainly, a lot of possibilities and opportunities with 180 00:06:40,620 --> 00:06:42,620 AI, as you mentioned, both on the clinical 181 00:06:42,620 --> 00:06:45,100 side as well as the operational side. And 182 00:06:45,100 --> 00:06:47,600 as you're building out your AI organization, 183 00:06:49,180 --> 00:06:50,480 what does that look like, 184 00:06:51,100 --> 00:06:53,019 if you're able to speak to just briefly, 185 00:06:53,019 --> 00:06:54,764 you know, what skills are you bringing in 186 00:06:54,764 --> 00:06:56,384 in house, how are you partnering, 187 00:06:56,925 --> 00:06:57,905 with other organizations 188 00:06:58,605 --> 00:07:01,745 as you're kind of thinking about, the AI 189 00:07:01,965 --> 00:07:03,985 for New York City health plus hospitals. 190 00:07:04,444 --> 00:07:05,905 You know, what what really 191 00:07:06,860 --> 00:07:08,300 comes to mind for you in terms of, 192 00:07:08,300 --> 00:07:09,899 like, how you're building up the team, the 193 00:07:09,899 --> 00:07:11,740 skill sets, the roles, and what you really 194 00:07:11,740 --> 00:07:12,240 need, 195 00:07:12,860 --> 00:07:13,759 from your team 196 00:07:14,220 --> 00:07:15,839 in a for AI in particular? 197 00:07:17,100 --> 00:07:19,019 That's a great question as we are in 198 00:07:19,019 --> 00:07:21,095 the midst of it. So, in supporting an 199 00:07:21,095 --> 00:07:23,274 AI org, we are talking about, 200 00:07:24,615 --> 00:07:25,115 organization 201 00:07:25,495 --> 00:07:27,514 that supports strategic procurement 202 00:07:28,615 --> 00:07:31,814 and strategic development of AI solutions and supporting 203 00:07:31,814 --> 00:07:32,474 our workflows. 204 00:07:32,879 --> 00:07:36,020 And in doing that, we also understand that 205 00:07:36,639 --> 00:07:38,639 there are several other front runners in our 206 00:07:38,639 --> 00:07:41,600 health care system, several provider organizations. So there's 207 00:07:41,600 --> 00:07:42,580 a lot of learnings, 208 00:07:42,960 --> 00:07:44,180 that we want to leverage, 209 00:07:45,040 --> 00:07:45,540 including 210 00:07:46,004 --> 00:07:48,025 consortiums both in industry and academia. 211 00:07:48,564 --> 00:07:51,145 For example, Coalition for Health Care AI, 212 00:07:51,845 --> 00:07:54,645 Health AI Partnership, Valid AI. These are key 213 00:07:54,645 --> 00:07:56,504 consortiums that we are part of. 214 00:07:56,884 --> 00:07:57,384 And, 215 00:07:57,764 --> 00:07:59,705 additionally, we want to also, 216 00:08:00,019 --> 00:08:02,660 as I mentioned, leverage the blueprint from existing 217 00:08:02,660 --> 00:08:04,759 organizations, but also have an organizational 218 00:08:05,139 --> 00:08:06,519 design with the multidisciplinary 219 00:08:07,060 --> 00:08:09,879 skills needed to support AI adoption and enablement. 220 00:08:10,819 --> 00:08:13,240 And in addition to that internal 221 00:08:14,694 --> 00:08:16,935 capabilities, we also want to have an open 222 00:08:16,935 --> 00:08:18,154 ecosystem of partnerships 223 00:08:18,855 --> 00:08:20,074 that spans across 224 00:08:20,775 --> 00:08:21,835 big to small 225 00:08:22,215 --> 00:08:22,715 companies, 226 00:08:23,095 --> 00:08:23,595 startups, 227 00:08:24,455 --> 00:08:25,275 cloud providers, 228 00:08:25,895 --> 00:08:28,154 because, you know, this is such an evolving 229 00:08:28,295 --> 00:08:31,160 space, Laura, that we see that we have 230 00:08:31,160 --> 00:08:33,399 to be truly multi cloud. We need to 231 00:08:33,399 --> 00:08:36,620 have options to work with multi multiple vendors, 232 00:08:37,080 --> 00:08:38,139 and more importantly, 233 00:08:38,679 --> 00:08:41,000 also have a data strategy, support that open 234 00:08:41,000 --> 00:08:41,894 data sharing, 235 00:08:42,295 --> 00:08:44,215 with AI vendors. So there's a lot of 236 00:08:44,215 --> 00:08:46,715 focus related to open ecosystem of partnerships. 237 00:08:48,054 --> 00:08:49,815 So in terms of skills we are trying 238 00:08:49,815 --> 00:08:51,735 to bring in, we want to support the 239 00:08:51,735 --> 00:08:53,355 entirety of AI life cycle. 240 00:08:53,815 --> 00:08:56,394 Right? All the way from ideation to development 241 00:08:56,695 --> 00:08:57,274 to deployment 242 00:08:58,579 --> 00:09:00,980 to performance and monitoring and also looking at 243 00:09:00,980 --> 00:09:03,779 evaluation and outcomes. So we're in the process 244 00:09:03,779 --> 00:09:04,919 of bringing a multidisciplinary 245 00:09:05,459 --> 00:09:05,959 team, 246 00:09:06,820 --> 00:09:09,480 some through our professional services, but slowly, 247 00:09:09,940 --> 00:09:12,519 you know, develop the inbuilt in house talent, 248 00:09:13,115 --> 00:09:14,495 across data science, 249 00:09:15,754 --> 00:09:16,894 AI, engineering, 250 00:09:17,674 --> 00:09:18,495 ML ops, 251 00:09:19,274 --> 00:09:22,414 UI, UX development, and most importantly, right, integration, 252 00:09:22,955 --> 00:09:25,674 developers in support of integration with our existing 253 00:09:25,674 --> 00:09:26,174 systems. 254 00:09:27,779 --> 00:09:29,700 That's really a helpful outline. Thank you so 255 00:09:29,700 --> 00:09:31,620 much for going through that with us, Didier. 256 00:09:31,620 --> 00:09:32,120 Now 257 00:09:32,580 --> 00:09:34,820 I wanted to ask you as well, lots 258 00:09:34,820 --> 00:09:38,100 of opportunities ahead, but also challenges. What are 259 00:09:38,100 --> 00:09:39,960 some of the big challenges that you're anticipating 260 00:09:40,100 --> 00:09:41,559 over the next year or two? 261 00:09:42,565 --> 00:09:44,985 Oh, yeah. I mean, always challenges and opportunities 262 00:09:45,045 --> 00:09:47,045 go in hand. Right? But I take challenges 263 00:09:47,045 --> 00:09:50,245 as opportunities as well. So one thing, Laura, 264 00:09:50,245 --> 00:09:52,644 that, you know, I've been really spending some 265 00:09:52,644 --> 00:09:54,745 time on is to see how we integrate 266 00:09:54,805 --> 00:09:56,585 AI into our health care systems 267 00:09:58,230 --> 00:10:00,230 and how do we balance the return on 268 00:10:00,230 --> 00:10:00,730 investment. 269 00:10:01,350 --> 00:10:03,290 Yes. There is AI everywhere, 270 00:10:04,230 --> 00:10:05,529 and there's proven, 271 00:10:06,389 --> 00:10:08,330 efforts across other health systems. 272 00:10:08,870 --> 00:10:11,110 But when we talk about return on investment, 273 00:10:11,110 --> 00:10:13,565 the hard ROI such as cost savings or 274 00:10:13,565 --> 00:10:14,625 potential revenue, 275 00:10:15,725 --> 00:10:16,225 operational 276 00:10:16,764 --> 00:10:17,264 efficiencies, 277 00:10:17,644 --> 00:10:20,304 or direct improvements in patient out outcomes 278 00:10:20,684 --> 00:10:22,845 will be more straightforward to measure and track 279 00:10:22,845 --> 00:10:24,444 as we implement AI. And we need to 280 00:10:24,444 --> 00:10:27,004 make sure that we have success metrics along 281 00:10:27,004 --> 00:10:27,504 the 282 00:10:28,070 --> 00:10:28,570 line. 283 00:10:29,029 --> 00:10:31,509 But let's talk about areas where there is 284 00:10:31,509 --> 00:10:33,049 soft ROI, which includes 285 00:10:33,909 --> 00:10:35,529 more intangible benefits, 286 00:10:35,990 --> 00:10:37,529 like improving clinician 287 00:10:37,990 --> 00:10:41,209 satisfaction, reducing burnout, enhancing patient 288 00:10:41,909 --> 00:10:44,475 experience, and fostering a culture of innovation. 289 00:10:44,934 --> 00:10:47,095 These are much more difficult to quantify and 290 00:10:47,095 --> 00:10:47,595 measure. 291 00:10:47,894 --> 00:10:49,995 So achieving a balance is essential 292 00:10:50,375 --> 00:10:52,554 for the long term success of AI initiatives. 293 00:10:53,095 --> 00:10:54,774 So we need to be ensuring that while 294 00:10:54,774 --> 00:10:56,699 we focus on hard metrics like cost savings 295 00:10:56,699 --> 00:10:59,500 and efficiency, we also measure and prioritize softer 296 00:10:59,500 --> 00:11:02,620 human centric aspects of AI that contribute to 297 00:11:02,620 --> 00:11:03,439 better outcomes, 298 00:11:03,819 --> 00:11:05,199 trust, and a sustainable 299 00:11:05,500 --> 00:11:07,519 people first approach to AI integration. 300 00:11:08,539 --> 00:11:11,679 Additionally, overcoming potential resistance to change, 301 00:11:12,535 --> 00:11:15,654 ensuring data privacy and security, and upskilling our 302 00:11:15,654 --> 00:11:18,075 workforce for workforce adoption and trust 303 00:11:18,855 --> 00:11:22,134 alongside AI solutions will be, you know, hurdles 304 00:11:22,134 --> 00:11:24,154 that, you know, I anticipate to address. 305 00:11:25,639 --> 00:11:26,839 And that makes a lot of sense. You 306 00:11:26,839 --> 00:11:28,279 know, I I really love that idea of 307 00:11:28,279 --> 00:11:30,279 having those soft metrics as well as the 308 00:11:30,279 --> 00:11:32,679 hard metrics to think about the return on 309 00:11:32,679 --> 00:11:34,059 investment and keep the humanity, 310 00:11:34,519 --> 00:11:37,000 within, you know, the health care system, which 311 00:11:37,000 --> 00:11:38,860 is such a a human to human industry. 312 00:11:39,554 --> 00:11:41,475 When you look at those, you know, soft 313 00:11:41,475 --> 00:11:41,975 ROI, 314 00:11:43,075 --> 00:11:44,915 how do you think about that? How do 315 00:11:44,915 --> 00:11:46,134 you know that you've been successful? 316 00:11:47,634 --> 00:11:50,355 Yeah. So, any initiatives that, 317 00:11:50,915 --> 00:11:51,894 we we prioritize, 318 00:11:52,674 --> 00:11:54,899 and I can give an example. Right? Ambient 319 00:11:54,899 --> 00:11:56,279 listening is a great example, 320 00:11:56,659 --> 00:11:57,720 that we are actually 321 00:11:58,100 --> 00:11:59,379 looking to see how we can, 322 00:12:00,339 --> 00:12:02,100 procure and roll it out because it's a 323 00:12:02,100 --> 00:12:03,879 very proven solution in the market. 324 00:12:04,659 --> 00:12:06,179 One of the things we are doing is 325 00:12:06,179 --> 00:12:09,175 we're making sure we understand the metrics for 326 00:12:09,175 --> 00:12:10,555 success early on 327 00:12:10,934 --> 00:12:14,154 even prior to actually moving moving to proceed, 328 00:12:14,455 --> 00:12:18,075 with procurement. So having those metrics, having baselines, 329 00:12:19,014 --> 00:12:21,274 you know, again, baselines that cannot be quantified 330 00:12:21,335 --> 00:12:23,675 but can be qualified. So having baselines, 331 00:12:24,419 --> 00:12:27,620 making sure we we have inputs from the 332 00:12:27,620 --> 00:12:28,120 multidisciplinary 333 00:12:28,740 --> 00:12:31,159 team who's going to eventually use the solution. 334 00:12:31,779 --> 00:12:34,659 And having a baseline is critical to a 335 00:12:34,659 --> 00:12:37,220 project success because that's how we're able to 336 00:12:37,220 --> 00:12:39,825 then see how we're actually improving care, how 337 00:12:39,825 --> 00:12:40,485 are we 338 00:12:40,945 --> 00:12:43,184 improving the efficiency, how are we actually improving 339 00:12:43,184 --> 00:12:44,565 patient experience. So, 340 00:12:45,184 --> 00:12:47,684 there's there's a very deliberate thought 341 00:12:47,985 --> 00:12:50,565 put in place by design to have baseline 342 00:12:51,024 --> 00:12:54,850 for every AI initiatives. And, software initiatives is 343 00:12:54,850 --> 00:12:55,990 another reason. Right? 344 00:12:56,769 --> 00:12:59,889 And which is very qualitative, but, certainly, we 345 00:12:59,889 --> 00:13:02,610 we make a deliberate attempt to capture that 346 00:13:02,610 --> 00:13:03,429 early on. 347 00:13:04,450 --> 00:13:06,450 That's helpful to know. I I appreciate your 348 00:13:06,450 --> 00:13:08,144 time and kind of going a a bit 349 00:13:08,144 --> 00:13:10,305 deeper there as well. Now before we wrap 350 00:13:10,305 --> 00:13:12,065 up with our last minute or two, I 351 00:13:12,065 --> 00:13:13,745 wanted to ask, what is the number one 352 00:13:13,745 --> 00:13:15,745 thing that you're doing right now to set 353 00:13:15,745 --> 00:13:17,685 the health system up for long term success? 354 00:13:18,225 --> 00:13:20,144 That's a great question, Laura. To set New 355 00:13:20,144 --> 00:13:22,305 York City Health and hospitals up for long 356 00:13:22,305 --> 00:13:23,125 term success, 357 00:13:23,850 --> 00:13:25,850 What I'm focusing on right now is to 358 00:13:25,850 --> 00:13:28,029 build strong AI governance framework 359 00:13:28,569 --> 00:13:29,870 that ensures equity, 360 00:13:30,329 --> 00:13:30,829 fairness, 361 00:13:31,690 --> 00:13:34,350 and transparency in the use of AI technologies. 362 00:13:34,970 --> 00:13:37,754 And given the diverse and vulnerable patient populations 363 00:13:39,035 --> 00:13:41,274 populations we serve, it is essential that our 364 00:13:41,274 --> 00:13:41,995 AI solutions are fair, ethical, and free from 365 00:13:41,995 --> 00:13:42,495 bias. 366 00:13:43,195 --> 00:13:45,615 So it's a number one priority for our 367 00:13:45,835 --> 00:13:48,955 system. So by embedding governance practices, we aim 368 00:13:48,955 --> 00:13:51,695 to prioritize patient safety, reduce the disparities 369 00:13:52,179 --> 00:13:55,220 of care, and enhance the delivery while ensuring 370 00:13:55,220 --> 00:13:57,559 AI aligns with our mission of providing equitable 371 00:13:57,620 --> 00:13:59,080 care. So this approach, 372 00:13:59,940 --> 00:14:02,600 we hope. Right? The plan is to actually 373 00:14:02,820 --> 00:14:04,200 foster more trust 374 00:14:04,554 --> 00:14:06,955 and improve patient outcomes and ensure our AI 375 00:14:06,955 --> 00:14:08,975 solutions benefit all populations. 376 00:14:10,715 --> 00:14:12,014 So governance is key. 377 00:14:13,274 --> 00:14:14,554 That makes a lot of sense. I I 378 00:14:14,554 --> 00:14:16,075 know how important it is to set up 379 00:14:16,075 --> 00:14:18,154 that governance, have the right stakeholders at the 380 00:14:18,154 --> 00:14:21,689 table, and, really truly make sure that AI 381 00:14:21,909 --> 00:14:22,570 is doing, 382 00:14:23,269 --> 00:14:25,269 so much good for the organization and and 383 00:14:25,269 --> 00:14:27,589 not, getting into some of the risks involved 384 00:14:27,589 --> 00:14:29,990 as well. That's right, Laura. I think having 385 00:14:29,990 --> 00:14:31,210 a multidisciplinary 386 00:14:32,445 --> 00:14:35,424 diverse stakeholder team is key to good governance 387 00:14:35,804 --> 00:14:37,965 so that we have the diverse perspectives. And 388 00:14:37,965 --> 00:14:40,785 in health and hospitals, this includes legal, compliance, 389 00:14:41,004 --> 00:14:43,264 IT, clinical, and business leaders. 390 00:14:44,205 --> 00:14:44,524 And, 391 00:14:45,404 --> 00:14:46,945 it's been going great. 392 00:14:47,529 --> 00:14:49,450 That's amazing to hear. Didya, thank you so 393 00:14:49,450 --> 00:14:51,129 much for joining us on the podcast today. 394 00:14:51,129 --> 00:14:53,629 This has been such a informative and inspiring 395 00:14:53,769 --> 00:14:55,690 conversation, and I look forward to connecting with 396 00:14:55,690 --> 00:14:58,169 you again soon. And, also, you know, seeing 397 00:14:58,169 --> 00:15:00,089 you in person in Chicago at our health 398 00:15:00,089 --> 00:15:03,070 IT digital health revenue cycle event, in October, 399 00:15:03,475 --> 00:15:05,315 I am really looking forward to your session 400 00:15:05,315 --> 00:15:07,875 and certainly will be excited to to see 401 00:15:07,875 --> 00:15:10,115 how these initiatives are continuing to evolve and 402 00:15:10,115 --> 00:15:10,615 grow. 403 00:15:11,475 --> 00:15:13,475 Oh, it's been absolutely a pleasure talking to 404 00:15:13,475 --> 00:15:16,034 you to today, Laura, and, looking forward to 405 00:15:16,034 --> 00:15:17,654 meeting you in person as well.