WEBVTT 00:00:00.000 --> 00:00:02.580 Did you know that even in high income countries, 00:00:03.060 --> 00:00:07.000 something like 50 % of patients don't actually 00:00:07.000 --> 00:00:10.039 get treatment for psychiatric disorders? And 00:00:10.039 --> 00:00:12.080 in low to middle income countries, well, that 00:00:12.080 --> 00:00:16.239 figure can shoot up to 85%. 85%. Quite staggering. 00:00:16.559 --> 00:00:19.839 It really is. That's a massive, undeniable treatment 00:00:19.839 --> 00:00:21.839 gap. The traditional system just isn't bridging 00:00:21.839 --> 00:00:24.219 it for everyone, is it? Clearly not. Welcome 00:00:24.219 --> 00:00:26.539 to the deep dive. This is where we take a stack 00:00:26.539 --> 00:00:29.059 of sources, articles, research, notes you've 00:00:29.059 --> 00:00:31.379 sent us, and we pull out the most important bits 00:00:31.379 --> 00:00:34.899 of knowledge, the key insights. Think of it as, 00:00:34.899 --> 00:00:37.880 well, a shortcut to getting properly informed, 00:00:38.000 --> 00:00:40.200 hopefully with a few surprising facts, maybe 00:00:40.200 --> 00:00:42.560 just enough humor to keep you listening. That's 00:00:42.560 --> 00:00:44.840 good. Today, we're diving deep into a body of 00:00:44.840 --> 00:00:48.560 work exploring the profound impact of AI and 00:00:48.560 --> 00:00:50.880 digital technologies on global health care. We're 00:00:50.880 --> 00:00:53.500 looking particularly at mental health and also 00:00:53.500 --> 00:00:56.640 the future of our hospitals. A huge topic. Absolutely. 00:00:57.500 --> 00:00:59.640 And guiding us through this really fascinating 00:00:59.640 --> 00:01:03.479 landscape, I'm joined by a profmo imam. His ability 00:01:03.479 --> 00:01:06.599 to synthesize all this diverse information, spot 00:01:06.599 --> 00:01:08.900 the patterns, connect the dots. It makes him, 00:01:08.900 --> 00:01:11.560 well, the ideal expert to help us unpack what 00:01:11.560 --> 00:01:14.840 truly matters in this dense but really crucial 00:01:14.840 --> 00:01:17.019 material. It's brilliant to have you here. Thank 00:01:17.019 --> 00:01:18.719 you. It's a privilege to dive into these sources 00:01:18.719 --> 00:01:21.280 with you. So right off the bat. The sources talk 00:01:21.280 --> 00:01:24.060 about a profound reform coming to health systems. 00:01:24.459 --> 00:01:27.180 It seems intrinsically linked to this digital 00:01:27.180 --> 00:01:29.379 transition. If you had to pick one thing, what's 00:01:29.379 --> 00:01:31.920 the single biggest, most fundamental change you 00:01:31.920 --> 00:01:34.040 see happening across the sector based on this 00:01:34.040 --> 00:01:36.099 material? That's a big question. Based on these 00:01:36.099 --> 00:01:38.599 sources, I'd say the single biggest change is 00:01:38.599 --> 00:01:42.599 the radical reshaping of where and how care is 00:01:42.599 --> 00:01:44.980 actually delivered. It's not just about digitizing 00:01:44.980 --> 00:01:47.159 the processes we already have. It's more fundamental. 00:01:47.299 --> 00:01:50.260 It's about a geographical and and functional 00:01:50.260 --> 00:01:53.700 reorientation. Right. The sources strongly suggest 00:01:53.700 --> 00:01:57.299 a future where the high level technologically 00:01:57.299 --> 00:02:00.439 advanced acute care sort of consolidates within 00:02:00.439 --> 00:02:03.040 hospitals. But at the same time, there's this 00:02:03.040 --> 00:02:07.200 massive and necessary push to strengthen care 00:02:07.200 --> 00:02:10.669 much closer to home. proximity, healthcare, primary 00:02:10.669 --> 00:02:13.430 care, home care, this dual movement, this push 00:02:13.430 --> 00:02:15.930 and pull, it fundamentally alters the traditional 00:02:15.930 --> 00:02:18.110 model we're used to. That tension between the 00:02:18.110 --> 00:02:21.110 high -tech hospitals and care closer to home, 00:02:21.150 --> 00:02:23.889 that's really interesting. Now, thinking specifically 00:02:23.889 --> 00:02:26.469 about mental health, these digital tools, things 00:02:26.469 --> 00:02:29.889 like smartphone apps, chat bots, they're obviously 00:02:29.889 --> 00:02:32.500 gaining traction. They are indeed. What's the 00:02:32.500 --> 00:02:35.280 most promising potential application highlighted 00:02:35.280 --> 00:02:37.840 in these sources for genuinely reaching more 00:02:37.840 --> 00:02:40.719 people, especially those who are currently underserved? 00:02:40.939 --> 00:02:42.780 Well, the most promising application, as the 00:02:42.780 --> 00:02:45.199 sources really highlight it, lies in the potential 00:02:45.199 --> 00:02:49.219 for these digital tools to democratize access 00:02:49.219 --> 00:02:52.580 and also to provide continuous objective insights. 00:02:52.580 --> 00:02:55.520 Right. Exactly. Think about smartphone apps enabling 00:02:55.520 --> 00:02:57.740 real -time data collection, data processing. 00:02:58.180 --> 00:03:00.479 They offer a pathway to get clinical insights 00:03:00.479 --> 00:03:04.580 that are relatively cheap, secure, fast, and 00:03:04.580 --> 00:03:07.580 crucially available to individuals who might 00:03:07.580 --> 00:03:10.860 face really significant barriers getting to traditional 00:03:10.860 --> 00:03:13.500 in -person care. Geographic barriers, stigma. 00:03:13.800 --> 00:03:16.259 All of those things. This capability offers a 00:03:16.259 --> 00:03:19.080 real route to extending support way beyond the 00:03:19.080 --> 00:03:22.080 clinic walls. OK. And the sources also delve 00:03:22.080 --> 00:03:24.879 into big data and machine learning AI, essentially 00:03:24.879 --> 00:03:27.259 for predicting health outcomes. Even incredibly 00:03:27.259 --> 00:03:30.280 sensitive things like suicide risk or psychosis. 00:03:30.840 --> 00:03:32.620 What's the fundamental shift that represents 00:03:32.620 --> 00:03:34.960 and how clinicians might actually work day to 00:03:34.960 --> 00:03:38.000 day? This shift is it's really towards what some 00:03:38.000 --> 00:03:40.569 call precision psychiatry. Or you could say truly 00:03:40.569 --> 00:03:42.750 individualized prediction. Precision psychiatry, 00:03:42.830 --> 00:03:45.189 okay. Yes. So instead of clinicians relying on 00:03:45.189 --> 00:03:47.409 these episodic, potentially quite subjective 00:03:47.409 --> 00:03:49.930 reports from patients during infrequent appointments. 00:03:50.150 --> 00:03:52.870 Snapshot view. Exactly. Big data and machine 00:03:52.870 --> 00:03:55.800 learning models. offer the ability to process 00:03:55.800 --> 00:03:59.120 these vast multi -dimensional data streams. This 00:03:59.120 --> 00:04:01.860 allows for continuous objective insights and 00:04:01.860 --> 00:04:04.479 crucially individualized risk estimates. Right, 00:04:04.620 --> 00:04:08.039 for that specific person. Precisely. It's moving 00:04:08.039 --> 00:04:11.400 away from a sort of population average view to 00:04:11.400 --> 00:04:14.080 a much more dynamic understanding and prediction 00:04:14.080 --> 00:04:17.000 of outcomes for the specific individual patient. 00:04:17.120 --> 00:04:19.079 Yeah. It fundamentally changes the information 00:04:19.079 --> 00:04:20.759 landscape that's available to the clinician. 00:04:20.920 --> 00:04:22.759 Okay, let's really unpack this further then. 00:04:22.759 --> 00:04:25.279 We're moving into our first proper deep dive 00:04:25.279 --> 00:04:27.579 segment now, focusing on that broader digital 00:04:27.579 --> 00:04:29.500 transition you mentioned and how these tools 00:04:29.500 --> 00:04:32.060 are reshaping care delivery right across the 00:04:32.060 --> 00:04:34.699 system. You started by talking about profound 00:04:34.699 --> 00:04:38.000 reform driven by demographic changes. What exactly 00:04:38.000 --> 00:04:40.920 did the sources zero in on when they talk about 00:04:40.920 --> 00:04:43.420 this big picture transformation? The sources 00:04:43.420 --> 00:04:46.319 are actually quite explicit here. They link the 00:04:46.319 --> 00:04:48.560 future of the health sector very strongly to 00:04:48.560 --> 00:04:51.500 advances in science and this, well, what feels 00:04:51.500 --> 00:04:53.470 like an inevitable digital transition. Right. 00:04:53.610 --> 00:04:56.310 A major driver they identify for this profound 00:04:56.310 --> 00:05:00.250 reform is the demographic transition specifically, 00:05:00.649 --> 00:05:02.810 increased longevity, particularly in more developed 00:05:02.810 --> 00:05:05.350 countries. People living longer. Exactly. People 00:05:05.350 --> 00:05:07.449 are living longer. And with that comes a greater 00:05:07.449 --> 00:05:10.529 prevalence of chronic diseases. Health systems 00:05:10.529 --> 00:05:13.129 simply must be equipped to manage these effectively, 00:05:13.230 --> 00:05:16.889 often over many years. And this requires a health 00:05:16.889 --> 00:05:19.170 system that's far more integrated than perhaps 00:05:19.170 --> 00:05:21.610 our traditional models have allowed for. So the 00:05:21.610 --> 00:05:24.220 aging population, the rise of chronic conditions. 00:05:24.779 --> 00:05:27.500 They're basically forcing this change. Precisely. 00:05:27.639 --> 00:05:30.139 And to manage this effectively, the sources call 00:05:30.139 --> 00:05:32.199 for significantly greater investment in what 00:05:32.199 --> 00:05:35.000 they term proximity healthcare. Proximity healthcare. 00:05:35.279 --> 00:05:38.000 Making care closer. Yes. Making care more accessible, 00:05:38.379 --> 00:05:40.240 closer to where people actually live and need 00:05:40.240 --> 00:05:43.579 it most. And as I mentioned just now, they articulate 00:05:43.579 --> 00:05:46.860 this fascinating tension. The need for this high 00:05:46.860 --> 00:05:49.240 -level, technologically supported acute care 00:05:49.240 --> 00:05:52.800 within hospitals has to coexist and be effectively 00:05:52.800 --> 00:05:55.360 integrated with long -term care delivered in 00:05:55.360 --> 00:05:57.399 primary care settings and home settings. The 00:05:57.399 --> 00:05:59.839 sources seem to see hospitals increasingly focusing 00:05:59.839 --> 00:06:03.339 on the complex, acute technological interventions 00:06:03.339 --> 00:06:06.480 solidifying their role as centers for specialized 00:06:06.480 --> 00:06:09.439 procedures, while at the same time the role of 00:06:09.439 --> 00:06:12.720 primary health care and home care is vastly strengthened. 00:06:12.879 --> 00:06:15.279 It's not just an organizational tweak, you see. 00:06:15.819 --> 00:06:18.540 It's presented in these sources as a fundamental, 00:06:18.759 --> 00:06:21.860 geographical, and functional reorientation of 00:06:21.860 --> 00:06:24.759 the entire health system. That perspective brings 00:06:24.759 --> 00:06:27.060 us neatly to the concept of smart hospitals, 00:06:27.160 --> 00:06:29.720 which is mentioned in the source material. What 00:06:29.720 --> 00:06:32.160 actually defines a smart hospital according to 00:06:32.160 --> 00:06:34.740 this body of work? What are the core characteristics? 00:06:35.339 --> 00:06:37.339 Well, the sources conceptualize smart hospitals 00:06:37.339 --> 00:06:40.560 with a, I'd say, paramount focus on two key things. 00:06:41.299 --> 00:06:44.060 patient safety, and genuine patient -centeredness. 00:06:44.279 --> 00:06:46.000 Patient -centeredness. Yes, they even refer to 00:06:46.000 --> 00:06:48.279 it as achieving a true patient -centricity model. 00:06:48.899 --> 00:06:51.139 The aspiration seems to be for pan -European 00:06:51.139 --> 00:06:53.740 and international health services to really prioritize 00:06:53.740 --> 00:06:56.019 this transformation. And is there a driver for 00:06:56.019 --> 00:06:58.000 this beyond just wanting things to be better? 00:06:58.250 --> 00:07:00.949 Oh, yes, the urgency isn't purely theoretical. 00:07:01.529 --> 00:07:04.449 The sources cite empirical evidence, including 00:07:04.449 --> 00:07:07.350 an EU survey which found that over a quarter, 00:07:07.750 --> 00:07:10.850 a quarter of respondents believe they or a family 00:07:10.850 --> 00:07:13.310 member had experienced an adverse health care 00:07:13.310 --> 00:07:16.790 event in hospitals. Wow, over 25 percent. Indeed. 00:07:17.290 --> 00:07:20.069 That stark statistic alone really underscores 00:07:20.069 --> 00:07:22.490 the vital need for the kind of safety improvements 00:07:22.490 --> 00:07:24.389 that digital transformation might facilitate. 00:07:24.720 --> 00:07:27.139 That figure is quite sobering, isn't it? So it's 00:07:27.139 --> 00:07:29.060 about safety. It's about the patient experience. 00:07:29.600 --> 00:07:31.819 How do the sources suggest we actually measure 00:07:31.819 --> 00:07:34.819 success and drive this complex change towards 00:07:34.819 --> 00:07:37.439 smarter hospitals? Because presumably it's not 00:07:37.439 --> 00:07:39.579 just about buying shiny new equipment. Absolutely 00:07:39.579 --> 00:07:42.319 not. No, the sources are very clear that governance 00:07:42.319 --> 00:07:44.800 and sophisticated change management strategies 00:07:44.800 --> 00:07:47.519 are critical pillars here. They call for the 00:07:47.519 --> 00:07:50.459 design of an evidence -based framework. specifically 00:07:50.459 --> 00:07:52.639 for measuring both intermediate and long -term 00:07:52.639 --> 00:07:55.079 outcomes that result from hospital planning, 00:07:55.139 --> 00:07:57.579 design, and management interventions. An evidence 00:07:57.579 --> 00:07:59.860 -based framework. Yes, and this framework needs 00:07:59.860 --> 00:08:02.379 to be robust, obviously drawing on data, but 00:08:02.379 --> 00:08:05.560 also customizable to different national or federal 00:08:05.560 --> 00:08:08.120 contexts. Furthermore, they really stress that 00:08:08.120 --> 00:08:11.560 developing, disseminating, and, crucially, sustaining 00:08:11.560 --> 00:08:14.779 these best practices requires extensive partnership. 00:08:15.120 --> 00:08:17.560 Partnership with whom? Well, they mentioned specific 00:08:17.560 --> 00:08:19.990 international organizations. the European Society 00:08:19.990 --> 00:08:23.269 of Quality Health Care, ESQH, the European Hospital 00:08:23.269 --> 00:08:26.670 and Health Care Federation, HAP, the WHO Patients 00:08:26.670 --> 00:08:29.009 for Patient Safety Program, the International 00:08:29.009 --> 00:08:32.470 Alliance of Patients Organizations, IAPO, and 00:08:32.470 --> 00:08:35.769 the European Patients Forum. A lot of acronyms 00:08:35.769 --> 00:08:38.289 there. Quite. But the point is, collaborating 00:08:38.289 --> 00:08:40.769 with these bodies, alongside national coalitions, 00:08:40.970 --> 00:08:43.269 health professional bodies, universities, policymakers, 00:08:43.909 --> 00:08:46.490 it's presented as absolutely essential for success. 00:08:46.889 --> 00:08:49.409 It's a deeply collaborative vision, not some 00:08:49.409 --> 00:08:51.730 sort of top -down technology mandate. It certainly 00:08:51.730 --> 00:08:54.320 sounds like it requires broad alignment, a lot 00:08:54.320 --> 00:08:56.220 of people working together. The sources also 00:08:56.220 --> 00:08:58.360 use some language that, well, it might sound 00:08:58.360 --> 00:09:00.840 a bit industrial, maybe manufacturing -like when 00:09:00.840 --> 00:09:03.639 applied to healthcare. Terms like processes and 00:09:03.639 --> 00:09:06.039 flow. Can you help us understand that perspective? 00:09:06.399 --> 00:09:08.480 It's an interesting perspective, isn't it? It 00:09:08.480 --> 00:09:12.200 draws on principles often found in production 00:09:12.200 --> 00:09:15.399 management, as referenced in the material. Concepts 00:09:15.399 --> 00:09:17.720 articulated by thinkers like Modig and Alstrom, 00:09:18.019 --> 00:09:20.700 for example. Okay. Processes are viewed as structures 00:09:20.700 --> 00:09:23.759 or perhaps channels through which units move 00:09:23.759 --> 00:09:27.539 or flow. In this view, flow and process aren't 00:09:27.539 --> 00:09:30.539 really separate things, but sort of complementary 00:09:30.539 --> 00:09:32.809 ways of looking at the same phenomenon. Right. 00:09:33.070 --> 00:09:35.549 When you translate that to health care, the flow 00:09:35.549 --> 00:09:38.250 units are fundamentally the patients themselves, 00:09:39.029 --> 00:09:41.110 along with all their associated information, 00:09:41.610 --> 00:09:43.909 their medical data, their history, their journey 00:09:43.909 --> 00:09:46.870 through the system. Ah, I see. The sources describe 00:09:46.870 --> 00:09:48.830 patient journeys through the health system, whether 00:09:48.830 --> 00:09:51.429 that's within a single hospital or across different 00:09:51.429 --> 00:09:53.850 care settings, as being managed by interconnected 00:09:53.850 --> 00:09:56.470 functional units. Functional units, like teams. 00:09:56.789 --> 00:09:59.120 Exactly. These units are typically multi -professional 00:09:59.120 --> 00:10:01.759 teams or what is sometimes called microsystems 00:10:01.759 --> 00:10:04.100 within a health care setting. So the patient 00:10:04.100 --> 00:10:06.440 is the unit moving through the system of teams. 00:10:06.840 --> 00:10:09.740 That's the core idea. And within this model, 00:10:10.279 --> 00:10:12.580 care activities are described as being co -created 00:10:12.580 --> 00:10:15.580 with the patient. Co -created? Yes. The goal 00:10:15.580 --> 00:10:18.419 of this co -creation is to continuously refine 00:10:18.419 --> 00:10:21.179 value, which, in the context of health care, 00:10:21.399 --> 00:10:23.240 obviously means improving the patient's health 00:10:23.240 --> 00:10:26.419 and their well -being. Process models then become 00:10:26.419 --> 00:10:28.799 these vital tools for describing the structure 00:10:28.799 --> 00:10:31.789 of this flow. They outline specific roles and 00:10:31.789 --> 00:10:34.029 responsibilities within the teams. They define 00:10:34.029 --> 00:10:36.289 how management and continuous development activities 00:10:36.289 --> 00:10:39.370 are organized. And crucially, they detail how 00:10:39.370 --> 00:10:41.649 various vertical functional units like specific 00:10:41.649 --> 00:10:44.429 departments or clinics need to interconnect. 00:10:44.610 --> 00:10:46.529 And why is that interconnection so important? 00:10:46.690 --> 00:10:48.929 Well, this interconnection is absolute key to 00:10:48.929 --> 00:10:51.370 ensuring a smooth, efficient patient journey 00:10:51.370 --> 00:10:54.909 and avoiding what the sources call suboptimization. 00:10:55.870 --> 00:10:57.730 Suboptimization. Yes, that's when one part of 00:10:57.730 --> 00:10:59.769 the system might operate very efficiently. isolation, 00:11:00.110 --> 00:11:02.690 but inadvertently creates bottlenecks or issues 00:11:02.690 --> 00:11:05.570 for the overall patient flow. You know, one clinic 00:11:05.570 --> 00:11:08.250 runs perfectly on time, but the handover to the 00:11:08.250 --> 00:11:10.950 next stage is chaotic. Right, I understand. It 00:11:10.950 --> 00:11:13.289 messes up the whole journey. Exactly. It's about 00:11:13.289 --> 00:11:15.970 optimizing the whole journey, not just the individual 00:11:15.970 --> 00:11:18.519 steps within it. That reframing of the patient 00:11:18.519 --> 00:11:21.519 journey as a flow managed through interconnected 00:11:21.519 --> 00:11:24.139 processes, that's actually really insightful 00:11:24.139 --> 00:11:26.299 for understanding system design, isn't it? I 00:11:26.299 --> 00:11:28.879 think so, yes. Let's shift gears now and dive 00:11:28.879 --> 00:11:31.360 into a major theme that weaves through the sources. 00:11:32.039 --> 00:11:34.539 Digital mental health. We all carry smartphones. 00:11:34.840 --> 00:11:37.799 The sources clearly see immense potential there. 00:11:37.879 --> 00:11:40.639 They absolutely do. The smartphone is presented 00:11:40.639 --> 00:11:44.779 as a, well, a remarkably powerful tool for real 00:11:44.779 --> 00:11:47.340 time data gathering and processing. Real time? 00:11:47.500 --> 00:11:50.700 Yes. And this capability is enabling this concept 00:11:50.700 --> 00:11:53.159 of digital phenotyping. Digital phenotyping? 00:11:53.200 --> 00:11:55.879 What exactly is that? It refers to the ability 00:11:55.879 --> 00:11:59.360 to quantify aspects of the human phenotype, basically. 00:11:59.919 --> 00:12:02.840 How an illness manifests in an individual using 00:12:02.840 --> 00:12:05.279 data that's passively or actively collected from 00:12:05.279 --> 00:12:07.480 their personal digital devices. Think about the 00:12:07.480 --> 00:12:09.639 limitations of traditional clinical appointments. 00:12:09.929 --> 00:12:12.769 They're snapshots in time, aren't they? Relying 00:12:12.769 --> 00:12:15.110 on patients or caregivers recalling symptoms, 00:12:15.529 --> 00:12:18.549 which can be subject to recall bias. Or it might 00:12:18.549 --> 00:12:21.450 only capture variations between those infrequent 00:12:21.450 --> 00:12:23.490 visits. Right. You only see what's happening 00:12:23.490 --> 00:12:26.149 on that particular Tuesday morning. Precisely. 00:12:26.570 --> 00:12:28.950 Whereas digital phenotyping offers a way to get 00:12:28.950 --> 00:12:31.690 a continuous picture. So it fills in the gaps 00:12:31.690 --> 00:12:34.970 between appointments. Exactly. Unlike those intermittent 00:12:34.970 --> 00:12:37.929 appointments, computers and smartphones can potentially 00:12:37.929 --> 00:12:41.350 monitor continuously and in real time. This opens 00:12:41.350 --> 00:12:43.470 the door to developing what are called digital 00:12:43.470 --> 00:12:47.110 biomarkers. Digital biomarkers? These are objective, 00:12:47.590 --> 00:12:50.710 quantifiable, physiological, or behavioral data 00:12:50.710 --> 00:12:52.909 points. They can be collected either actively, 00:12:52.990 --> 00:12:55.250 maybe through app -based questionnaires, prompting 00:12:55.250 --> 00:12:57.029 symptom checks. Like, how are you feeling today? 00:12:57.750 --> 00:13:01.350 Sort of, yes. or passively through data generated 00:13:01.350 --> 00:13:04.129 simply by using the smartphone or perhaps wearable 00:13:04.129 --> 00:13:06.230 sensors. Okay, what kind of passive data? Well, 00:13:06.230 --> 00:13:08.509 this could include things like screen time, patterns 00:13:08.509 --> 00:13:10.970 of social media interaction, heart rhythm data 00:13:10.970 --> 00:13:13.610 from a smartwatch, sleep duration and quality, 00:13:13.990 --> 00:13:16.649 or physical activity levels tracked by the phone's 00:13:16.649 --> 00:13:19.590 accelerometer. It provides a much richer, more 00:13:19.590 --> 00:13:23.070 dynamic, continuous stream of data to understand 00:13:23.070 --> 00:13:25.950 an individual's illness trajectory almost moment 00:13:25.950 --> 00:13:29.559 to moment. That ability to get a continuous objective 00:13:29.559 --> 00:13:32.220 view of someone's state seems incredibly powerful, 00:13:32.519 --> 00:13:34.419 especially in mental health where symptoms can 00:13:34.419 --> 00:13:36.679 fluctuate so much. It certainly holds that potential, 00:13:36.940 --> 00:13:40.500 yes. The sources also strongly link this to empowering 00:13:40.500 --> 00:13:43.039 patients and democratizing access. Yes, they 00:13:43.039 --> 00:13:45.659 do. They highlight the smartphone as a great 00:13:45.659 --> 00:13:47.840 instrument for empowering patients to actively 00:13:47.840 --> 00:13:50.799 manage their own health on a daily basis. It 00:13:50.799 --> 00:13:53.460 offers the potentially cheap, secure, and fast 00:13:53.460 --> 00:13:55.700 approach to obtaining clinical insights that 00:13:55.700 --> 00:13:57.960 can be used by both the patient and their clinician. 00:13:58.539 --> 00:14:00.659 Right. And crucially, as we touched on earlier, 00:14:01.039 --> 00:14:03.059 it offers a pathway to reach individuals who 00:14:03.059 --> 00:14:06.059 might face those significant geographical, financial, 00:14:06.340 --> 00:14:08.759 or stigma -related barriers that prevent them 00:14:08.759 --> 00:14:10.840 accessing the traditional health system. So it 00:14:10.840 --> 00:14:13.500 expands reach. It aligns very strongly with the 00:14:13.500 --> 00:14:16.659 goal of putting patients first and expanding 00:14:16.659 --> 00:14:19.340 access to care beyond those traditional boundaries. 00:14:19.720 --> 00:14:22.700 What about more structured digital interventions? 00:14:23.240 --> 00:14:26.019 What specific technologies are discussed in this 00:14:26.019 --> 00:14:29.159 digital mental health space within the sources? 00:14:29.620 --> 00:14:32.620 The sources discuss several key ones. Chatbots, 00:14:32.840 --> 00:14:35.840 for instance. Chatbots. AI conversations? Essentially, 00:14:36.120 --> 00:14:38.500 yes. Digital systems designed to interact using 00:14:38.500 --> 00:14:41.519 natural language processing. Their use for mental 00:14:41.519 --> 00:14:44.120 health promotion and basic care is increasing. 00:14:44.320 --> 00:14:46.809 Right. And the sources see them as potentially 00:14:46.809 --> 00:14:49.210 democratizing access to psychotherapeutic care, 00:14:49.590 --> 00:14:52.190 making some level of support available to 3 .47. 00:14:52.610 --> 00:14:54.730 Right. Well, they also note that this area is 00:14:54.730 --> 00:14:57.029 still in quite early development. And there's 00:14:57.029 --> 00:14:59.090 a current lack of standardization in how studies 00:14:59.090 --> 00:15:01.389 evaluating them are conducted, which makes it 00:15:01.389 --> 00:15:03.590 hard to compare results effectively. OK. What 00:15:03.590 --> 00:15:06.070 else? Telepsychiatry is another key technology 00:15:06.070 --> 00:15:09.110 mentioned, which of course saw a massive rapid 00:15:09.110 --> 00:15:11.629 uptake during the COVID -19 pandemic when face 00:15:11.629 --> 00:15:13.210 -to -face consultations just weren't available 00:15:13.210 --> 00:15:16.950 for many. Yes, huge shift then. Huge. The sources 00:15:16.950 --> 00:15:19.090 mention its recognition across several cultures 00:15:19.090 --> 00:15:22.409 now as a valid substitute for in -person sessions. 00:15:23.250 --> 00:15:25.610 Reviews cited in the material suggest success 00:15:25.610 --> 00:15:28.149 in terms of patient and clinician satisfaction. 00:15:28.379 --> 00:15:31.860 decreased no -show rates because those logistical 00:15:31.860 --> 00:15:34.600 barriers like travel are removed. Makes sense. 00:15:34.700 --> 00:15:36.759 And it's considered well established now for 00:15:36.759 --> 00:15:40.019 specific populations like older adults, children, 00:15:40.460 --> 00:15:43.820 and adolescents. Furthermore, some reviews suggest 00:15:43.820 --> 00:15:46.259 it seems broadly equivalent to traditional therapy 00:15:46.259 --> 00:15:48.940 regarding the therapeutic alliance. The therapeutic 00:15:48.940 --> 00:15:51.340 alliance, the connection between therapist and 00:15:51.340 --> 00:15:53.659 patient. Exactly, which is of course a critical 00:15:53.659 --> 00:15:55.639 component of successful outcomes in therapy. 00:15:55.759 --> 00:15:58.940 So with all these tools, the continuous monitoring, 00:15:59.080 --> 00:16:02.679 the chatbots, telepsychiatry, does this material 00:16:02.679 --> 00:16:05.720 paint a picture of a future digital clinic? Is 00:16:05.720 --> 00:16:07.620 that where we're heading? Absolutely. The sources 00:16:07.620 --> 00:16:10.340 explicitly paint a picture of a potential digital 00:16:10.340 --> 00:16:12.139 clinic that could leverage these capabilities. 00:16:12.179 --> 00:16:14.279 What would that look like? Well, it could involve 00:16:14.279 --> 00:16:16.919 real time monitoring of symptoms using smartphones 00:16:16.919 --> 00:16:19.519 and various sensors, as we discussed. This stream 00:16:19.519 --> 00:16:24.299 of complex data would ideally be curated by skilled 00:16:24.299 --> 00:16:26.799 professionals who can translate these insights 00:16:26.799 --> 00:16:29.720 into accessible information. So not just raw 00:16:29.720 --> 00:16:32.840 data overload. No, definitely not. The idea is 00:16:32.840 --> 00:16:35.659 that this allows for data -driven, accessible, 00:16:35.940 --> 00:16:38.509 shared decision making between the patient and 00:16:38.509 --> 00:16:40.929 their clinician. It's about using the technology 00:16:40.929 --> 00:16:43.490 and the data it generates to provide a richer, 00:16:43.509 --> 00:16:45.730 more continuous understanding of the patient's 00:16:45.730 --> 00:16:48.029 state, enabling more informed discussions and 00:16:48.029 --> 00:16:50.549 treatment choices. The technology in this vision 00:16:50.549 --> 00:16:53.629 is meant to enhance, not entirely replace, the 00:16:53.629 --> 00:16:55.769 human element of care. That's a key point. And 00:16:55.769 --> 00:16:57.409 finally, in this segment, the sources call out 00:16:57.409 --> 00:17:00.149 a specific category, digital therapeutics or 00:17:00.149 --> 00:17:03.169 DTX. How are these different from, say, a wellness 00:17:03.169 --> 00:17:05.329 app, and what examples are given? Right. Digital 00:17:05.329 --> 00:17:07.670 therapeutics are presented as a distinct category. 00:17:07.869 --> 00:17:10.470 Unlike general wellness apps, DTX are defined 00:17:10.470 --> 00:17:13.430 as high -quality software programs designed explicitly 00:17:13.430 --> 00:17:15.869 to prevent, manage, or treat a specific medical 00:17:15.869 --> 00:17:18.009 disorder or disease. OK, so they make medical 00:17:18.009 --> 00:17:20.890 claims. Exactly. And they often require regulatory 00:17:20.890 --> 00:17:23.730 approval, much like a drug or a medical device 00:17:23.730 --> 00:17:26.890 would. Oh, I see. The sources note their particularly 00:17:26.890 --> 00:17:29.710 high growth in the mental health space. They 00:17:29.710 --> 00:17:32.029 mentioned the first FDA -approved DTX was back 00:17:32.029 --> 00:17:36.109 in 2017 for substance use and opioid use disorder. 00:17:36.269 --> 00:17:39.009 Right. Interestingly, the COVID -19 pandemic 00:17:39.009 --> 00:17:41.569 actually spurred a more streamlined review process 00:17:41.569 --> 00:17:44.589 for digital health devices addressing psychiatric 00:17:44.589 --> 00:17:46.789 disorders, which helped accelerate the market 00:17:46.789 --> 00:17:49.670 entry for several DTX products. Any examples 00:17:49.670 --> 00:17:51.930 mentioned? Yes. Examples cited in the sources 00:17:51.930 --> 00:17:54.369 include things like an app -guided hardware device 00:17:54.369 --> 00:17:57.079 designed to treat depression. specific products 00:17:57.079 --> 00:17:59.960 for improving sleep, and even a video game platform 00:17:59.960 --> 00:18:02.339 that has been cleared as an adjunct treatment 00:18:02.339 --> 00:18:05.779 for ADHD. A video game as treatment. As an adjunct 00:18:05.779 --> 00:18:08.000 treatment, yes. The sources discuss the potential 00:18:08.000 --> 00:18:11.180 for DTX to become a sort of default tool prescribed 00:18:11.180 --> 00:18:14.480 by clinicians, perhaps similar to how pharmacotherapy 00:18:14.480 --> 00:18:17.440 is used today, particularly if the necessary 00:18:17.440 --> 00:18:19.539 infrastructure, things like digital formularies 00:18:19.539 --> 00:18:22.059 and reimbursement models, develop alongside it. 00:18:22.099 --> 00:18:24.490 But it's not all rosy. No, they do inject a note 00:18:24.490 --> 00:18:26.829 of caution. They state that while the field is 00:18:26.829 --> 00:18:29.789 advancing rapidly, early trials haven't universally 00:18:29.789 --> 00:18:33.450 shown all positive results, which really just 00:18:33.450 --> 00:18:36.069 underscores the need for continued rigorous evaluation. 00:18:36.509 --> 00:18:39.869 That's a really clear distinction then, DTX, 00:18:39.910 --> 00:18:42.789 as regulated medical interventions. It's clear 00:18:42.789 --> 00:18:45.410 there's immense technological potential reshaping 00:18:45.410 --> 00:18:48.029 how care is delivered from that system level 00:18:48.029 --> 00:18:50.750 flow right down to individual patient monitoring. 00:18:51.579 --> 00:18:54.000 Welcome back to the Deep Dive. We've explored 00:18:54.000 --> 00:18:56.559 how digital tools are starting to reshape healthcare 00:18:56.559 --> 00:18:59.319 delivery, especially in mental health. Now we're 00:18:59.319 --> 00:19:01.559 shifting focus a bit to the engine driving much 00:19:01.559 --> 00:19:04.200 of this potential. The power of big data and 00:19:04.200 --> 00:19:06.539 machine learning. The engine room, you might 00:19:06.539 --> 00:19:09.079 say. Exactly. The sources really delve into this 00:19:09.079 --> 00:19:11.279 as foundational. How do they actually define 00:19:11.279 --> 00:19:13.759 big data? And specifically, what are these Vs 00:19:13.759 --> 00:19:15.960 they mention? It sounds a bit technical. It does, 00:19:15.980 --> 00:19:18.170 but it's a useful framework. The sources spend 00:19:18.170 --> 00:19:20.289 time defining big data because it is distinct 00:19:20.289 --> 00:19:22.549 from traditional data sets. They use the widely 00:19:22.549 --> 00:19:24.569 accepted framework of the five V's. Okay, the 00:19:24.569 --> 00:19:26.849 five V's, what are they? Alright, first there's 00:19:26.849 --> 00:19:29.589 velocity. This refers to the increasing speed 00:19:29.589 --> 00:19:32.069 at which data is generated, often in real time, 00:19:32.529 --> 00:19:34.630 from all sorts of connected devices. Right, speed. 00:19:34.730 --> 00:19:38.029 Second is volume. Just the sheer massive amount 00:19:38.029 --> 00:19:41.130 of data being produced, constantly growing. Terabytes, 00:19:41.309 --> 00:19:44.690 petabytes. Huge scale. Huge. Third is variety. 00:19:45.000 --> 00:19:47.220 the highly diversified nature of the data. It 00:19:47.220 --> 00:19:49.559 comes from countless sources, electronic health 00:19:49.559 --> 00:19:52.660 records, EHRs, mobile devices, social media, 00:19:52.859 --> 00:19:55.839 genomics, sensors, and it's in different formats 00:19:55.839 --> 00:19:58.099 too, structured numerical data, unstructured 00:19:58.099 --> 00:20:01.059 text notes, images, audio. Okay, so messy data. 00:20:01.220 --> 00:20:04.319 Can be, yes, which leads to the fourth V veracity. 00:20:04.509 --> 00:20:06.890 This speaks to the reliability, the accuracy, 00:20:07.049 --> 00:20:09.730 the trustworthiness of the data, which is a critical 00:20:09.730 --> 00:20:12.549 factor given the diverse sources and the potential 00:20:12.549 --> 00:20:15.329 for errors or bias. How good is the data, basically? 00:20:15.470 --> 00:20:17.230 Essentially, yes. Yeah. And finally, the fifth 00:20:17.230 --> 00:20:20.329 V is value, the practical applicability and utility 00:20:20.329 --> 00:20:22.670 of the data. Can we actually extract meaningful 00:20:22.670 --> 00:20:26.089 insights and value from this vast, sometimes 00:20:26.089 --> 00:20:27.970 messy pool? Right. Does it actually tell us anything 00:20:27.970 --> 00:20:31.059 useful? Exactly. And this whole picture contrasts 00:20:31.059 --> 00:20:33.779 quite starkly with small data, which typically 00:20:33.779 --> 00:20:36.519 involves static cross -sectional data collected 00:20:36.519 --> 00:20:39.859 from specific population samples. Big data often 00:20:39.859 --> 00:20:42.960 aims for an an all approach, attempting to capture 00:20:42.960 --> 00:20:45.480 continuous information from entire systems or 00:20:45.480 --> 00:20:48.119 very large populations. That framework really 00:20:48.119 --> 00:20:50.880 helps clarify what we mean by big data. So how 00:20:50.880 --> 00:20:53.759 does machine learning, this branch of AI, actually 00:20:53.759 --> 00:20:57.099 leverage this massive complex data? Well, machine 00:20:57.099 --> 00:20:59.180 learning is fundamentally about systems that 00:20:59.180 --> 00:21:02.279 learn automatically from data without being explicitly 00:21:02.279 --> 00:21:04.720 programmed for every single possible scenario. 00:21:05.059 --> 00:21:07.680 Learning from experience, like humans. In a way, 00:21:07.880 --> 00:21:10.940 yes. It uses mathematical models that learn relationships 00:21:10.940 --> 00:21:13.160 and patterns implicitly from the data they're 00:21:13.160 --> 00:21:15.539 fed. The sources highlight how this is particularly 00:21:15.539 --> 00:21:18.099 relevant in areas like psychiatry, because these 00:21:18.099 --> 00:21:20.680 models can handle the complex multi -dimensional 00:21:20.680 --> 00:21:23.619 data sets generated by mobile sensors, EHRs, 00:21:23.799 --> 00:21:26.099 genomic data, all those sources that constitute 00:21:26.099 --> 00:21:29.180 big data. They broadly discuss two main categories 00:21:29.180 --> 00:21:31.900 relevant here. There's supervised learning. Yes, 00:21:32.099 --> 00:21:34.339 where the model is trained on a data set that 00:21:34.339 --> 00:21:37.160 includes both the input variables and the desired 00:21:37.160 --> 00:21:39.849 output or outcome. For example, predicting a 00:21:39.849 --> 00:21:42.410 diagnosis based on a set of symptoms. It learns 00:21:42.410 --> 00:21:45.930 to map the inputs to the outputs and then applies 00:21:45.930 --> 00:21:49.130 this learning to predict outcomes on new unseen 00:21:49.130 --> 00:21:51.890 data. Okay. Training it with answers? You could 00:21:51.890 --> 00:21:54.630 put it that way. The other main type is unsupervised 00:21:54.630 --> 00:21:57.650 learning. This involves exploring unlabeled data 00:21:57.839 --> 00:22:00.380 data without predefined answers to find inherent 00:22:00.380 --> 00:22:02.900 structures or clusters within it. For example, 00:22:03.240 --> 00:22:05.279 grouping patients with similar symptom patterns 00:22:05.279 --> 00:22:07.839 together, even without a predefined diagnosis 00:22:07.839 --> 00:22:10.279 in mind. Finding hidden patterns. Precisely. 00:22:10.400 --> 00:22:12.839 This ability to find patterns in complex data 00:22:12.839 --> 00:22:15.400 leads to some incredibly sensitive, but as you 00:22:15.400 --> 00:22:18.160 say, potentially vital, applications discussed 00:22:18.160 --> 00:22:21.019 in the sources, specifically predicting severe 00:22:21.019 --> 00:22:23.299 outcomes like suicide risk and psychosis risk. 00:22:23.400 --> 00:22:25.980 How is big data and machine learning being applied 00:22:25.980 --> 00:22:27.960 in these areas according to the material? The 00:22:27.960 --> 00:22:31.059 sources highlight the, well, the pressing need 00:22:31.059 --> 00:22:33.259 for more individualized prediction when it comes 00:22:33.259 --> 00:22:36.140 to suicide risk. Traditional methods, as we know, 00:22:36.500 --> 00:22:39.140 often rely on population level statistics or 00:22:39.140 --> 00:22:41.839 simple risk checklists. Which aren't always accurate 00:22:41.839 --> 00:22:45.660 for an individual. Exactly. They have limitations 00:22:45.660 --> 00:22:47.900 in predicting risk for a specific individual 00:22:47.900 --> 00:22:51.960 at a specific time. This is where precision psychiatry 00:22:51.960 --> 00:22:54.720 comes back in using that multi -dimensional big 00:22:54.720 --> 00:22:57.460 data, processed by sophisticated algorithms, 00:22:58.000 --> 00:23:00.579 to make more accurate individualized predictions 00:23:00.579 --> 00:23:03.619 of mental health outcomes. And crucially, to 00:23:03.619 --> 00:23:06.039 guide targeted preventative interventions. Right. 00:23:06.200 --> 00:23:09.380 Proactive, not just reactive. Hopefully. The 00:23:09.380 --> 00:23:11.339 courses discuss machine learning models developed 00:23:11.339 --> 00:23:13.779 specifically for high -risk populations, such 00:23:13.779 --> 00:23:15.900 as individuals with mood disorders or bipolar 00:23:15.900 --> 00:23:18.819 disorder. These models use a really wide array 00:23:18.819 --> 00:23:21.759 of data clinical variables from EHRs, demographic 00:23:21.759 --> 00:23:24.339 information, and in some research studies, even 00:23:24.339 --> 00:23:26.660 genomic data. And how accurate are they? Well, 00:23:26.759 --> 00:23:28.660 the sources mentioned some promising accuracy 00:23:28.660 --> 00:23:31.980 rates in specific studies. They cite AUCs. That's 00:23:31.980 --> 00:23:34.339 AREOBER. Under the receiver operating characteristic 00:23:34.339 --> 00:23:37.099 curve, a measure of accuracy ranging from 0 .77 00:23:37.099 --> 00:23:41.039 up to as high as 0 .98 in predicting suicide 00:23:41.039 --> 00:23:43.700 attempts within certain populations in some studies. 00:23:43.900 --> 00:23:47.599 0 .98. That sounds incredibly high. It is very 00:23:47.599 --> 00:23:50.319 high. For this kind of prediction, yes. An AUC 00:23:50.430 --> 00:23:53.490 close to one indicates very high accuracy in 00:23:53.490 --> 00:23:55.529 distinguishing between those who will and won't 00:23:55.529 --> 00:23:58.009 experience the outcome. They also identify key 00:23:58.009 --> 00:24:00.450 predictors that these models consistently highlight 00:24:00.450 --> 00:24:02.549 as significant. Like what? Things like prior 00:24:02.549 --> 00:24:05.230 hospitalizations, the presence of comorbidities, 00:24:05.569 --> 00:24:07.730 other health conditions, psychotic symptoms, 00:24:08.269 --> 00:24:10.250 previous suicide attempts, which is understandably 00:24:10.250 --> 00:24:12.470 a very strong predictor, and the presence of 00:24:12.470 --> 00:24:15.559 a personality disorder. Okay. The sources cite 00:24:15.559 --> 00:24:17.900 machine learning models for suicide risk prediction, 00:24:18.160 --> 00:24:20.700 showing promising accuracy, with AUCs up to 0 00:24:20.700 --> 00:24:23.359 .98 in some studies, identifying key predictors 00:24:23.359 --> 00:24:25.880 like previous suicide attempts and personality 00:24:25.880 --> 00:24:29.220 disorder. This shifts us towards precision psychiatry, 00:24:29.579 --> 00:24:31.920 making individualized predictions from vast datasets. 00:24:32.359 --> 00:24:35.119 Those accuracy numbers, particularly that 0 .98, 00:24:35.339 --> 00:24:37.680 are quite striking for something as complex as 00:24:37.680 --> 00:24:41.559 suicide risk. Moving on to psychosis risk, the 00:24:41.559 --> 00:24:43.319 sources specifically mentioned something called 00:24:43.319 --> 00:24:47.680 a transdiagnostic risk calculator. What exactly 00:24:47.680 --> 00:24:50.420 is that and why is it needed? Right. This is 00:24:50.420 --> 00:24:53.160 a fascinating example of trying to put prediction 00:24:53.160 --> 00:24:56.079 into actual clinical practice. The calculator 00:24:56.079 --> 00:24:58.359 was developed and then externally validated within 00:24:58.359 --> 00:25:01.420 the South London and Maudsley NHS Foundation 00:25:01.420 --> 00:25:04.420 Trust, known as SLAM, one of the largest mental 00:25:04.420 --> 00:25:07.279 health providers in the UK. OK, SLAM. And why 00:25:07.279 --> 00:25:09.900 develop it? The why is crucial. Many individuals 00:25:09.900 --> 00:25:11.980 who eventually go on to experience a first episode 00:25:11.980 --> 00:25:14.940 of psychosis are not identified during the potentially 00:25:14.940 --> 00:25:17.119 high -risk prodromal stages, the early warning 00:25:17.119 --> 00:25:19.220 phase, through traditional clinical routes. They 00:25:19.220 --> 00:25:21.259 slip through the net. Right. This calculator 00:25:21.259 --> 00:25:23.940 is designed for systematic detection of psychosis 00:25:23.940 --> 00:25:26.880 risk within secondary mental health care populations, 00:25:27.299 --> 00:25:29.299 basically by screening routine data that's already 00:25:29.299 --> 00:25:31.759 been collected. Screening existing data, okay. 00:25:31.940 --> 00:25:34.480 What's special about it? Its core characteristics, 00:25:34.740 --> 00:25:37.400 as detailed in the sources, are quite innovative. 00:25:38.180 --> 00:25:41.759 Firstly, It's lifespan inclusive. It can be applied 00:25:41.759 --> 00:25:44.759 to individuals of any age. It uses clinically 00:25:44.759 --> 00:25:47.180 -based predictors, features identified through 00:25:47.180 --> 00:25:49.720 clinical knowledge, as being associated with 00:25:49.720 --> 00:25:52.599 psychosis risk, selected through a rigorous process. 00:25:53.180 --> 00:25:56.579 It's transdiagnostic. Transdiagnostic, meaning? 00:25:56.640 --> 00:25:58.859 Meaning it works across different diagnostic 00:25:58.859 --> 00:26:02.140 categories or presentations. It's not just for 00:26:02.140 --> 00:26:04.779 people already labeled as high risk for psychosis. 00:26:05.519 --> 00:26:08.400 It provides individualized risk estimates rather 00:26:08.400 --> 00:26:11.859 than just a binary yes or no. It's designed to 00:26:11.859 --> 00:26:14.380 be cheap and automated, screening EHRs in the 00:26:14.380 --> 00:26:16.420 background, so it's highly scalable for large 00:26:16.420 --> 00:26:19.839 populations. And finally, it's built for e -health 00:26:19.839 --> 00:26:22.160 implementation, designed to be integrated directly 00:26:22.160 --> 00:26:24.529 into clinical systems. So it's designed to catch 00:26:24.529 --> 00:26:26.269 people who might otherwise slip through that 00:26:26.269 --> 00:26:28.809 net. Has it actually been tested outside of SLAM 00:26:28.809 --> 00:26:31.150 where it was developed? That's exactly the intention. 00:26:31.369 --> 00:26:33.829 And yes, the sources do discuss replication studies 00:26:33.829 --> 00:26:36.170 in other UK settings, specifically Canton and 00:26:36.170 --> 00:26:38.650 Islington and Oxford Health NHS trusts. And did 00:26:38.650 --> 00:26:40.650 it work there, too? These studies were important 00:26:40.650 --> 00:26:42.710 because these populations had key differences 00:26:42.710 --> 00:26:45.430 compared to SLAM, for instance. CNI had an older 00:26:45.430 --> 00:26:48.029 average age, fewer males, different prevalence 00:26:48.029 --> 00:26:50.529 rates for things like substance use or anxiety 00:26:50.529 --> 00:26:53.049 disorders. Despite these variations, the risk 00:26:53.049 --> 00:26:56.210 calculator retained acceptable performance. The 00:26:56.210 --> 00:26:59.630 source is, quote, a Herald C statistic of 0 .73. 00:27:00.009 --> 00:27:03.009 Herald C, similar to AUC. Yes, it's a similar 00:27:03.009 --> 00:27:06.099 measure of discrimination. A value of 0 .73 indicates 00:27:06.099 --> 00:27:08.740 the model has a reasonably good ability to discriminate 00:27:08.740 --> 00:27:10.900 between individuals who will and won't develop 00:27:10.900 --> 00:27:13.799 psychosis. No, obviously not perfect. Okay, and 00:27:13.799 --> 00:27:16.519 was it actually used in practice? Yes, an important 00:27:16.519 --> 00:27:18.539 feasibility study integrated this calculator 00:27:18.539 --> 00:27:22.210 directly into the SLAM EHR system. It automatically 00:27:22.210 --> 00:27:25.309 screened over 3 ,700 individuals receiving care, 00:27:25.869 --> 00:27:28.430 and it flagged 115 who were identified as being 00:27:28.430 --> 00:27:30.349 at elevated risk, according to the model. And 00:27:30.349 --> 00:27:32.769 did clinicians act on those flags? Crucially, 00:27:32.990 --> 00:27:35.509 yes. The study tracked the clinical response. 00:27:35.990 --> 00:27:38.430 77 % of clinicians whose patients received an 00:27:38.430 --> 00:27:41.670 alert responded to it in some way. And 55 % of 00:27:41.670 --> 00:27:43.910 those responses resulted in a referral for a 00:27:43.910 --> 00:27:46.069 more refined in -person psychosis assessment. 00:27:46.220 --> 00:27:48.920 And did those people actually develop psychosis? 00:27:49.099 --> 00:27:51.220 Well, the incidence of psychosis within six months 00:27:51.220 --> 00:27:53.180 for the individuals who were detected by the 00:27:53.180 --> 00:27:55.619 calculator and then subsequently assessed was 00:27:55.619 --> 00:27:58.250 12 percent. which is actually comparable to the 00:27:58.250 --> 00:28:00.710 incidents seen in traditional clinically -defined 00:28:00.710 --> 00:28:03.069 high -risk groups identified through other means. 00:28:03.109 --> 00:28:06.930 Wow. So this outcome strongly supports the calculator's 00:28:06.930 --> 00:28:08.849 potential for systematic detection at scale. 00:28:08.970 --> 00:28:11.509 That is genuinely fascinating, taking routine 00:28:11.509 --> 00:28:14.329 clinical data and using it proactively to identify 00:28:14.329 --> 00:28:16.990 risk at a population level within a health service. 00:28:17.509 --> 00:28:20.529 Amazing. The sources also briefly touch on how 00:28:20.529 --> 00:28:23.509 data is providing insights into other human behaviors, 00:28:23.970 --> 00:28:26.500 mentioning gaming and dating apps. What do they 00:28:26.500 --> 00:28:28.720 highlight there? They do touch on these, yes, 00:28:29.000 --> 00:28:31.420 perhaps more as examples of how data can't illuminate 00:28:31.420 --> 00:28:33.819 behavior outside traditional clinical settings. 00:28:34.420 --> 00:28:36.500 They note that gaming deporter is now recognized 00:28:36.500 --> 00:28:39.099 in the ICD -11 classification. The International 00:28:39.099 --> 00:28:42.059 Classification of Diseases. Correct. And it's 00:28:42.059 --> 00:28:44.019 associated with other conditions like depression, 00:28:44.380 --> 00:28:48.759 anxiety, ADHD, and also comorbidities, with things 00:28:48.759 --> 00:28:51.579 like excessive social media use, gambling, and 00:28:51.579 --> 00:28:55.059 substance use. Data from gaming platforms themselves 00:28:55.059 --> 00:28:57.819 can potentially reveal patterns of behavior relevant 00:28:57.819 --> 00:29:01.079 to diagnosis and intervention. Hmm. They mentioned 00:29:01.079 --> 00:29:03.319 data showing a higher prevalence of gaming disorder 00:29:03.319 --> 00:29:06.059 in males, potentially linked to industry strategy 00:29:06.059 --> 00:29:08.599 or perhaps achievement drives, but they also 00:29:08.599 --> 00:29:11.400 acknowledge rising female participation and the 00:29:11.400 --> 00:29:13.680 potential for bias in the research itself. Right, 00:29:13.759 --> 00:29:16.240 and dating apps. Dating apps are also mentioned, 00:29:16.640 --> 00:29:18.500 highlighting their surprisingly long history. 00:29:18.599 --> 00:29:20.559 Apparently computer matched parties existed as 00:29:20.559 --> 00:29:24.539 far back as 1959. Really? 1959. So the sources 00:29:24.539 --> 00:29:26.619 say, and of course their massive growth now, 00:29:27.019 --> 00:29:29.019 studies using data from these apps or surveys 00:29:29.039 --> 00:29:31.660 of users provide insights into user characteristics, 00:29:32.099 --> 00:29:34.400 suggesting users are more likely to be men, young 00:29:34.400 --> 00:29:37.160 adults, and sexual minorities, and also into 00:29:37.160 --> 00:29:39.359 personality traits, with studies using models 00:29:39.359 --> 00:29:41.880 like the Big Five indicating higher open -mindedness 00:29:41.880 --> 00:29:44.559 and extraversion, maybe lower conscientiousness, 00:29:44.880 --> 00:29:47.619 and high sensation -seeking. These points just 00:29:47.619 --> 00:29:50.039 illustrate how our digital footprints across 00:29:50.039 --> 00:29:52.680 various platforms can become sources of data 00:29:52.680 --> 00:29:55.220 for understanding behavior, sometimes behavior 00:29:55.220 --> 00:29:57.839 linked to mental health. It's clear the potential 00:29:57.839 --> 00:30:00.839 of data and AI is immense across so many aspects 00:30:00.839 --> 00:30:03.720 of life and health. But these technologies also 00:30:03.720 --> 00:30:05.960 bring significant challenges, don't they? The 00:30:05.960 --> 00:30:09.480 sources explicitly acknowledge this. What are 00:30:09.480 --> 00:30:11.859 the main ethical issues and privacy concerns 00:30:11.859 --> 00:30:15.180 highlighted as critical hurdles? They are indeed 00:30:15.180 --> 00:30:17.859 presented as paramount concerns, particularly 00:30:17.859 --> 00:30:20.680 mental health, where the data involved is inherently 00:30:20.680 --> 00:30:23.619 so sensitive, so personal. Absolutely. patient 00:30:23.619 --> 00:30:26.039 and participant privacy is stressed as absolutely 00:30:26.039 --> 00:30:28.500 critical. There's significant worry raise over 00:30:28.500 --> 00:30:31.619 the potential misuse of this personal data. Examples 00:30:31.619 --> 00:30:33.720 given include companies potentially branding 00:30:33.720 --> 00:30:36.400 or targeting products specifically at vulnerable 00:30:36.400 --> 00:30:39.000 populations based on their digital data. Exploiting 00:30:39.000 --> 00:30:42.640 vulnerability. Potentially yes. Or the risk of 00:30:42.640 --> 00:30:45.539 data being traded to third parties without explicit 00:30:45.539 --> 00:30:49.029 informed consent. The sources cite some worrying 00:30:49.029 --> 00:30:51.150 findings that many mental health apps currently 00:30:51.150 --> 00:30:53.730 on the market simply don't adhere to clinical 00:30:53.730 --> 00:30:56.769 or ethical guidelines. And their privacy policies 00:30:56.769 --> 00:30:59.930 are often either absent entirely or deeply flawed, 00:31:00.490 --> 00:31:03.150 failing to adequately protect user data. That's 00:31:03.150 --> 00:31:06.339 quite concerning. It is, and this lack of standardization 00:31:06.339 --> 00:31:08.700 and regulation, particularly in contexts like 00:31:08.700 --> 00:31:11.440 the USA, which is mentioned specifically, contributes 00:31:11.440 --> 00:31:13.660 significantly to a lack of public confidence. 00:31:14.279 --> 00:31:16.480 The forces include a figure suggesting only about 00:31:16.480 --> 00:31:18.900 8 % of users are willing to share their health 00:31:18.900 --> 00:31:22.170 data with technology companies. Only 8%. That 00:31:22.170 --> 00:31:24.509 really underscores the depth of this trust issue, 00:31:24.690 --> 00:31:26.670 doesn't it? It certainly does. It's a major barrier. 00:31:26.930 --> 00:31:29.809 And with literally thousands of apps out there, 00:31:29.990 --> 00:31:32.289 how are clinicians or even patients themselves 00:31:32.289 --> 00:31:34.150 supposed to know which ones are safe and effective? 00:31:34.650 --> 00:31:36.630 That's another significant challenge the sources 00:31:36.630 --> 00:31:38.849 highlight. The sheer number of available apps 00:31:38.849 --> 00:31:41.509 makes evaluation incredibly difficult, both for 00:31:41.509 --> 00:31:43.710 professionals and for consumers. An impossible 00:31:43.710 --> 00:31:47.750 task, almost. Almost. There's considerable variation 00:31:47.750 --> 00:31:50.529 in the evaluation frameworks that do exist, which 00:31:50.529 --> 00:31:54.049 makes comparing apps tricky. The sources explicitly 00:31:54.049 --> 00:31:56.769 call for the development of standardized, robust 00:31:56.769 --> 00:32:00.069 quality measures and clear guidance for clinicians 00:32:00.069 --> 00:32:02.569 on how to evaluate and potentially recommend 00:32:02.569 --> 00:32:05.470 apps. They make it very clear that simply relying 00:32:05.470 --> 00:32:08.509 on ratings or descriptions in app stores is nowhere 00:32:08.509 --> 00:32:10.710 near sufficient for ensuring quality and safety. 00:32:10.839 --> 00:32:13.779 Okay. Beyond just evaluating the apps themselves, 00:32:14.380 --> 00:32:17.619 how do these new data -driven technologies impact 00:32:17.619 --> 00:32:20.039 the clinician's actual workflow, their daily 00:32:20.039 --> 00:32:22.759 practice? It must require a fundamental shift 00:32:22.759 --> 00:32:25.470 in how they work. It absolutely does. The sources 00:32:25.470 --> 00:32:27.490 really stress that clinicians need to fundamentally 00:32:27.490 --> 00:32:30.230 alter their way of working to effectively and 00:32:30.230 --> 00:32:32.309 ethically incorporate these advanced data -driven 00:32:32.309 --> 00:32:34.490 technologies. It's not just learning new software, 00:32:34.670 --> 00:32:37.490 then? No, not at all. It requires them to engage 00:32:37.490 --> 00:32:39.490 directly with technologists, with engineers, 00:32:40.049 --> 00:32:42.190 to truly understand how these tools function, 00:32:42.349 --> 00:32:44.269 what their limitations are, and importantly to 00:32:44.269 --> 00:32:46.509 ensure their use doesn't compromise clinical 00:32:46.509 --> 00:32:49.150 or ethical duties. Right, bridging that gap. 00:32:49.450 --> 00:32:51.970 Precisely. The sources also mentioned the need 00:32:51.970 --> 00:32:54.470 for developing new competencies for health professionals 00:32:54.470 --> 00:32:57.069 in using other emergent technologies like augmented 00:32:57.069 --> 00:33:00.529 reality, simulation, gamification, which are 00:33:00.529 --> 00:33:02.710 also finding applications in healthcare training 00:33:02.710 --> 00:33:06.309 and delivery. Integrating apps into existing 00:33:06.309 --> 00:33:08.970 clinical workflows, providing staff training 00:33:08.970 --> 00:33:11.609 on how to use them and interpret the data, deciding 00:33:11.609 --> 00:33:13.769 on the appropriate level of clinical oversight 00:33:13.769 --> 00:33:16.670 for patient app use, handling the complexities 00:33:16.670 --> 00:33:19.779 of data sharing and interpretation. These are 00:33:19.779 --> 00:33:22.259 all presented as significant practical challenges. 00:33:22.440 --> 00:33:24.619 Yeah, I can see that. And the move towards these 00:33:24.619 --> 00:33:27.079 potential real -time assessment models means 00:33:27.079 --> 00:33:29.599 clinicians will need to actively consider not 00:33:29.599 --> 00:33:32.319 just whether an app exists, but if the data it 00:33:32.319 --> 00:33:35.039 provides is genuinely valuable to clinical decision 00:33:35.039 --> 00:33:37.420 making. And if it actually helps demonstrate 00:33:37.420 --> 00:33:39.640 real progress for the patient, is it useful? 00:33:39.880 --> 00:33:42.200 It sounds like navigating all this requires a 00:33:42.200 --> 00:33:44.440 lot of cross -disciplinary understanding, a lot 00:33:44.440 --> 00:33:46.559 of collaboration. It certainly does. Which brings 00:33:46.559 --> 00:33:50.000 us back to the human element. How do clinicians, 00:33:50.200 --> 00:33:53.400 patients, and even these AI systems like chatbots 00:33:53.400 --> 00:33:56.119 actually interact effectively and safely according 00:33:56.119 --> 00:33:58.660 to these sources? Collaboration and communication 00:33:58.660 --> 00:34:01.839 are highlighted as absolutely essential for successful 00:34:01.839 --> 00:34:05.099 adoption. Introducing advanced technologies into 00:34:05.099 --> 00:34:07.849 healthcare requires a, well... The concerted 00:34:07.849 --> 00:34:09.670 effort from all parties involved clinicians, 00:34:10.110 --> 00:34:12.750 patients, technologists to understand one another's 00:34:12.750 --> 00:34:15.869 perspectives, needs, capabilities, and to co 00:34:15.869 --> 00:34:18.809 -create innovations that are acceptable and genuinely 00:34:18.809 --> 00:34:21.710 beneficial to everyone. Co -creation again? Yes. 00:34:22.329 --> 00:34:24.409 Clinicians, as we've said, need to engage with 00:34:24.409 --> 00:34:26.590 technologists to ensure tools are clinically 00:34:26.590 --> 00:34:29.369 appropriate and ethically sound. Patients and 00:34:29.369 --> 00:34:31.469 clinicians should also engage with data scientists 00:34:31.469 --> 00:34:33.750 to understand how advanced data -driven technologies 00:34:33.750 --> 00:34:35.840 work. allowing them to make informed decisions 00:34:35.840 --> 00:34:38.000 about their use and interpretation. Is there 00:34:38.000 --> 00:34:40.139 willingness for that engagement? The sources 00:34:40.139 --> 00:34:42.639 note that there is empirical evidence indicating 00:34:42.639 --> 00:34:45.599 a general willingness among these groups to engage, 00:34:45.820 --> 00:34:48.539 which needs to be actively encouraged, fostering 00:34:48.539 --> 00:34:50.920 a sense of shared responsibility in how these 00:34:50.920 --> 00:34:53.059 technologies are developed and deployed in practice. 00:34:53.320 --> 00:34:56.159 And specifically with chatbots, where the interaction 00:34:56.159 --> 00:34:59.099 can feel almost human -like sometimes, are there 00:34:59.099 --> 00:35:01.900 unique risks or relational considerations, the 00:35:01.900 --> 00:35:04.929 sources point out? Yes, the sources do raise 00:35:04.929 --> 00:35:07.329 specific concerns regarding the relational aspects 00:35:07.329 --> 00:35:09.630 of interacting with chatbots, particularly in 00:35:09.630 --> 00:35:12.070 sensitive areas like mental health. How so? Well, 00:35:12.110 --> 00:35:14.210 while chatbots interact using natural language, 00:35:14.710 --> 00:35:16.570 internally they are processing text strings. 00:35:17.210 --> 00:35:20.230 They may potentially miss nonverbal cues or nuances 00:35:20.230 --> 00:35:22.909 like, say, urgency conveyed in spoken language. 00:35:23.090 --> 00:35:25.789 Right, the tone. Exactly. Concerns are noted 00:35:25.789 --> 00:35:28.090 regarding the potential for users to form attachments 00:35:28.090 --> 00:35:30.840 to bots. perhaps alluding to codependency, or 00:35:30.840 --> 00:35:33.820 for users to be overly credulous, to unquestioning 00:35:33.820 --> 00:35:36.119 of the bot's responses, especially given their 00:35:36.119 --> 00:35:39.599 often human -like features and that 247 availability. 00:35:39.760 --> 00:35:43.300 Hmm, blurring lines. It can. And this creates 00:35:43.300 --> 00:35:46.420 a difficult dilemma. While human validation and 00:35:46.420 --> 00:35:49.159 monitoring of chatbot responses might be needed 00:35:49.159 --> 00:35:51.440 for safety, particularly given the potential 00:35:51.440 --> 00:35:55.159 for unexpected or even harmful outputs. This 00:35:55.159 --> 00:35:57.420 oversight could simultaneously jeopardize the 00:35:57.420 --> 00:35:59.760 patient's privacy in their communication with 00:35:59.760 --> 00:36:02.360 the chat bot. A real balancing act? It highlights 00:36:02.360 --> 00:36:05.079 the delicate balance needed between safety, effectiveness, 00:36:05.320 --> 00:36:07.739 and privacy in these kinds of interactions. So 00:36:07.739 --> 00:36:09.900 despite all the incredible technology, all the 00:36:09.900 --> 00:36:11.840 data, the importance of the human connection 00:36:11.840 --> 00:36:14.599 and care remains absolutely central. Absolutely. 00:36:14.860 --> 00:36:17.670 The sources reinforce this point strongly. The 00:36:17.670 --> 00:36:19.769 fundamental value in therapy sessions, whether 00:36:19.769 --> 00:36:22.889 conducted online via telepsychiatry or in person, 00:36:23.429 --> 00:36:25.690 is the therapist focused attention on the patient. 00:36:25.849 --> 00:36:28.489 The relationship. The relationship. The technology, 00:36:28.610 --> 00:36:31.010 whether it's real -time monitoring data curated 00:36:31.010 --> 00:36:33.690 by a professional or communication tools, should 00:36:33.690 --> 00:36:36.190 ideally enable and enhance this therapeutic focus 00:36:36.190 --> 00:36:38.730 in the human relationship, not aim to replace 00:36:38.730 --> 00:36:41.289 that essential connection entirely. Right. Augment, 00:36:41.449 --> 00:36:44.659 not replace. Precisely. They mentioned the need 00:36:44.659 --> 00:36:46.920 for investment and leadership to develop digital 00:36:46.920 --> 00:36:49.659 clinician pathways, which would involve training 00:36:49.659 --> 00:36:51.820 and infrastructure to support clinicians in leveraging 00:36:51.820 --> 00:36:54.500 these tools effectively while always maintaining 00:36:54.500 --> 00:36:56.880 the centrality of the patient relationship. This 00:36:56.880 --> 00:37:00.059 has been a really detailed and comprehensive 00:37:00.059 --> 00:37:03.659 exploration of a rapidly evolving field. To wrap 00:37:03.659 --> 00:37:05.519 things up, let's do a quick lightning round, 00:37:06.000 --> 00:37:08.139 drawing on some specific details from the sources 00:37:08.139 --> 00:37:10.519 we've discussed. Just quick, sharp answers based 00:37:10.519 --> 00:37:13.590 on the material, if you can. One digital tool 00:37:13.590 --> 00:37:15.590 mentioned that allowed for basic medical exams 00:37:15.590 --> 00:37:17.929 at home, perhaps during a lockdown scenario. 00:37:18.710 --> 00:37:20.809 Tidal Home Remote Exam Kits were mentioned. Okay. 00:37:21.489 --> 00:37:23.690 Beyond just helping with diagnosis, how else 00:37:23.690 --> 00:37:26.210 can digital phenotyping provide value in areas 00:37:26.210 --> 00:37:28.730 like mood disorders? Give me a couple of ways. 00:37:28.809 --> 00:37:30.869 Right. You can assist physicians by collecting 00:37:30.869 --> 00:37:33.889 signs continuously. Process those signs into 00:37:33.889 --> 00:37:36.889 mood and behavior patterns. Compare current data 00:37:36.889 --> 00:37:39.909 to a patient's baseline. Enhance detection of 00:37:39.909 --> 00:37:43.409 initial signs. tailor interventions, even suggest 00:37:43.409 --> 00:37:46.389 context -aware coping strategies, lots of potential 00:37:46.389 --> 00:37:49.929 applications. Okay. After the big surge during 00:37:49.929 --> 00:37:52.429 the pandemic, what's a key challenge mentioned 00:37:52.429 --> 00:37:55.869 for widespread equitable telepsychiatry access 00:37:55.869 --> 00:37:58.309 going forward? Access to the necessary technology 00:37:58.309 --> 00:38:00.889 and adequate training for professionals to use 00:38:00.889 --> 00:38:03.769 it effectively. Those two are key. Right. And 00:38:03.769 --> 00:38:06.309 finally, one core skill needed for senior leadership 00:38:06.309 --> 00:38:09.989 roles in health tech, like a chief clinical information 00:38:09.989 --> 00:38:14.969 officer or CCIO, something that goes beyond just 00:38:14.969 --> 00:38:18.329 being technically savvy, being good with computers. 00:38:18.449 --> 00:38:20.849 Change management, definitely. Understanding 00:38:20.849 --> 00:38:23.710 human factors, a deep understanding of systems 00:38:23.710 --> 00:38:27.170 and organizations, and critically, being good 00:38:27.170 --> 00:38:30.010 with people, not just computers. Brilliant. Thank 00:38:30.010 --> 00:38:32.289 you. Now, let's try and distill some actionable 00:38:32.289 --> 00:38:34.929 takeaways from this deep dive. Key points for 00:38:34.929 --> 00:38:36.869 you, the professional listener, to consider based 00:38:36.869 --> 00:38:39.010 only on the source material we've covered. Okay. 00:38:39.389 --> 00:38:41.309 Based on our discussion, here are perhaps some 00:38:41.309 --> 00:38:43.469 key considerations drawn directly from the sources. 00:38:43.630 --> 00:38:45.769 Okay, first. Firstly, digital transformation 00:38:45.769 --> 00:38:47.829 in healthcare isn't just about adding some new 00:38:47.829 --> 00:38:50.590 tools. It really necessitates a fundamental reform 00:38:50.590 --> 00:38:52.989 of health systems. Right. This requires properly 00:38:52.989 --> 00:38:56.139 integrating care across different levels. hospitals, 00:38:56.500 --> 00:38:59.719 primary care, home care, and significantly investing 00:38:59.719 --> 00:39:02.519 in developing new competencies for health professionals 00:39:02.519 --> 00:39:05.159 so they can effectively and ethically use these 00:39:05.159 --> 00:39:07.340 emerging technologies. Okay. Fundamental reform, 00:39:07.619 --> 00:39:10.800 new skills. What's next? Secondly, digital mental 00:39:10.800 --> 00:39:13.639 health tools covering things like digital phenotyping, 00:39:13.960 --> 00:39:17.460 mobile apps. tell psychiatry, chatbots, they 00:39:17.460 --> 00:39:20.239 hold substantial potential for expanding access 00:39:20.239 --> 00:39:23.380 to care and enabling truly personalized interventions. 00:39:23.539 --> 00:39:26.719 Nice. But their successful implementation really 00:39:26.719 --> 00:39:30.159 hinges on rigorous validation, very careful ethical 00:39:30.159 --> 00:39:32.519 consideration, and finding ways for seamless 00:39:32.519 --> 00:39:34.539 integration into existing clinical workflows 00:39:34.539 --> 00:39:37.039 and practices. It needs to work in the real world. 00:39:37.239 --> 00:39:39.480 Validation, ethics, integration. Got it. Third 00:39:39.480 --> 00:39:41.719 takeaway. Thirdly, leveraging the power of big 00:39:41.719 --> 00:39:44.409 data and AI, specifically machine learning, offers 00:39:44.409 --> 00:39:46.650 powerful capabilities for predicting individual 00:39:46.650 --> 00:39:49.269 risk trajectories, particularly in sensitive 00:39:49.269 --> 00:39:52.030 domains like suicide and psychosis risk prediction. 00:39:52.429 --> 00:39:56.090 The precision psychiatry idea. Exactly. But this 00:39:56.090 --> 00:39:58.570 power is absolutely contingent on having access 00:39:58.570 --> 00:40:02.329 to robust, high -quality data. It demands transparency 00:40:02.329 --> 00:40:04.849 in how the predictive models actually function. 00:40:05.079 --> 00:40:07.619 We need to understand them. And it requires a 00:40:07.619 --> 00:40:10.360 careful balancing act between the automated insights 00:40:10.360 --> 00:40:13.820 and essential human clinical judgment. Data quality, 00:40:14.159 --> 00:40:17.500 transparency, human oversight. Okay, fourth. 00:40:17.769 --> 00:40:20.570 Fourthly, the widespread adoption of health technology 00:40:20.570 --> 00:40:23.570 introduces critical responsibilities as highlighted 00:40:23.570 --> 00:40:25.789 quite strongly in the sources. There must be 00:40:25.789 --> 00:40:27.670 paramount attention paid to ensuring patient 00:40:27.670 --> 00:40:30.969 safety and data privacy. Non -negotiables. Absolutely. 00:40:31.329 --> 00:40:33.650 Establishing clear ethical guidelines for technology 00:40:33.650 --> 00:40:36.289 use and developing standardized evaluation frameworks 00:40:36.289 --> 00:40:38.610 and potentially even new regulatory approaches 00:40:38.610 --> 00:40:40.650 are needed to build and maintain public trust. 00:40:40.929 --> 00:40:43.429 Safety, privacy, trust through standards make 00:40:43.429 --> 00:40:46.849 sense. And finally. And finally, successful digital 00:40:46.849 --> 00:40:49.289 health adoption is perhaps fundamentally driven 00:40:49.289 --> 00:40:51.690 by human fangars and effective change management. 00:40:52.349 --> 00:40:54.550 It necessitates genuine collaboration and open 00:40:54.550 --> 00:40:56.849 communication across all disciplines, clinicians, 00:40:57.269 --> 00:40:59.250 technologists, researchers, patients themselves. 00:41:00.150 --> 00:41:03.050 Yes, to co -create innovation that truly works 00:41:03.050 --> 00:41:06.550 for everyone involved, moving beyond just a sole 00:41:06.550 --> 00:41:09.090 focus on the technology itself. It's about the 00:41:09.090 --> 00:41:11.719 people. and the process. Collaboration, human 00:41:11.719 --> 00:41:14.300 factors, change management, brilliant summary. 00:41:14.739 --> 00:41:16.400 So here's a final thought, perhaps something 00:41:16.400 --> 00:41:20.519 for you to mull over. As data and AI become increasingly 00:41:20.519 --> 00:41:23.800 central to health care, to prediction, particularly 00:41:23.800 --> 00:41:26.780 in these really sensitive areas like mental health, 00:41:27.480 --> 00:41:30.860 how do we ensure that the pursuit of efficiency, 00:41:31.039 --> 00:41:34.320 of precision, doesn't inadvertently erode that 00:41:34.320 --> 00:41:36.699 essential human relationship at the very heart 00:41:36.699 --> 00:41:39.309 of care? A critical question. It's a critical 00:41:39.309 --> 00:41:41.150 tension, isn't it? Something for us all to navigate 00:41:41.150 --> 00:41:43.949 carefully. If you found value in this deep dive 00:41:43.949 --> 00:41:45.969 today, please do rate and share the show. It 00:41:45.969 --> 00:41:48.469 genuinely helps more people discover how to quickly 00:41:48.469 --> 00:41:50.849 become well -informed on topics like this. Profi 00:41:50.849 --> 00:41:53.369 Mom, thank you once again for guiding us so expertly 00:41:53.369 --> 00:41:55.610 through this complex but absolutely vital material 00:41:55.610 --> 00:41:57.070 today. It was my pleasure. Thank you for having 00:41:57.070 --> 00:41:58.809 me. That's all for this deep dive.