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		<title>Data-Driven Digital Diabetes</title>
		<link>https://innohealthmagazine.com/2021/persona/guest-column/data-driven-digital-diabetes/</link>
					<comments>https://innohealthmagazine.com/2021/persona/guest-column/data-driven-digital-diabetes/#respond</comments>
		
		<dc:creator><![CDATA[InnoHEALTH magazine digital team]]></dc:creator>
		<pubDate>Wed, 16 Jun 2021 07:36:10 +0000</pubDate>
				<category><![CDATA[Guest Column]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Data visualisation]]></category>
		<category><![CDATA[Data-Driven Care]]></category>
		<category><![CDATA[Data-driven precision medicine]]></category>
		<category><![CDATA[Diabetes]]></category>
		<category><![CDATA[Diabetes Consultant]]></category>
		<category><![CDATA[Digital Diabetes]]></category>
		<category><![CDATA[Dr. Debbie Wake]]></category>
		<category><![CDATA[Global Digital Health]]></category>
		<category><![CDATA[Medication]]></category>
		<category><![CDATA[new sensor-based technology]]></category>
		<category><![CDATA[remote technology]]></category>
		<category><![CDATA[Scotland]]></category>
		<category><![CDATA[type 2 diabetes management]]></category>
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					<description><![CDATA[<p>The post <a href="https://innohealthmagazine.com/2021/persona/guest-column/data-driven-digital-diabetes/">Data-Driven Digital Diabetes</a> appeared first on <a href="https://innohealthmagazine.com">InnoHEALTH magazine</a>.</p>
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	<p><strong><em>Dr Debbie Wake is a diabetologist, Clinical Reader at University of Edinburgh and Diabetes Consultant, (MBChB, BSc, PhD, Clin Ed Dip), and CEO and co-founder of <a href="https://mywaydigitalhealth.co.uk/" target="_blank" rel="noopener">MyWay Digital Health</a> (MWDH). She leads national programmes on diabetes artificial intelligence/ international diabetes education programmes (Kuwait/ China). Previously, she was a health columnist for a national UK newspaper and TV doctor/ presenter for STVs ‘The Hour’ programme.</em></strong></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; color: #a5a5a5; font-size: 22px; line-height: 1.7;"><strong><em>&#8220;new sensor-based technology that measures glucose (interstitially), through flash and continuous glucose monitoring (CGM) is transforming the lives of people with type 1 diabetes.&#8221;</em></strong></h2>
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	<p><span style="font-weight: 400;">Diabetes now affects around 9-10% of the global population/costing ~£500bn per year as per the study of International Diabetes Federation (IDF)</span><span style="font-weight: 400;">,</span><span style="font-weight: 400;"> with huge associated morbidity and mortality. </span><span style="font-weight: 400;">As per the study of </span><span style="font-weight: 400;">Grand View Research Reports, </span><span style="font-weight: 400;">in</span><span style="font-weight: 400;"> parallel, the global digital health market is growing, and now totals over $206 billion, and by 2025 is expected to reach $509 billion. CAGR (compound annual growth rate) is 27.7%. Diabetes lends itself well to a digitally supported model of care delivery, and data-driven IT systems and digital applications may facilitate improved diabetes outcomes. </span></p>
<p><strong>Internet-based technology can </strong></p>
<ol>
<li><span style="font-weight: 400;">i) support people with diabetes directly, enabling better self-management (e.g. through internet linked apps supporting education, lifestyle or treatment titration)</span></li>
<li><span style="font-weight: 400;">ii) support health care professionals to deliver better care through electronic health records, decision support, remote consultation tools, and population analytics. </span></li>
</ol>
<p><span style="font-weight: 400;">In addition, new sensor-based technology that measures glucose (interstitially), through flash and continuous glucose monitoring (CGM) is transforming the lives of people with type 1 diabetes. Closed loop systems (i.e. continuous glucose monitors linked to insulin pumps, with dose adjustments driven by automated algorithms) are becoming the gold standard.  Advanced glucose sensing technology is also showing promise for some aspects of type 2 diabetes management, although price currently inhibits widespread use.</span></p>
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	<p><span style="font-weight: 400;">The current COVID-19 epidemic (and poorer COVID outcomes in people with diabetes) has further increased the need for digital solutions, particularly technology which supports remote care models and population triage suggested in some scientific literature by Nagi and his team. Scalable remote technology based patient education approaches such as massive online open courses (MOOCs) may be highly cost effective (</span><span style="font-weight: 400;">Mackenzie and team</span><span style="font-weight: 400;">). Systematic literature reviews, evaluating use of digital tools and apps in diabetes self-management more generally, have demonstrated improvements in clinical outcomes, but show significant variability between interventions. </span></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; font-size: 22px; line-height: 1.7;"><strong>Data-Driven Care </strong></h2>
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	<p><span style="font-weight: 400;">Gathering and exchanging data is key for any learning health system. Data in diabetes may come from electronic health records (e.g. demographics, clinic measurements,  lab data, medication, appointments,  past medical history etc.), screening programmes, and from patients directly through home devices such as activity monitors, glucose meters and increasingly through a plethora of home diagnostics and ‘internet of things’ sensors and devices. This may include foot pressure/ heat sensors to aid early detection of neuropathy, leading to foot ulcer prevention through to home urine testing linked to smartphone apps, to support detection of urine albumin. In addition, lifestyle and diet apps (including some with inbuilt food nutritional analysis are supporting day to day self-management decisions. Data may also come from questionnaires/ patient reported outcome/ experience measures (PROMs/PREMs),and other internet sources including social and environmental data.</span></p>
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	<p><span style="font-weight: 400;">The ability for a system to use data effectively to improve health care can be summarised by the informatics maturity model (table 1). The highest level (level 8) is associated with transformation of data and delivery to clinicians or patients through outputs which support a personalised precision medicine approach.</span></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; font-size: 22px; line-height: 1.7;"><strong>Precision Medicine /Decision Support</strong></h2>
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	<p><span style="font-weight: 400;">Using data to enable more personalised care, through data modelling and decision support could be transformative. A recent American Diabetes Association (ADA) /European Association for the Study of Diabetes (EASD) consensus report highlighted key areas where diabetes care is ready for precision diagnostics, therapeutics and prognostics, noting that &#8220;pragmatic studies of decision-support systems utilising rich information in health care systems&#8230; are needed&#8221;. Data-driven precision medicine in diabetes can support better diagnosis (including diabetes subtyping), more personalised prescribing (drug-response), and better prediction of short and long complications enabling early intervention. In addition, image-analysis AI is being used to support more rapid retinal image screening and for racking diabetic wound healing. </span></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; color: #a5a5a5; font-size: 22px; line-height: 1.7;"><strong><em>&#8220;To date, evidence for the use of Artificial Intelligence (AI)-driven decision support in clinical settings for chronic disease management remains limited. Data visualisation, or linked decision-support advice/ alerts can empower end users, i.e. turn data into knowledge and action. Clinical decision support (CDS) provides timely information, usually at the point of care, to help inform decisions about a patient&#8217;s care.&#8221;</em></strong></h2>
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	<p><span style="font-weight: 400;">To date, evidence for the use of Artificial Intelligence (AI)-driven decision support in clinical settings for chronic disease management remains limited. Data visualisation, or linked decision-support advice/ alerts can empower end users, i.e. turn data into knowledge and action. Clinical decision support (CDS) provides timely information, usually at the point of care, to help inform decisions about a patient&#8217;s care. A recent meta-analysis (BMJ) demonstrated that clinical decision support interventions in a more general setting achieve small to moderate improvements in targeted processes of care, with limited evidence to date demonstrating improved clinical outcomes. The paper calls for a human factors approach to understand workflows, patient-orientated support, and the use of AI to improve prediction, and preventative care decision support.</span></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; color: #a5a5a5; font-size: 22px; line-height: 1.7;"><strong><em>&#8220;Data is made available for clinical use, audit, research and patient self-management. The use of a national data platform in Scotland has been associated with significant improvements in care quality and outcomes.&#8221;</em></strong></h2>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; font-size: 22px; line-height: 1.7;"><strong>Case Study (Scotland)</strong></h2>
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	<p><span style="font-weight: 400;">Over the past 20 years, Scotland has taken a national approach to diabetes management underpinned by exchanging linked data. The national SCI-Diabetes platform which is accessible to all clinicians in Scotland who manage diabetes, exchanges data from all primary care clinics, national laboratory data and national screening programmes. Data exchange is possible through the use of a unique patient identifier [Community Health Index (CHI)]. Data is made available for clinical use, audit, research and patient self-management. The use of a national data platform in Scotland has been associated with significant improvements in care quality and outcomes.</span></p>
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	<p><i><span style="font-weight: 400;">Legend: Schemata of national diabetes data integration in Scotland coordinated through the central (SCI-diabetes) clinical IT platform.  </span></i></p>
<p><span style="font-weight: 400;">Rules-based decision support tools embedded in SCI-diabetes (the national clinical diabetes platform; https://www.sci-diabetes.scot.nhs.uk)) further delivered a 3-4x improved compliance with national medical guidelines.  </span></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; font-size: 22px; line-height: 1.7;"><strong>My Diabetes My Way (now MyWay Diabetes)</strong></h2>
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	<p><span style="font-weight: 400;">SCI-diabetes data is made available to patients through Scotland&#8217;s MyDiabetesMyWay (MDMW) patient data access and education platform. Around 500,000 people have used the MDMW website and &gt;60,000 registered for data access (since 2010). This platform also supports online structured education courses.  MDMW use has been associated with improvements in key parameters such as HbA1c and cost-savings with ~ 5:1 return on investment. More advanced AI-driven predictive analytics and linked decision support is currently being tested; supported by MyWay Digital Health.</span></p>
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	<h2 class="Body" style="text-align: justify; text-justify: inter-ideograph; color: #a5a5a5; font-size: 22px; line-height: 1.7;"><strong><em>&#8220;Population management systems can further focus care toward those in most need. Data-driven solutions have the potential to reduce mortality, morbidity, reduce complications, drive more effective treatment prescriptions, improve quality of life, improve patient safety, enable more effective diagnosis and prescribing and delivery system efficiencies.&#8221;</em></strong></h2>
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	<p><span style="font-weight: 400;">Digital technologies and data could have wide benefits for people with diabetes and other long-term conditions, enabling better personalised self-management, scaled through internet-based delivery. In addition, given the high prevalence of diabetes, most care is provided by generalists, who may lack specialist knowledge; accessible guideline-linked evidence-based decision support, could be a great enabler. Population management systems can further focus care toward those in most need. Data-driven solutions have the potential to reduce mortality, morbidity, reduce complications, drive more effective treatment prescriptions, improve quality of life, improve patient safety, enable more effective diagnosis and prescribing and delivery system efficiencies. Investment in underlying infrastructure and policies to support data standardisation, interoperability, information governance is essential to realise these benefits, and ongoing research is encouraged to better understand the impact. </span></p>
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<p>The post <a href="https://innohealthmagazine.com/2021/persona/guest-column/data-driven-digital-diabetes/">Data-Driven Digital Diabetes</a> appeared first on <a href="https://innohealthmagazine.com">InnoHEALTH magazine</a>.</p>
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		<title>HOW TO GET DIGITAL TRANSFORMATION RIGHT</title>
		<link>https://innohealthmagazine.com/2017/blog/how-to-get-digital-transformation-right/</link>
					<comments>https://innohealthmagazine.com/2017/blog/how-to-get-digital-transformation-right/#respond</comments>
		
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		<pubDate>Fri, 17 Feb 2017 12:30:54 +0000</pubDate>
				<category><![CDATA[Blog]]></category>
		<category><![CDATA[InnoHEALTH]]></category>
		<category><![CDATA[Magazine]]></category>
		<category><![CDATA[Digital Health Innovation]]></category>
		<category><![CDATA[Digital Transformation]]></category>
		<category><![CDATA[Global Digital Health]]></category>
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		<category><![CDATA[innovation]]></category>
		<category><![CDATA[Innovation Integration Curve]]></category>
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					<description><![CDATA[<p>Health leaders and executives globally are focused</p>
<p>The post <a href="https://innohealthmagazine.com/2017/blog/how-to-get-digital-transformation-right/">HOW TO GET DIGITAL TRANSFORMATION RIGHT</a> appeared first on <a href="https://innohealthmagazine.com">InnoHEALTH magazine</a>.</p>
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	<p><span style="color: #0073b4;"><strong>How to get digital transformation right</strong></span><br />
<strong><span style="color: #0073b4;">Fard Johnmar</span></strong> is a Digital Health Futurist, Researcher, Strategist and Founder of Enspektos.</p>
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	<p><strong>If you thought digital transformation was only about technology, read ahead to know that it is way much more. There are forces that shape innovation efforts in health sector and then there are barriers in the path ahead.</strong></p>
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	<p style="text-align: justify !important;">Health leaders and executives globally are focused on addressing a key strategic question: “How do we activate, cultivate, manage and accelerate digital innovation within our organizations?”</p>
<p style="text-align: justify !important;">There’s a right and a wrong way to address this question and successfully ignite digital transformation. Let’s start by taking a look at the wrong way.</p>
<p style="text-align: justify !important;"><span style="color: #0073b4;"><strong>Why the traditional technology-centric approach towards digital transformation is flawed ?</strong></span><br />
Customarily, health organizations have taken a very technology-centric approach to fostering digital transformation. Here is a simplified overview of how this strategy is usually applied:<br />
<strong>• Step I:</strong> Learn about a new technology trend or tool<br />
<strong>• Step II:</strong> Decide to implement pilot projects to test it<br />
<strong>• Step III:</strong> Exemplify it for how the organization is digitizing its operations and approach<br />
While simply testing various technologies is clearly an important first step for organizations seeking to further their digital transformation efforts, this approach has the following flaws.<br />
<strong>• It promotes silo building:</strong> In many cases, the digital health innovation work is disconnected from the larger organizations, promoting the creation of innovation silos that are distant from other parts of the organization, and thereby receiving less support in due course of time.<br />
<strong>• It is difficult to scale:</strong> Pilots are by their very definition, short-term experiments. Without careful consideration of how they fit into larger strategic and tactical objectives, they can be difficult for the organization to scale over time.<br />
<strong>• Lessons and best/worst practices are often not shared broadly:</strong> Because these pilots are often being led by small teams or innovation leaders, sometimes — because they are disconnected from the larger organization — lessons from these pilots are not being shared widely or used to tackle related businesses and organizational issues.<br />
<strong>• Leadership and budgetary support is limited:</strong> Leadership may be aware of these technology-focused projects, but view them as short-term “side projects”, disconnected from the larger strategic and tactical imperatives. This lack of leadership support translates into more limited budgets for the initial and ongoing digital innovation.<br />
<strong>• It is hard to prove return on investment (ROI):</strong> Without carefully thinking about how these innovations will either reduce expenses or drive revenue (via improvements in workflows, better health outcomes, etc.) it is often difficult to determine a positive ROI for these efforts.<br />
It is clear that the technology-centric approach may deliver short-term benefits in terms of improving understanding and the ability to implement certain digital innovations. However, it has significant limitations when viewed from the context of helping health organizations develop a more viable and sustainable digital transformation strategy.<br />
Fortunately, there is a better way!</p>
<p style="text-align: justify !important;"><span style="color: #0073b4;"><strong>A superior approach: Understand and manage the four fundamental forces shaping digital innovation at health organizations</strong></span><br />
For more than 10 years, I have worked with leading health organizations (including government, pharmaceutical firms and payers) on various digital transformation-related initiatives. And, late last year, I developed a unique research initiative called the State of Digital Health Innovation. This study is designed to identify the forces that are accelerating or slowing digital initiatives at health organizations. This experience and research clearly demonstrates that while technology plays an important role in fostering digital transformation, it is far from the only factor. In fact, as illustrated in the image below, there are four.</p>
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	<p>Health organizations that fail to consider, account for and manage the four fundamental forces shaping digital innovation will ultimately be less successful in their transformation efforts. These forces are:</p>
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<li><strong>Human:</strong> This involves:</li>
<li style="text-align: justify !important;">Ensuring that innovation initiatives — whether large or small — are supported by leadership. This does not just mean garnering support from the C-suite, but also ensuring that the team and functional leaders understand how these innovations can benefit them in due course of time.</li>
<li style="text-align: justify !important;">Creating opportunities for people throughout the organization to be educated about innovation — not only the ones of immediate relevance, but also other, potentially high-impact trends and technologies that are being developed almost daily. We found that a major drag on innovation is personnel who are incurious and uneducated about the evolving digital technology landscape. Going to events, and holding innovation days are not enough. Organizations must find ways to inject innovation into executives’ daily routines and workflows in easy and unobtrusive ways.</li>
<li style="text-align: justify !important;"><strong>Economic:</strong> This is about answering a fundamental question on innovations — even at the earliest stage. Can technology increase revenues or reduce health spending (via improved workflows, better health outcomes, etc.)? Failing to address this question early and often is another major barrier to successful digital transformation. If internal innovators can’t demonstrate the potential positive impact of these digital activities on the organization, they will be less likely to be funded or receive support from team, functional and organizational leadership.</li>
<li style="text-align: justify !important;"><strong>Technological:</strong> This is not just about ensuring that people within the organization have the ability to execute around one or more innovations. It is also about asking tough questions regarding whether the key stakeholders who will utilize these technologies, have the capacity to use them. There are many examples of organizations that have deployed digital solutions to patients, providers and other stakeholders; solutions that have failed because innovations were too difficult to use, people were unfamiliar with the technology and other factors. Digital transformation will be stalled if internal and external technology-related issues are not considered and addressed.</li>
<li style="text-align: justify !important;"><strong>Policy/regulatory:</strong> This area is one of the vital, but frequently overlooked, forces impacting digital transformation efforts. There are two sides to this issue:</li>
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<li style="text-align: justify !important;"><strong>Internal policies and procedures:</strong> For many organizations, well-crafted policies and procedures can be the lubricant of innovation progress. If effective policies exist, executives and leaders can move forward with a clear understanding of the guardrails that shape their decisions and processes. All-to-often policies are too restrictive, but if crafted well, they can also spur innovation because people can move forward with confidence and certainty.</li>
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<p style="text-align: justify !important;">If policies don’t exist, it will be much harder to scale innovations because people will be uncertain about what will be and will not be acceptable. For example, in the United States, the lack of clear policies around how social media technologies could be used by internal stakeholders, prevented many pharmaceutical companies from utilizing social tools more effectively and widely. A major reason for the lack of policy development was limited guidance from government agencies about what was acceptable from a social media implementation perspective.</p>
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<li style="text-align: justify !important;"><strong>Legal/regulatory clarity:</strong> Another major issue impacting digital transformation across organizations is the legal and regulatory landscape, specifically, whether government is developing innovation-friendly legislation and regulations. This means that even if organizations get everything right internally, external legal and regulatory forces can have a significant impact on their success.</li>
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<p style="text-align: justify !important;">One relevant example has been the slow uptake of telemedicine technologies in South Korea. Although innovators have developed unique solutions, government has been slow to test these tools and produce legislation enabling physicians to examine patients and prescribe medications via remote technologies such as mobile and video. Failing to become educated about the shape, scope and future of the legal and regulatory landscape can doom digital acceleration efforts.</p>
<p style="text-align: justify !important;"><span style="color: #0073b4;"><strong>What is the status of health organizations in their efforts towards digital transformation and what are the common barriers to success?</strong></span></p>
<p style="text-align: justify !important;">It can be helpful for health organizations seeking acceleration of digital transformation to have a sense of how their national and international peers are doing. In addition, it can be beneficial for them to have a clear communication during the overall digital innovation process and for quickly identifying (and addressing) areas of strengths and weaknesses.</p>
<p style="text-align: justify !important;">To help, I developed and released an industry-first digital maturity framework called the Digital Health Innovation Acceleration Curve (displayed below) in mid-2015.</p>
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	<p style="text-align: justify !important;">This framework organizes organizational innovation activity into three stages:<br />
<strong>• Stage I &#8211; Awareness and education:</strong> Becoming aware of and informed about new digital innovations<br />
<strong>• Stage II – Engagement and capacity boosting:</strong> Engaging in pilot projects and other implementations that help build knowledge and understanding<br />
<strong>• Stage III – Proficiency and scaling:</strong> Having the knowledge, ability and focus to scale innovations across the enterprise, use case studies, patients’ groups, therapeutic categories and more</p>
<p style="text-align: justify !important;">This innovation maturity model is at the center of our State of Digital Health Innovation research initiative. Study participants from health organizations (and their partners, e.g., startups, technology firms, consultants) rated organizational readiness and ability in each of the four areas (economics, human, policy, technological) discussed previously. Scores in each of these areas were calculated and aggregated to determine organizations’ overall position on the Digital Health Innovation Integration Curve and provide important information about their strengths and weaknesses.</p>
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	<p style="text-align: justify !important;">Overall, (as illustrated above) Wave I of this study reveals that only 5% of organizations globally are at Stage III, i.e., operating at the highest level of proficiency and benefitting from a digital innovation perspective. (State of Digital Health Innovation study)1</p>
<p style="text-align: justify !important;"><span style="color: #0073b4;"><strong>What is holding back digital innovation and transformation efforts?</strong></span><br />
We found that:<br />
<strong>•</strong> Among Stage I organizations: Limited budgets, lack of technological ability and low levels of innovation education and knowledge among executives and others are major reasons behind organizations not being at Stage II</p>
<p style="text-align: justify !important;"><strong>•</strong> Among Stage II organizations: Digital transformation efforts are hampered by limited budgets, lack of internal policies guiding innovation deployment and, in some technology areas (particularly wearable), limited technical ability and knowledge</p>
<p style="text-align: justify !important;"><span style="color: #0073b4;"><strong>The road ahead: Towards a more holistic digital transformation approach</strong></span></p>
<p style="text-align: justify !important;">Leaders and executives at health organizations struggling to ignite innovation and guide digital transformation have long understood the problem, but have had limited tools they could use to treat it successfully.<br />
What the State of Digital Health Innovation research initiative provides is a means they can use to quickly assess (by participating in the research), identify and remove roadblocks to digital transformation progress.<br />
It may be difficult to change organizational culture to move towards strategies that favor holistic rather than technology-centric digital transformation approach. However doing so is vital if we are to explore new opportunities to realize the promise of digital revolution for physicians, patients, caregivers, institutions and others in the health sector.</p>
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<p>The post <a href="https://innohealthmagazine.com/2017/blog/how-to-get-digital-transformation-right/">HOW TO GET DIGITAL TRANSFORMATION RIGHT</a> appeared first on <a href="https://innohealthmagazine.com">InnoHEALTH magazine</a>.</p>
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