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<h2 class="hd hd-2 unit-title">Challenges in Clinical Decision Making</h2>
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<h4>Introduction</h4>
<p>Humans are excellent at certain tasks. Humans are notably superior to computers in making strategic decisions, recognizing patterns, planning, noticing unusual events, doing procedures, providing empathy. At the same time, humans make mistakes, and we make them in repeatable patterns. We miss details, we forget individual facts, we cannot always recall the most significant pieces of information at the time they are needed. In healthcare, this human failing leads to errors of commission, such as incorrect dosing, and of omission, such as forgetting to provide timely drug therapy or vaccination. It also leads to inefficiencies, such as redoing tests more frequently than necessary. These errors can lead to adverse events and suboptimal outcomes. We know, for example, that fewer than 55% of all patients in the best medical centers get all of the available recommended preventive care management tests and vaccinations. We also know that hundreds of thousands of hospitalized patients die each year from adverse events, many of which are preventable.</p>
<p>Clinical decision support (CDS) can help prevent these human errors, and can facilitate the consistent use of best clinical practices. Clinical decision support is a process for ensuring that health-related decisions and actions are informed by pertinent patient information and clinical knowledge to enhance health and healthcare delivery. CDS entails providing clinicians and patients with a combination of clinical knowledge and patient data, intelligently filtered or presented at appropriate times, to help make the best decision at a given point in time for a given patient.</p>
<p>To get a sense of this concept, think of the GPS routing apps found on many smartphones or installed in motor vehicles. Behind the scenes, these apps combine an impressive collection of technology: a satellite-derived fix on your current location (data), a map of every street in the area, and a routing algorithm to find the best route between any two points (domain knowledge). But to the user, the result of all this is filtered and presented in the clearest way: a picture of a street, an arrow, a voice saying “turn right”. The GPS provides you with the precise information you need, in the most actionable format. Well-designed and implemented CDS provides much the same service, helping clinicians and patients decide which way to turn at any step in their management of a medical issue.</p>
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<p>Leo Anthony Celi MD MS MPH<br />Beth Israel Deaconess Medical Center<br />MIT Institute for Medical Engineering and Science</p>
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<h3 class="hd hd-2">Current State of Clinical Decision Making</h3>
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<h3 class="hd hd-2">Rationale for Data Analytics for Decision Support</h3>
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<h2 class="hd hd-2 unit-title">Key Elements and Types of Clinical Decision Support</h2>
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<h4>Key Elements</h4>
<p>The figure below is a general diagram describing the different stages of workflow and information used in any clinical patient encounter, from initial intake and assessment through diagnosis and treatment all the way to discharge and patient self-care at home. Each step has its own particular information needs and each lends itself to particular kinds of clinical decision support. For example, in step C, when forming the overall plan of care, interactive reference can present evidence-based guidelines, outlining the specific diagnostic and therapeutic actions the clinician should take and the rationale behind those choices. When test results come back on step H, alerts and prompts can notify the clinician of abnormal or concerning results. Toward discharge, patient education and patient self-care guidance tools can be the most valuable.</p>
<p><img src="/assets/courseware/v1/aeaeddbb4ca5271fd82ce17ade3e2c3a/asset-v1:MITx+HST.936x+1T2019+type@asset+block/Figure_26.4.png" alt="Stages of workflow and information use in a clinical encounter" type="saveimage" target="[object Object]" preventdefault="function (){r.isDefaultPrevented=n}" stoppropagation="function (){r.isPropagationStopped=n}" stopimmediatepropagation="function (){r.isImmediatePropagationStopped=n}" isdefaultprevented="function t(){return!1}" ispropagationstopped="function t(){return!1}" isimmediatepropagationstopped="function t(){return!1}" width="874" height="419" /></p>
<p>Regardless of the type of CDS used, there are common design elements that need to be designed properly to ensure ease of implementation, acceptance by clinicians, and effectiveness in improving quality and safety (see figure below). Any type of intervention starts with a trigger -- the initial event, action or passage of time that suggests that the logic of a particular CDS intervention should be checked. A trigger could be a laboratory test result prompting the computer to check for an abnormal-lab alert; a new diagnosis triggering a potential order set; an admission event prompting review of the patient’s vaccination status; a discharge event triggering automatic suggestions for patient self-care materials, or the passage of time suggesting that a check should be run for missed drug doses.</p>
<p>Once triggered, the intervention logic goes into action to interpret the available data. The logic could simply determine that no new information needs to be presented at this time (e.g., if the lab test is abnormal but actually improving, the required dose has been given, or no new orders are needed). If the logic determines that there are alerts, suggested orders, or other information important enough to present to the clinician and/or patient, then the CDS system presents that information in one of the ten forms described above. At that point the clinician may select one or more actions. Actions typically include new orders, changing or stopping orders, viewing new information, posting a fact to the medical record, or, importantly, posting an exception, i.e., a reason why the clinician chooses not to accept this particular CDS suggestion.</p>
<p>As a routine matter, a record should be made of all CDS interventions considered and presented and the clinical actions taken in response. This generates a database that is invaluable for short-term and long-term CDS evaluation studies, which will lead to much more precise and effective CDS in the future. Such a database is also an important tool for understanding patterns of illness progression and comparing effectiveness of different treatment approaches.</p>
<p><img src="/assets/courseware/v1/8d6b31bc0b477ccb88a4a74cfa24daa5/asset-v1:MITx+HST.936x+1T2019+type@asset+block/Figure_26.5.png" alt="Core components of a CDS intervention" type="saveimage" target="[object Object]" preventdefault="function (){r.isDefaultPrevented=n}" stoppropagation="function (){r.isPropagationStopped=n}" stopimmediatepropagation="function (){r.isImmediatePropagationStopped=n}" isdefaultprevented="function t(){return!1}" ispropagationstopped="function t(){return!1}" isimmediatepropagationstopped="function t(){return!1}" width="847" height="455" /></p>
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<h4>Alerts</h4>
<p>One of the most recognizable CDS interventions is an allergy warning, a member of the alert CDS type. In the screenshot below, the clinician has started to enter an order for the antibiotic cefuroxime. The patient is allergic to penicillins, which can also indicate an allergy to the related cephalosporin drug class that includes cefuroxime. The clinician forgot or didn’t know this about the patient, but the computer has kept track because that allergy was previously entered into the record. As soon as the drug name is entered, an alert screen appears, declaring that this order may be hazardous because of the patient’s possible allergy. Supplemental decision-making information is provided to the clinician, specifically that the patient develops hives when exposed to penicillins, suggesting a more serious allergy. Importantly, at the bottom of the window the clinician can immediately make the decision and act on it: to cancel the order because of concerns about the allergy, or to override the recommendation, perhaps because the clinician knows that this patient has tolerated this medication in the past. Providing clear and precise information and allowing the resulting decision to be executed right away are two features associated with successful, well-accepted CDS.</p>
<p>Alerts are an important and well-recognized form of CDS intervention, but must be handled with care. In fact, an increasing body of evidence suggests that alerts are often overused, and that they disrupt the clinician's workflow too frequently (alert fatigue). As a result, clinicians may try to subvert the alerting process, learning quickly what buttons they have to press to make an alert go away rather than paying close attention to its clinical meaning.</p>
<p><img src="/assets/courseware/v1/8fedd592e84bc36f41d438e2a3513646/asset-v1:MITx+HST.936x+1T2019+type@asset+block/Screen_Shot_2016-08-29_at_11.26.37_AM.png" alt="Example of an Alert" type="saveimage" target="[object Object]" preventdefault="function (){r.isDefaultPrevented=n}" stoppropagation="function (){r.isPropagationStopped=n}" stopimmediatepropagation="function (){r.isImmediatePropagationStopped=n}" isdefaultprevented="function t(){return!1}" ispropagationstopped="function t(){return!1}" isimmediatepropagationstopped="function t(){return!1}" width="950" height="488" /></p>
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<h4>Order Set</h4>
<p>An order set is a collection of prewritten orders suitable for a given clinical situation. For example, when admitting a patient with pneumonia, normally the clinician should order a chest x-ray, one or more antibiotics, perhaps blood cultures and certain other lab tests. Rather than making the clinician go through the time-consuming process of determining, selecting and entering each of these orders individually (whether on paper or in a computerized order entry system), an order set can speed up the ordering process greatly, while also ensuring that the clinician sees the most recommended orders (as determined by the person or group that wrote the order set, often a hospital’s quality committee) and has every opportunity to utilize those for the patient. Because ordering is a task that a clinician must do anyway, and because order sets are usually faster than individual order selection, they are generally well appreciated and well used by clinicians.</p>
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<h4>Relevant Data Summaries</h4>
<p>Relevant data summaries provide the clinician with a subset of the patient's data, a subset chosen to facilitate one particular decision or to illustrate one particular clinical issue. For example, in a tuberculosis treatment program, a relevant data display may show all of the anti-TB antibiotics that have been used, alongside the patient's culture results and clinical status indicators for the same time period. The figure below shows such a display in OpenMRS for MDR-TB patients - used in Haiti and other LMICs. The clinician can easily see and process this cluster of information without having to extract it from the clutter of all the other medications, tests, and findings in the record. Relevant data displays have been shown to significantly reduce medical errors, in such areas as patient care handoffs from one clinician to another.</p>
<p><img src="/assets/courseware/v1/4704a2e56c16a42cf28ebe8468434dea/asset-v1:MITx+HST.936x+1T2019+type@asset+block/Screen_Shot_2016-08-29_at_11.33.36_AM.png" alt="Display of key laboratory data on MDR-TB patient in Haiti using the OpenMRS-TB system " type="saveimage" target="[object Object]" width="870" height="519" /></p>
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<h4>Filtered Reference Information</h4>
<p>Filtered reference information can be especially useful when doing initial patient assessment or trying to understand a particular clinical issue or medical problem. If a clinician is seeing a patient with an unfamiliar illness, the clinician may need to pause, find a library reference, find the right section, and scan through that to get the information needed to further evaluate the patient. In the CDS version, the reference information is directly within the electronic health record, often in the form of a carefully placed hyperlink or "infobutton" and is pre-selected to be suited to the current situation and likely questions. The whole process is much faster, making it much more likely that the clinician will actually take the time to read and benefit from the information.</p>
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<h4>Analytics</h4>
<p>Analytics is coming into greater prominence as larger and more sophisticated databases come available. Analytic engines can combine dozens of parameters to make a decision, whereas traditional decision-support, and traditional human brains, normally can only handle a few factors at a time. As a result, analytics can allow us to make much more specific recommendations and to separate similar but non-identical patients much more precisely. New applications of analytics include selecting precise cancer treatments for individual patients and customizing management of chronic medical conditions such as congestive heart failure and diabetes.</p>
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<h4>Impact of Clinical Decision Support</h4>
<p>Properly designed and implemented, CDS can have a profound beneficial impact on process measures and on patient outcomes. Studies showed early on that CDS could prevent 80% of errors and 55% of adverse events in a hospital service. Other studies have demonstrated significant reductions in incorrect dosing, increased use of guidelines, and reduction in readmissions.</p>
<p>On the other hand, some studies have shown that electronic medical record use (not CDS in particular) can be a contributing cause of new errors. These errors were frequently due to awkward workflows or difficult-to-read information on the screen that led to, for example, writing orders for the wrong patient or selecting incorrect doses. Other CDS interventions could potentially be put in place to eliminate some of these problems. However, a key take-away lesson is that CDS does not produce beneficial impact automatically; careful attention to design, communication, implementation, and usability for all health information technology is important if CDS is to make healthcare better and not worse. Another lesson from these studies is that CDS success depends on people factors – gaining support of key clinicians, strong communication with medical leadership, responsiveness to concerns and complaints – as much or more than it does on system design.</p>
<p>Overall, several reviewshave shown a net positive impact from CDS implementation and use, and improved usability and design will continue to increase its benefit for healthcare quality, safety and cost.</p>
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<h2 class="hd hd-2 unit-title">Health Big Data</h2>
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<h4>Secondary Health Data Use</h4>
<p>The primary use of health data is to improve delivery of clinical care to individuals. In high-resource health settings, this is exemplified by the use of electronic medical records (EMRs) to deliver improved services via collating clinical records, generating patient-specific alerts, and automating outputs. In the United States, uptake of EMRs has been promulgated by a 2009 US$27 billion federal government commitment to have EMRs used by hospitals and health care providers. Although there are technical challenges in implementation, adoption of EMRs in primary care is steadily increasing and has been linked to cost savings and improved clinical care. However, health records provide only a fragment of information on the determinants of population health. The promise of big data for health is in developing new insights from linking varied sources of information together, a process called secondary data use.</p>
<p>Secondary data use encompasses all analysis beyond the primary intended use—that is, individual health care delivery, in the case of EMRs. We divide secondary data use into two broad entities. Direct secondary data in health includes clinical audit, research, and population-level health projects. This data is sourced directly from health records, then collated and analyzed to inform future care at both the individual and population levels. The critical characteristic is the creation of this data during routine provision of individual health care. In contrast, a vast amount of information can be sourced from data not intended for health purposes, such as death certificates, hospital and physician billing data, vehicle registration information, criminal records, public health surveillance, municipal zoning registries, commercial databases, census databases, and so on. When carefully assembled together, these multiple sources of information can be linked to inform population health interventions. This data repurposing is termed indirect secondary data use in health. This data was not initially created for the provision of individual health care, but can be used to inform population health strategy or targeted care delivery.</p>
<p>Secondary data use, whether direct or indirect, makes it possible to use information routinely collected to better understand risks and determinants of health for populations and improve disease management. Thus, secondary data use provides a mechanism to tackle determinants of health status that are unrelated to direct service provision [6]. This chapter outlines the risks and opportunities of secondary uses of data in health. Although we focus primarily on direct secondary data use for health, the principles apply to both direct and indirect uses. The role of secondary data use in high-resource settings is then contrasted with opportunities in low- and middle-income countries (LMIC), before challenges and future steps are discussed.</p>
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<h4 class="TxNITextNoIndent">Health Big Data in Resource-Poor Settings</h4>
<p class="TxNITextNoIndent">The diffusion of new technology in LMIC is commonly a mix of appropriation, diffusion, and, often, the “leapfrogging” of intermediate development phases. “Leapfrogging” is starkly exemplified by the penetration of mobile phones in low-resource settings over the last 15 years without a preceding period of fixed-line telephone ownership. The use of mobile phones has already provided a pertinent example of indirect secondary data use.</p>
<p class="TxNITextNoIndent">Buckee and colleagues demonstrated how tracking mobile phone use could help fight malaria by revealing where to focus mosquito eradication efforts. They used mobile phone use to analyze the regional travel patterns of nearly 15 million individuals over the course of a year in, and found that people making calls or sending text messages originating at the Kericho tower were three times more likely to visit a region northeast of Lake Victoria, a malaria hot spot. The Kericho tower thus represented a waypoint for transmission of malaria. A similar model has been used in the 2014 Ebola outbreak.</p>
<p class="TxNITextNoIndent">The secondary use of data in LMIC has distinctly different dynamics from data use in high-resource settings. Critically, the primary use of physician-held EMRs and health data in LMIC is limited, but the relatively democratized data collection from individuals and communities may be more acceptable. For example, in Kenya, nearly 500 community elders were trained to weigh newborns and report via text messaging birth weights to vital statistics registers.</p>
<p class="TxNITextNoIndent">The incentives for collection and use also differ between high- and low-resource settings. Proprietary systems dominate the EMR market in the developed world, and data may be “owned” by health care providers or consortiums. However, different market dynamics in LMIC mean that more mHealth and eHealth projects are conducted by governments and not-for-profit actors. These providers tend to use open-source EMR products and often have a stated goal of improving population outcomes in addition to individual care. However, ad hoc mHealth systems may not follow interoperability standards. This can complicate data linkage and limit analysis.</p>
<p class="TxNITextNoIndent">Indirect use of secondary data is also influenced by differing incentives. In many parts of the world, reliable data may be more likely to be collected by mobile phone operators or banks than government-directed health data sources. However, the private sector may have strikingly different priorities and commercial sensitivities determining the use of data. These issues affect the potential use of indirect secondary data in the interest of humanitarian or public health outcomes. Paradoxically, an uncritical urge to share data of vulnerable populations in order to “do good” can increase the risk of inadequate consultation or unforeseen consequences. Authors of a London School of Economic report caution that “the challenge for developing countries and humanitarian operations is that we have a tendency to think and act on behalf of the citizens and patients”; they call for a rights-based approach to data sharing and collection.</p>
<p class="TxNITextNoIndent">The staggering pace of primary and secondary data use for health in LMIC may offer novel opportunities to embed appropriate mechanisms for secondary health data use in first-generation platforms. One of the most promising opportunities for big data in global health can be seen in India’s ambitious personal identification program. In 2010, the government of India began issuing “Aadhaar” cards with unique identifying numbers to all 1.2 billion of its citizens. Biometric identification captured through fingerprints and iris identification offers the opportunity for generating and monitoring health and social data. Use of Aadhaar linked to immunization records could help ensure individual children receive the appropriate vaccine according to national schedules. Furthermore, secondary analysis of de-identified data may make it possible to identify areas with low immunization rates for targeted immunization campaigns.</p>
<p class="TxNITextNoIndent">One of the greatest challenges in fulfilling the potential of secondary use of data in resource-limited settings is the ability to analyze quantitative outputs. The uptake of mHealth in LMIC reflects low barriers to entry; namely, widespread mobile phone ownership and the rapidly appreciable utility of text messages or other tangible rewards for participation. In contrast, the value of the secondary use of health data resides in centralizing and linking datasets, which requires technical skills, specialized equipment, interoperability standards, coherent collection, analytical systems, and regulatory oversight. Moreover, the distance between individuals contributing data and systems outputs and benefits can be immense. However, signs of data ownership from LMIC are emerging; for example, a new network, critical of existing data-sharing approaches, is collating public health research data from health and demographic surveillance systems of over 3 million people in 48 populations. Overcoming the analytic capacity challenges in LMIC may make it possible to leapfrog the nascent secondary use of health data in high-resource settings and embed secondary-use capacity in mHealth and eHealth projects in low-resource settings.</p>
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<h4 class="TxNITextNoIndent">Challenges Abound</h4>
<p class="TxNITextNoIndent">Large-scale implementation of big-data and secondary-use systems for health in LMIC faces many challenges. The collection of individual-level information—a prerequisite for big data—poses risks commensurate to the existing challenges of care delivery in low-resource settings. </p>
<p class="TxNITextNoIndent">Of universal concern is the threat to privacy posed by the digitization and centralization of personal health information. Although in practice, secondary use involves anonymized data, capacity to interpolate back to individuals may be intentionally or unintentionally retained. Sweeney claims that 87% of the US population can be uniquely identified simply using their date of birth, gender, and zip code. For this reason, US health privacy regulation—HIPAA—stipulates that 18 specific identifiers must be removed before data is used for population health analysis.</p>
<p class="TxNITextNoIndent">Privacy concerns are further amplified when information is about individuals in vulnerable populations and communities. Even very basic health data—ethnicity, reproductive health history, sexually transmitted infections, diseases with a genetic basis, or risk exposures for disease—has the potential for misuse, discrimination, personal danger, or, in some cases, death. Breaches to digital privacy in any setting are problematic, and there is concern the risk may be amplified by secondary use of health data. Certainly, the risk of accidental or intentional breaches of data security may be increased with limited literacy, high corruption, and rapid technological transitions. Privacy management in high-resource settings is rapidly becoming counterproductive and outdated and is of little help to LMIC looking at ways to tackle these challenges. Traditionally, privacy laws have emphasized consent for data collection by an individual for a specific purpose. In direct opposition to this system, secondary use of health data is about linking and re-using data for a different purpose. Current approaches may involve long, cumbersome, and unwieldy consent forms that are meaningless to patients and leave individuals poorly protected, yet set up barriers to the beneficial use of data. In many LMIC settings, legislation supporting the privacy and security of information services is frequently underdeveloped and rarely enforced. These issues highlight the difficulties in data-sharing guidelines between LMIC stakeholders that not only hamper the benefits of big data for health but can also compromise those in play. The solutions inevitably lie in moving from individual purpose-specific consent to emphasizing data access monitoring, ethical use of data, and data governance.</p>
<p class="TxNITextNoIndent">Increasingly, poorly designed privacy safeguards are having dangerous consequences in limiting the benefits of data. Data is accumulated in silos, but it is increasingly clear that linkages and secondary use can result in potentially lifesaving intervention at the level of care delivery and planning. Direct secondary data for health is grossly underutilized, and the neglect of indirect secondary data is likely to be even greater. One review found that many of the variables collected in epidemiological studies were never cleaned or coded—the primary researchers could not even use the data. Pisani and AbouZahr articulate that “undervalued and underfunded, inadequate data management undermines the rest of the scientific enterprise”. Indeed, it undermines population health.</p>
<p class="TxNITextNoIndent">By default, it is impossible to imagine all possible secondary uses of data at the time of collection. Consequently, purpose-specific consent processes at the time of collection need to be replaced with selective sharing capability and transparent, traceable data access. There is increasing need for an educated transparent process for data governance on behalf of patients and populations, rather than piecemeal and undecipherable consent protocols that provide little effective choice or protection. Such a process needs to translate societal values and concerns into best practices on complex issues balancing benefits and risks of data use. Concerns about consent and data sharing will persist, particularly around the power imbalance between high- and low-resource settings. An open, transparent process of data governance is necessary as societal values and technological feasibility continue to rapidly evolve.</p>
<p class="TxNITextNoIndent">This vision is tempered by today’s reality in low-resource settings: weak health systems and limited governance structures complicate the emergence of a coherent and safe health data strategy. Many countries in greatest need of health metrics struggle to collect vital, simple statistics on births and deaths, while epidemiological data of variable reliability come from small sentinel sites. Moreover, little information is reliably digitized. Health interventions—food, water, and sanitation—remain top priorities for over two billion people. Data alone cannot address development challenges. However, as the cost of aggregating and coordinating resources and services electronically falls, big data stands to deliver disproportionately large benefits to LMIC. The more limited the resources for interventions, the more valuable targeting and focusing can be.</p>
<p class="TxNITextNoIndent">The application of big data to global health amplifies potential benefits, risks, and challenges. The persistent tension between vertical or disease-specific programs and horizontal or health system–focused approaches remains unresolved. Big data fits best with a horizontal approach—potentially improving data for all diseases in the spirit of the ambitious and valuable Global Burden of Disease project. However, global health remains a relatively siloed undertaking driven by disease-specific interests. Disease-specific advocacy groups may well be at the forefront of applications for big data—risking further fragmentation of the enterprise. Ensuring universal standards that allow data linkage, inclusive data collection, and data dissemination and application is critical for maximizing big data’s potential.</p>
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<h3 class="hd hd-3 problem-header" id="13b37f6f7ff5488b8f3c686516895b86-problem-title" aria-describedby="block-v1:MITx+HST.936x+1T2019+type@problem+block@13b37f6f7ff5488b8f3c686516895b86-problem-progress" tabindex="-1">
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<legend id="13b37f6f7ff5488b8f3c686516895b86_2_1-legend" class="response-fieldset-legend field-group-hd">Which of the following statements are true?</legend>
<p class="question-description" id="description_13b37f6f7ff5488b8f3c686516895b86_1_1"> Select all that apply.</p>
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<input type="checkbox" name="input_13b37f6f7ff5488b8f3c686516895b86_2_1[]" id="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_0" class="field-input input-checkbox" value="choice_0"/><label id="13b37f6f7ff5488b8f3c686516895b86_2_1-choice_0-label" for="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_13b37f6f7ff5488b8f3c686516895b86_2_1 description_13b37f6f7ff5488b8f3c686516895b86_1_1"> Secondary data use makes it possible to use information routinely collected to better understand risks and determinants of health for populations and to improve disease management.
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<input type="checkbox" name="input_13b37f6f7ff5488b8f3c686516895b86_2_1[]" id="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_1" class="field-input input-checkbox" value="choice_1"/><label id="13b37f6f7ff5488b8f3c686516895b86_2_1-choice_1-label" for="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_13b37f6f7ff5488b8f3c686516895b86_2_1 description_13b37f6f7ff5488b8f3c686516895b86_1_1"> Big data&#8217;s mechanism of action is magnification, which makes its risks and benefits larger. This amplification of risks is a bit less in low-resource settings, where data is less vulnerable to fragmentation and misuse.
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<input type="checkbox" name="input_13b37f6f7ff5488b8f3c686516895b86_2_1[]" id="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_2" class="field-input input-checkbox" value="choice_2"/><label id="13b37f6f7ff5488b8f3c686516895b86_2_1-choice_2-label" for="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_13b37f6f7ff5488b8f3c686516895b86_2_1 description_13b37f6f7ff5488b8f3c686516895b86_1_1"> The secondary use of data in low- and middle-income countries has similar dynamics from data use in high-resource settings, and is influenced by the same incentives.
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<input type="checkbox" name="input_13b37f6f7ff5488b8f3c686516895b86_2_1[]" id="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_3" class="field-input input-checkbox" value="choice_3"/><label id="13b37f6f7ff5488b8f3c686516895b86_2_1-choice_3-label" for="input_13b37f6f7ff5488b8f3c686516895b86_2_1_choice_3" class="response-label field-label label-inline" aria-describedby="status_13b37f6f7ff5488b8f3c686516895b86_2_1 description_13b37f6f7ff5488b8f3c686516895b86_1_1"> There is increasing need for an educated transparent process for data governance on behalf of patients and populations, rather than piecemeal and undecipherable consent protocols that provide little effective choice or protection. Such a process needs to translate societal values and concerns into best practices on complex issues balancing benefits and risks of data use.
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<h2 class="hd hd-2 unit-title">Use Cases</h2>
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<h4>Clinical Decision Support and Global Health</h4>
<p>Much of the published literature on CDS systems comes from industrialized countries, which have had more time to evolve health information technology and more money and time to devote to CDS. However, the benefit that CDS can provide may be even greater in lower and middle-income countries, where basic health needs are so great and where basic quality infrastructure may not yet be fully implemented. CDS tools that may be highly beneficial in LMICs include:</p>
<ul>
<li>Vaccination reminder systems</li>
<li>Tracking and missed-event detection systems for fieldworkers administering complicated drug treatments for infectious diseases such as MDR-TB</li>
<li>Interactive reference guides for complex, unfamiliar and/or unusual problems</li>
<li>Automatic printing of patient self-care materials for nutrition, hydration, chronic condition self-care, and more</li>
</ul>
<p>The recent Ebola outbreak in West Africa illustrates where effective CDS could make a real difference. The recommended treatment regimen changed as the crisis progressed; this could be addressed with order sets and interactive reference clarifying the changes. As the endemic areas shifted, surveillance tools could be directed to the most critical areas. Contact tracing systems could be matched against census information to ensure the best possible results. In developed countries where fear of importing cases of Ebola was very high, CDS was used to provide continually updated screening tools[ii]. As new medications and vaccines for Ebola come into play, alerting systems could prompt clinicians in both developed and developing countries to be aware of these new treatment possibilities when they are seeing a patient with possible Ebola infection.</p>
<p>LMICs often have serious shortages of trained medical and nursing staff, leading to task shifting of clinical diagnosis and management to less skilled staff. The potential benefits of CDS are increased even more in these settings. Well-designed CDS systems can provide knowledge of optimal care patterns embedded into order sets, care plans, and smart documentation tools, while also providing expert situational awareness through alerts and reminders. Such tools can be beneficial, for examples, for nurses managing HIV patients in Kenya or MDR-TB patients in Peru or for Community Healthcare Workers managing malaria in East Africa. </p>
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<p>Andrew Kanter MD MPH<br /><span style="text-rendering: optimizeLegibility; margin: 0px; padding: 0px; border: 0px; outline: 0px; font-variant-numeric: inherit; font-stretch: inherit; line-height: 25.6px; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; vertical-align: baseline;">Chief Medical Officer</span><br style="text-rendering: optimizeLegibility; line-height: 1.4em; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;" /><span style="text-rendering: optimizeLegibility; margin: 0px; padding: 0px; border: 0px; outline: 0px; font-variant-numeric: inherit; font-stretch: inherit; line-height: 25.6px; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; vertical-align: baseline;">Integrated Medical Objects</span></p>
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<h3 class="hd hd-2">CommCare</h3>
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<h3 class="hd hd-2">Open MRS</h3>
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Unit Exercise
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<legend id="3a8ce5158c744b8881f75bbc37fea57d_2_1-legend" class="response-fieldset-legend field-group-hd">The Clinical Decision Support in the example above using CommCare in Ghana is provided through the use of</legend>
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<input type="radio" name="input_3a8ce5158c744b8881f75bbc37fea57d_2_1" id="input_3a8ce5158c744b8881f75bbc37fea57d_2_1_choice_1" class="field-input input-radio" value="choice_1"/><label id="3a8ce5158c744b8881f75bbc37fea57d_2_1-choice_1-label" for="input_3a8ce5158c744b8881f75bbc37fea57d_2_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_3a8ce5158c744b8881f75bbc37fea57d_2_1"> reminders sent through SMS texts
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</div>
<div class="field">
<input type="radio" name="input_3a8ce5158c744b8881f75bbc37fea57d_2_1" id="input_3a8ce5158c744b8881f75bbc37fea57d_2_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="3a8ce5158c744b8881f75bbc37fea57d_2_1-choice_2-label" for="input_3a8ce5158c744b8881f75bbc37fea57d_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_3a8ce5158c744b8881f75bbc37fea57d_2_1"> guidelines embedded in smart phones
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<div class="field">
<input type="radio" name="input_3a8ce5158c744b8881f75bbc37fea57d_2_1" id="input_3a8ce5158c744b8881f75bbc37fea57d_2_1_choice_3" class="field-input input-radio" value="choice_3"/><label id="3a8ce5158c744b8881f75bbc37fea57d_2_1-choice_3-label" for="input_3a8ce5158c744b8881f75bbc37fea57d_2_1_choice_3" class="response-label field-label label-inline" aria-describedby="status_3a8ce5158c744b8881f75bbc37fea57d_2_1"> telemedicine
</label>
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<span id="answer_3a8ce5158c744b8881f75bbc37fea57d_2_1"/>
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<div class="indicator-container">
<span class="status unanswered" id="status_3a8ce5158c744b8881f75bbc37fea57d_2_1" data-tooltip="Not yet answered.">
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<div class="action">
<input type="hidden" name="problem_id" value="Unit Exercise" />
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<button type="button" class="submit btn-brand" data-submitting="Submitting" data-value="Submit" data-should-enable-submit-button="True" aria-describedby="submission_feedback_3a8ce5158c744b8881f75bbc37fea57d" >
<span class="submit-label">Submit</span>
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<div class="submission-feedback" id="submission_feedback_3a8ce5158c744b8881f75bbc37fea57d">
<span class="sr">Some problems have options such as save, reset, hints, or show answer. These options follow the Submit button.</span>
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is-hidden"
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