Using Data to Improve Student Outcomes

Targeting where conservation works best Conservation has long wrestled with a deceptively simple question: not whether to act, but where action will matter most. Restoration, protected areas, corridors and enforcement all compete for limited funding across landscapes that differ widely in ecology, governance & human pressure. Increasingly, research suggests that improving outcomes depends less on new tools than on using existing ones more selectively — directing effort to places where it will make the greatest difference relative to doing nothing. A 2025 paper led by Rebecca Spake described this approach as “precision ecology.” It argued that conservation should move beyond estimating average effects and instead predict site-specific outcomes, tailoring actions to local conditions. The concept draws on precision medicine, which matches treatments to individual patients rather than applying uniform therapies. The logic is straightforward. Conservation operates in heterogeneous systems, where the same intervention can succeed in one place and fail in another. Tree planting may restore ecosystem function where soils, rainfall and protection are adequate, yet fail where drought, fire or grazing dominate. Anti-poaching patrols may deter illegal hunting in accessible reserves but struggle in remote areas. One-size-fits-all strategies are therefore unreliable. The paper highlights statistical methods — drawn from economics & machine learning — that estimate how intervention effects vary with context. Yet conservation has long targeted its efforts. Planning tools design protected-area networks to maximize biodiversity at minimum cost. Restoration programs prioritize areas with high recovery potential, while satellite monitoring directs responses. In practice, managers already concentrate resources where threats or opportunities are greatest. Where precision ecology differs is in emphasis. Traditional targeting often focuses on ecological value or threat. The newer perspective asks about effectiveness: the difference an intervention will make. A site may be biologically rich yet likely to persist unaided, while a less celebrated area might decline rapidly without action. Implementing such approaches depends heavily on data. Advances in remote sensing and environmental monitoring provide unprecedented detail, but gaps remain in many regions, and models built on sparse data can give a false sense of certainty. Practical constraints also matter. Land tenure, community priorities & political feasibility often determine where projects occur as much as ecological potential. Seen this way, precision ecology is a refinement. Conservation has gradually moved toward more evidence-based, context-specific strategies. Perfect prediction is impossible, but better targeting can help ensure scarce resources achieve the greatest impact.  As pressures on ecosystems intensify, the difference between acting everywhere and acting strategically may prove decisive.

Recent population health data suggests that residents in the northern region of Singapore, particularly towns such as Woodlands, Yishun, and Sembawang, have a higher prevalence of diabetes and hypertension compared with national averages. While the numbers are clear, the underlying causes are less certain. Several plausible factors may contribute: • Demographic structure, including an older population profile • Socioeconomic gradients that influence diet, stress, and health behaviours • Differences in physical activity and lifestyle patterns • Ethnic distribution and associated metabolic risk profiles • More active screening and detection through primary care networks However, these remain hypotheses. More rigorous research is needed to understand the drivers behind this geographic clustering of chronic disease. One promising approach is the use of artificial intelligence for population health monitoring. AI can integrate multiple data sources such as electronic medical records, screening programmes, pharmacy data, wearable devices, and socioeconomic indicators to detect emerging patterns of disease. With machine learning and geospatial analytics, health systems could identify high-risk neighbourhoods earlier, monitor behavioural risk factors such as physical activity, and predict which communities are most vulnerable to chronic disease. This would allow health systems to move beyond reactive care toward proactive population health management. Instead of waiting for complications to appear, we can anticipate risk, target prevention programmes, and evaluate whether interventions are working. Understanding why disease burden concentrates in specific communities is essential for designing effective public health strategies. Combining epidemiological research with AI-driven monitoring may help us better understand these patterns and ultimately improve the health of our population. #PopulationHealth #Diabetes #Hypertension #AIinHealthcare #PublicHealth #Singapore

Our new peer-reviewed research on preventing ED visits and hospitalizations among patients receiving Medicaid: https://lnkd.in/g7wrhEhr When patients have multiple conditions, the optimal "next best step" is rarely clear. With 15 minutes left on a Friday night to spend with a patient with uncontrolled schizophrenia and hypertension, do I first spend my time on a prior authorization for a long-acting antipsychotic or on medication adherence counseling for a new blood pressure regimen? Standard guidelines leave these crucial, sequential decisions to varied individual judgement and experience. Our new, peer-reviewed research, published in JMIR AI, shows that Reinforcement Learning (RL) offers powerful guidance for such decisions. Instead of relying on LLMs (which can hallucinate, risking safety), we carefully studied years of intervention data from multidisciplinary population health teams, comparing the outcomes of similar patients who received different interventions or intervention sequences. We used these historical intervention sequences and their outcomes to build a State-Action-Reward-State-Action (SARSA) RL model to recommend the optimal interventions to population health teams. The results: - In this counterfactual causal inference study, the RL-guided approach reduced acute care events (ED visits and hospitalizations) by 12 percentage points compared to the status quo, a 20.7% relative reduction (P=0.02). - It yielded a Number Needed to Treat (NNT) of 8.3 patients getting service to prevent one acute care event. - Crucially, there was no evidence of harm (number needed to harm = infinity). - The RL-guided approach also improved fairness across demographic groups, with a 28.3% reduction in gender-based disparity and a 37.1% reduction in race/ethnicity-based disparity. This work demonstrates that population health teams enabled by RL technologies can outperform those relying on experience- or playbook-based practices alone, particularly when navigating the complex intersections of medical and social needs. This builds on ongoing work at Waymark in RL for Medicaid: Test-Time Learning and Inference-Time Deliberation for Efficiency-First Offline Reinforcement Learning in Care Coordination and Population Health Management: https://lnkd.in/gm8hU7Pd Hybrid Adaptive Conformal Offline Reinforcement Learning for Fair Population Health Management: https://lnkd.in/gVuWmj5p Accepted to Stanford's #Agents4Science2025: Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management: https://lnkd.in/dC48TCdp Andrew Ng James Zou Pranav Rajpurkar Lucas Hopkins Andrea Ramirez Scott Anders Michael Pencina Josh Patten Keith Payet Jerold Mammano Joel Gray Doug McMillen Haroon Hyder Wael Haidar Yasir Tarabichi, Brian Martin, Mohammad Dar Christina Severin Sunita Kasliwal Baligh Yehia, Jeffrey Cohen Paul Testa Suja Mathew Tracy B. Aparna Abburi Erin Nahrgang,Rob Fields Daniel Barchi Shantanu Nundy Vineeta Agarwala, Hui Cheng

There is increasing emphasis on using quality improvement to address health inequalities—but what does the evidence say about impact? A 2025 synthesis from The Health Equity Evidence Centre reviewed multiple QI programmes designed to reduce disparities in access, experience, and outcomes. 🔗 Read the report: https://lnkd.in/eMrJNKGd Across studies, the most effective interventions shared three core design features: - Data stratification (e.g. by deprivation, ethnicity) - Co-design with affected populations - Iterative testing using PDSA cycles Where quantitative results were reported: - Screening uptake increased by ~5–15 percentage points in underserved groups - Improvements in chronic disease outcomes (e.g. HbA1c reduction in targeted diabetes cohorts) - Reduced non-attendance when culturally tailored engagement strategies were used However, the review also highlights methodological gaps: - Limited use of controlled or quasi-experimental designs - Small-scale studies with variable generalisability - Sparse long-term sustainability data The implication is nuanced: 👉 QI can reduce inequalities—but only when equity is explicitly integrated into the design and delivery of the quality improvement work. Are we truly co-designing our quality improvement work with those who are most affected and most vulnerable to inequitable outcomes? Are we integrating equity into the way we design aim statements and measurement plans? Are we designing targeted interventions—not just universal ones? Are we testing and adapting with the same rigour we apply elsewhere in QI?

Pharmacy Interventions: The Power of Proper Documentation, Categorization, and Data Analysis Pharmacists intervene every day to prevent harm, optimize therapy, and improve outcomes—but the true value of these actions depends on how well they’re documented, categorized, and analyzed. Why does this matter? Correct Documentation ensures clarity, reproducibility, and accountability. Vague notes like "checked Rx" or "adjusted dose" don’t reflect the clinical thought process or impact. Accurate Categorization allows us to group interventions by drug-related problem type, underlying cause, severity, and impact—providing structured insight into where systems are failing or succeeding. Meaningful Data Analysis turns scattered entries into trends, performance indicators, and opportunities for systemic improvement. Once standardized, this information becomes a powerful quality tool that can be presented in: Medication Safety Committees Pharmacy & Therapeutics (P&T) Committees Clinical Governance Forums Quality and Risk Management Committees Executive Leadership Reviews It supports safer prescribing practices, informs training needs, guides formulary decisions, and demonstrates the clinical and financial impact of pharmacy services. These tools and references can help: 1. PCNE Classification System – A globally accepted tool for categorizing drug-related problems. https://lnkd.in/dZkNrcDC 2. ASHP Guidelines – Outlines documentation as a core responsibility of clinical pharmacists. https://lnkd.in/dREmj-cT 3. CLEO Tool (French Society of Clinical Pharmacy) – A structured way to assess the Clinical, Economic, and Organizational impact of interventions. https://lnkd.in/dKh2mmuP 4. STOPP/START Criteria – Especially useful in older adults for identifying inappropriate prescribing and missed therapies. https://lnkd.in/dSAKQbxx 5. Systematic Review of Clinical Pharmacy Interventions – A great reference that shows the breadth and benefit of pharmacy interventions. https://lnkd.in/dgrfUuDa Let’s elevate pharmacy documentation from a checkbox activity to a strategic enabler of safe, high-quality care. #PharmacyPractice #MedicationSafety #ClinicalPharmacy #PatientSafety #HealthcareData #QualityImprovement #PharmacyLeadership #PCNE #CLEO #STOPPSTART #ASHP #Governance #PharmacyDocumentation

In the current global landscape, where the success of health programs is increasingly contingent on the precise application of data analysis and interpretation, the indispensable role of Monitoring and Evaluation (M&E) has taken center stage. This document offers a well-structured, detailed guide, specifically tailored for M&E professionals and humanitarian workers, highlighting how data-informed decision-making can drive the success of health interventions. With its focus on practical tools and strategies, this guide empowers readers to improve health outcomes through more robust program evaluations and service delivery assessments. By delving into core statistical principles and methodologies, this guide enables M&E workers to refine their ability to monitor program performance and assess the impact of health services. The challenges posed by ongoing global health crises—such as the HIV epidemic, the resurgence of tuberculosis, and the continued prevalence of malaria—underscore the necessity of harnessing accurate, high-quality data for informed decision-making. This guide exemplifies these principles through real-world case studies, such as Thailand’s HIV prevention initiatives and the effective scaling of HIV testing in Nigeria, both of which leveraged data to inform critical policy and operational decisions. Ultimately, this document serves as an invaluable resource for M&E professionals, providing them with the skills and knowledge necessary to improve the quality of their evaluations and the programs they support. By embracing the insights and techniques offered in this guide, readers will be better equipped to influence policies, enhance service delivery, and foster more impactful, data-driven solutions to some of the world’s most pressing health challenges.

How are you using your program data? If your answer is reporting and then some silence....read on. Your program data should be used for multiple purposes beyond accountability and reporting. Here are some tips to apply to start utilizing your program data for learning and adaptive management; 1) Assess if you are answering the correct questions. ↪ Is the data you are collecting what you need to learn? For example, will the number of farmers trained tell you what you need to do to improve the intervention? 2) Design learning questions with the program decision-makers. ↪ Bring together the critical program decision-makers and go through an exercise to determine what information they rely on to assess if things are working as expected. 3) Review and redesign your data collection and analysis system to address the learning needs. ↪ Think beyond quantitative data collection methods. Incorporate participatory M&E and qualitative inquiry approaches. 4) Provide evidence on time and in the correct format to the different decision-makers. ↪ Have more than 1 format for presenting the evidence gathered and ensure it comes at the right time to influence decision-making. 5) Support evidence translation. ↪ Sharing the evidence in written formats is not enough. Consider evidence synthesis and sensemaking activities that help the team understand what the evidence is 'saying.' 6) Set up follow-up systems ↪ Design systems to track how the evidence is used and how the adaptations affect program outcomes. PS: What would you add to the list? Follow me, Florence Randari, for more tips and resources on learning and adaptive management!

The presentation by Jessina McGregor, PhD, explains how Interrupted Time Series (ITS) is a robust quasi-experimental design widely used to evaluate the effect of interventions, especially when randomized controlled trials aren’t feasible. ITS analyzes outcome data collected at multiple, evenly spaced time points before and after an intervention to assess whether the intervention caused a change. It can detect both immediate effects (as a sudden shift in the outcome level) and gradual effects (as a change in the trend over time). ITS belongs to the broader field of causal inference because it aims to answer: Did the intervention cause a change in the outcome? While ITS can’t guarantee the same level of causal certainty as randomization, its strength comes from its structured design: using many observations over time, ruling out pre-existing trends, and sometimes including control groups, staggered rollouts, or intervention removal to strengthen causal claims. Statistical analysis of ITS often uses segmented regression or ARIMA models, which properly account for autocorrelation (the fact that observations over time are related). Careful planning is critical: defining the intervention clearly, selecting measurable outcomes, collecting long enough baseline and follow-up periods, and adjusting for other events like policy changes or seasonal effects. Overall, ITS is an essential tool in causal inference, particularly valuable for evaluating large-scale or system-level interventions in fields like antimicrobial stewardship, where randomized trials are often impractical. Link: https://lnkd.in/eGby6kMc #statistics #quasiexperimental #causalinference

Data Wrangling During WWII, Allied analysts studied returning bombers riddled with bullet holes. They initially thought the most-hit areas needed extra armour. However, mathematician Abraham Wald famously realised the opposite: the truly vulnerable spots were the areas with no bullet holes – those planes hit there never came back. So, what does a WWII aircraft have to do with your data strategy? This counterintuitive insight saved lives by focusing not on the obvious damage, but on the missing bullet holes. (see the image). It’s a powerful example of survivorship bias – the mistake of focusing only on what (or who) survived, while ignoring what didn’t. And in data strategy, that’s a dangerous trap. (Survivorship bias - Wikipedia https://lnkd.in/e3qhF9Q4). Sometimes the most important insight isn’t in what did happen, but in what didn’t. Applying Wald’s wisdom to modern data strategy: 1️⃣ 𝐋𝐨𝐨𝐤 𝐟𝐨𝐫 𝐀𝐛𝐬𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 – Don’t just pour over the data you have; ask about the data you don’t have. Which users aren’t converting or returning, and why? Which product features are being avoided, not just used? Like Wald’s bullet-free zones, the gaps in your data often pinpoint critical issues. If you only analyse who clicked or survived, you’ll miss insights hidden in who bounced or failed to show up. 2️⃣ 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐭𝐡𝐞 𝐍𝐨𝐫𝐦𝐚𝐥 – Challenge the assumptions drawn from only the “visible” success stories. Just because a tactic is considered industry best practice doesn’t mean it worked for everyone – it might just mean the failures aren’t being discussed. Always ask yourself: “What are we not seeing?” 3️⃣ 𝐈𝐧𝐜𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞 𝐅𝐚𝐢𝐥𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 – Make sure your analysis includes the flops, dropouts, and churned users, not just the success cases. There are lessons in why a product failed or a customer churned that you’ll never learn from your happy path data. Don’t shy away from the negative results; embrace them as a guide to what needs fixing. 4️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐔𝐧𝐬𝐞𝐞𝐧 – Design your products, systems, and even security frameworks with the “unseen” threats and failures in mind. It’s not enough to base plans only on what has worked before; also account for what didn’t work or what hasn’t happened yet. In product development, think about the users who quit during onboarding or the edge cases causing crashes – then improve those areas, not just the features active users love. In cybersecurity, if you only reinforce defences where attacks happened last time, you might leave yourself exposed elsewhere. Experts have noted that focusing solely on past breach data (the obvious “bullet holes”) can cause teams to misjudge risk and overlook critical vulnerabilities. #DataWrangling #Data #DataInsights #DataQuality #DataCompleteness #SurvivorshipBias #DataStrategy #BIAS #CyberSecurity #GoodData #BadData

Every referral in healthcare is a $10,000 decision. But too often, they’re still made by habit, not data. “We’ve always referred here.” “That’s who we know.” “They’ve always been good to us.” That mindset is common. But it leaves money on the table, and patients at risk. When you redesign referral networks around data instead of anecdotes, 3 things happen: ✅ Patients are guided to specialists with stronger outcomes ✅ Health systems cut avoidable costs at scale ✅ PCPs make decisions with clarity and confidence So how do you put data into action? → 𝗠𝗮𝗽 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘄𝗶𝘁𝗵 𝘀𝗰𝗼𝗿𝗲𝗰𝗮𝗿𝗱𝘀. Use data to see where patients are actually going, highlight costly leakage, and identify which specialists consistently deliver better outcomes. → 𝗟𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘁𝗼 𝘄𝗶𝗻 𝘁𝗿𝘂𝘀𝘁. PCPs care most about patient outcomes. Show them evidence of higher-quality care first, cost savings will follow naturally. → 𝗦𝗲𝘁 𝗰𝗹𝗲𝗮𝗿 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮-𝗯𝗮𝗰𝗸𝗲𝗱 𝗮𝗴𝗿𝗲𝗲𝗺𝗲𝗻𝘁𝘀. Define expectations around access, communication, and coordination backed by real data. So accountability is built in from day one. Because the point isn’t to gather more data. It’s to use the right data to guide better action for patients, for systems, for growth. That’s how networks stop being a cost center and start driving sustainable growth. 👉 If you were redesigning your referral network today, which single data point would you put at the center?