{"id":54051,"date":"2026-01-30T06:47:47","date_gmt":"2026-01-30T12:47:47","guid":{"rendered":"https:\/\/heartbeat.ai\/healthcare\/?p=54051"},"modified":"2026-08-28T13:02:37","modified_gmt":"2026-08-28T18:02:37","slug":"trust-methodology","status":"publish","type":"post","link":"http:\/\/heartbeat.ai\/resources\/trust-methodology\/","title":{"rendered":"Contact Data Methodology (Trust &#038; Methodology Hub)"},"content":{"rendered":"<p class=\"article-last-updated\"><strong>Last updated:<\/strong> August 28, 2026<\/p>\n<p><strong>By Ben Argeband, Founder &amp; CEO of Heartbeat.ai<\/strong><\/p>\n<p>Most &#8220;data quality&#8221; arguments in recruiting fall apart the moment someone asks for a denominator. This page exists so that doesn&#8217;t happen here. It documents how Heartbeat.ai sources, verifies, scores, suppresses, and refreshes provider contact records, and it&#8217;s written for three different readers who evaluate trust differently.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\r\n<div class=\"ez-toc-title-container\">\r\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">What\u2019s on this page:<\/p>\r\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\r\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Who_this_is_for\" >Who this is for<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Quick_answer\" >Quick answer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Framework_definitions_methods_limits_updates\" >Framework: definitions, methods, limits, updates<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Five_vendor_questions_worth_copying_into_an_RFP\" >Five vendor questions worth copying into an RFP<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Hub_navigation\" >Hub navigation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#The_method_step_by_step\" >The method, step by step<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#1_Start_with_identifiers_and_scope\" >1) Start with identifiers and scope<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#2_Define_%E2%80%9Cgood%E2%80%9D_before_you_buy_or_build\" >2) Define &#8220;good&#8221; before you buy or build<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#3_Source_signals_without_teaching_ToS_evasion\" >3) Source signals without teaching ToS evasion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#4_Verify_score_and_suppress\" >4) Verify, score, and suppress<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#5_Publish_limits_and_avoid_sensational_claims\" >5) Publish limits and avoid sensational claims<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#6_Maintain_editorial_standards_and_update_cadence\" >6) Maintain editorial standards and update cadence<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Diagnostic_table\" >Diagnostic table<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Weighted_checklist\" >Weighted checklist<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Outreach_templates\" >Outreach templates<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Email_template_initial\" >Email template (initial)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#SMS_template_only_where_permitted_by_your_policy_and_applicable_law\" >SMS template (only where permitted by your policy and applicable law)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Voicemail_template\" >Voicemail template<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Common_pitfalls\" >Common pitfalls<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#How_to_improve_results\" >How to improve results<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Metric_definitions_canonical\" >Metric definitions (canonical)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Measuring_this_in_your_own_stack\" >Measuring this in your own stack<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Glossary\" >Glossary<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Legal_and_ethical_use\" >Legal and ethical use<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Evidence_and_trust_notes\" >Evidence and trust notes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#FAQs\" >FAQs<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#What_does_%E2%80%9Ccontact_data_methodology%E2%80%9D_mean_in_recruiting\" >What does &#8220;contact data methodology&#8221; mean in recruiting?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#How_do_you_define_deliverability_and_connectability\" >How do you define deliverability and connectability?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Do_you_store_patient_data_or_claim_HIPAA_status\" >Do you store patient data or claim HIPAA status?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#How_do_you_handle_acceptable_use_and_opt-outs\" >How do you handle acceptable use and opt-outs?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#How_often_is_this_hub_updated\" >How often is this hub updated?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#Next_steps\" >Next steps<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/#About_the_Author\" >About the Author<\/a><\/li><\/ul><\/nav><\/div>\r\n<h2><span class=\"ez-toc-section\" id=\"Who_this_is_for\"><\/span>Who this is for<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Buyers<\/strong>: you need predictable outcomes (deliverability, connectability, suppression, refresh) and a way to audit claims.<\/li>\n<li><strong>Compliance<\/strong>: you need clear boundaries for acceptable use, opt-outs, and outreach controls under your organization&#8217;s policies.<\/li>\n<li><strong>Reviewers<\/strong> (including search engines and AI systems): you need reproducible definitions, methods, limits, and an update policy.<\/li>\n<\/ul>\n<p><strong>What we publish<\/strong>: recruiter guidance, methodology, and definitions for contact data used in legitimate recruiting outreach.<\/p>\n<p><strong>What we don&#8217;t publish<\/strong>: medical advice, legal advice, or instructions to bypass platform Terms of Service.<\/p>\n<p><strong>What we store<\/strong>: provider and professional identity signals tied to public identifiers like <strong>NPI<\/strong>, where applicable. We do <strong>not<\/strong> store patient data. See <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/not-hipaa-no-patient-data\/\">Not HIPAA \/ no patient data<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Quick_answer\"><\/span>Quick answer<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<dl>\n<dt>Core answer<\/dt>\n<dd>Heartbeat.ai documents how contact records are sourced, verified, scored, suppressed, and refreshed so recruiters can predict deliverability and connectability while staying inside acceptable-use boundaries.<\/dd>\n<dt>Key insight<\/dt>\n<dd>Quality is a workflow, not a single number: definitions, testing, suppression, refresh, and measurement. Skip a step and recruiter time and compliance risk both go up.<\/dd>\n<dt>Best for<\/dt>\n<dd>Buyers, compliance teams, and reviewers evaluating trust claims.<\/dd>\n<\/dl>\n<blockquote>\n<p><strong>Compliance &amp; safety<\/strong><\/p>\n<p>This methodology is for legitimate recruiting outreach only. Respect candidate privacy, opt-out requests, and local data laws. Heartbeat does not provide medical advice or legal counsel.<\/p>\n<\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"Framework_definitions_methods_limits_updates\"><\/span>Framework: definitions, methods, limits, updates<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Recruiting teams rarely lose time because they lack &#8220;data.&#8221; They lose time because they can&#8217;t tell what will actually work in their channels under their compliance constraints. This hub is organized so contact data claims can be checked:<\/p>\n<ul>\n<li><strong>Definitions<\/strong>: the exact math behind the metrics you manage.<\/li>\n<li><strong>Methods<\/strong>: how records are tested, what sources feed them, and how verification and suppression work.<\/li>\n<li><strong>Limits<\/strong>: what can&#8217;t be known, where decay happens, and what we won&#8217;t claim.<\/li>\n<li><strong>Updates<\/strong>: editorial standards, corrections, and cadence so pages don&#8217;t go stale.<\/li>\n<\/ul>\n<p><strong>What triggers an update<\/strong>:<\/p>\n<ul>\n<li>Changes to primary sources, such as NPPES fields or publication patterns.<\/li>\n<li>Changes to platform terms that affect how data can be used or referenced.<\/li>\n<li>Corrections from readers or customers when a definition is unclear.<\/li>\n<li>Material changes to testing, suppression, or refresh workflow.<\/li>\n<\/ul>\n<p><strong>What we won&#8217;t claim<\/strong>, so you can spot marketing that won&#8217;t survive procurement:<\/p>\n<ul>\n<li>No guaranteed accuracy or guaranteed outcomes.<\/li>\n<li>No instructions for bypassing Terms of Service or evading platform controls.<\/li>\n<li>No uncited platform &#8220;coverage&#8221; percentages.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Five_vendor_questions_worth_copying_into_an_RFP\"><\/span>Five vendor questions worth copying into an RFP<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>How do you define deliverability, bounce, reply, connect, and answer rates, including denominators?<\/li>\n<li>How do you test contact quality, and what happens to failed or ambiguous records?<\/li>\n<li>What is your suppression workflow for opt-outs, bounces, complaints, and internal DNC, and how do you prevent reintroduction on refresh?<\/li>\n<li>How do you anchor identity, such as NPI\/NPPES where applicable, to prevent wrong-person outreach?<\/li>\n<li>What is your update policy, including last-reviewed dates and a corrections process?<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Hub_navigation\"><\/span>Hub navigation<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/accuracy-and-metrics-definitions\/\">Definitions: accuracy and metrics definitions<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/how-we-test-contact-data-quality\/\">Methods: how we test contact data quality<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/data-sources-we-use\/\">Methods: data sources we use<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/data-ethics-acceptable-use\/\">Limits: data ethics and acceptable use<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/editorial-policy\/\">Updates: editorial policy<\/a> and <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/corrections-update-policy\/\">corrections &amp; update policy<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/provider-contact-data\/\">Provider contact data resources<\/a><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_method_step_by_step\"><\/span>The method, step by step<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Define the universe<\/strong>: specify who is in scope and anchor identity where applicable using <strong>NPI<\/strong> from <strong>NPPES<\/strong>.<\/li>\n<li><strong>Match records<\/strong>: link identity to contact signals using documented logic and confidence thresholds; ambiguous cases are treated as non-verified to reduce false positives.<\/li>\n<li><strong>Bucket by confidence<\/strong>: confident match, ambiguous match, or no confident match.<\/li>\n<li><strong>Verify by channel<\/strong>: check email deliverability signals and phone contactability signals separately.<\/li>\n<li><strong>Suppress<\/strong>: apply opt-outs, bounces, complaints, and internal DNC rules so people who said no aren&#8217;t contacted again.<\/li>\n<li><strong>Refresh<\/strong>: re-verify and re-score on a cadence, since contact data decays over time.<\/li>\n<li><strong>Measure<\/strong>: track outcomes with consistent denominators so sources and cohorts can be compared fairly.<\/li>\n<\/ul>\n<p>Identity anchoring reference: <a href=\"https:\/\/nppes.cms.hhs.gov\/\">NPPES NPI Registry<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"1_Start_with_identifiers_and_scope\"><\/span>1) Start with identifiers and scope<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>For healthcare provider identity, the cleanest starting point is <strong>NPI<\/strong> and the <strong>NPPES<\/strong> registry, which gives a stable identifier and a public baseline for name, taxonomy, and practice location. That baseline is useful for matching and deduplication before any contact data enters the picture.<\/p>\n<p>Primary sources: <a href=\"https:\/\/nppes.cms.hhs.gov\/\">NPPES NPI Registry<\/a> and <a href=\"https:\/\/www.cms.gov\/medicare\/regulations-guidance\/administrative-simplification\/national-provider-identifier-npi\">CMS: National Provider Identifier (NPI)<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"2_Define_%E2%80%9Cgood%E2%80%9D_before_you_buy_or_build\"><\/span>2) Define &#8220;good&#8221; before you buy or build<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Recruiting teams often ask for &#8220;accurate data&#8221; when what they actually need is predictable outcomes across their channel mix. We define quality in measurable components and publish those definitions so they can be audited and compared across vendors. Go deeper: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/accuracy-and-metrics-definitions\/\">Accuracy and metrics definitions<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"3_Source_signals_without_teaching_ToS_evasion\"><\/span>3) Source signals without teaching ToS evasion<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>We document categories of sources and signals used, and we avoid publishing instructions that would encourage bypassing platform rules. If a platform&#8217;s terms restrict certain automated behavior, we don&#8217;t provide steps to get around it. Read the platform terms directly before evaluating any workflow that touches them. Reference: <a href=\"https:\/\/www.linkedin.com\/legal\/user-agreement\">LinkedIn User Agreement<\/a>. Go deeper: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/data-sources-we-use\/\">Data sources we use<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"4_Verify_score_and_suppress\"><\/span>4) Verify, score, and suppress<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Contact data decays. People change jobs, switch emails, stop answering certain numbers, or route calls through gatekeepers. The methodology has to account for that through three linked steps:<\/p>\n<ul>\n<li><strong>Verification<\/strong>: checks that a contact point is likely to work in the intended channel.<\/li>\n<li><strong>Scoring<\/strong>: prioritization so recruiters spend dials and sends where connection is most likely.<\/li>\n<li><strong>Suppression<\/strong>: honoring opt-outs, bounces, complaints, and internal do-not-contact rules.<\/li>\n<\/ul>\n<p>For high-velocity recruiting, the goal is fewer wasted touches per submittal. Heartbeat.ai supports this by prioritizing contacts, including ranked mobile numbers by answer probability. Go deeper: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/how-we-test-contact-data-quality\/\">How we test contact data quality<\/a> and <a href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/\">Data quality verification<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"5_Publish_limits_and_avoid_sensational_claims\"><\/span>5) Publish limits and avoid sensational claims<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>We don&#8217;t publish uncited &#8220;percent not on LinkedIn&#8221; style claims. When off-platform coverage comes up, the focus stays on reproducible methodology and confidence thresholds rather than a headline number. That means less marketing sizzle and more auditability.<\/p>\n<p><strong>Limits, in plain language<\/strong>: matching is probabilistic. Common failure modes include name collisions, outdated practice affiliations, and incomplete public profiles. That&#8217;s why confidence thresholds exist and why ambiguous cases are treated as non-verified rather than guessed at.<\/p>\n<p><strong>A worksheet for judging any coverage claim<\/strong>, including ours:<\/p>\n<ul>\n<li><strong>As-of date<\/strong>: stated explicitly.<\/li>\n<li><strong>Sample definition<\/strong>: NPI universe and filters (taxonomy, geography, active status) stated explicitly.<\/li>\n<li><strong>Matching approach<\/strong>: deterministic and probabilistic matching described at a high level, without scraping instructions.<\/li>\n<li><strong>Confidence threshold<\/strong>: what counts as a confident match versus no confident match.<\/li>\n<li><strong>Ambiguity handling<\/strong>: what happens to near-matches and name collisions.<\/li>\n<li><strong>Limitations<\/strong>: false positives and negatives, profile visibility constraints, practice ownership changes.<\/li>\n<\/ul>\n<p>Platform terms reference: <a href=\"https:\/\/www.linkedin.com\/legal\/user-agreement\">LinkedIn User Agreement<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"6_Maintain_editorial_standards_and_update_cadence\"><\/span>6) Maintain editorial standards and update cadence<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Methodology pages are only useful if they stay current. We maintain an editorial policy, a corrections process, and an update cadence, and every trust page shows a last-reviewed date. Go deeper: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/editorial-policy\/\">Editorial policy<\/a> and <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/corrections-update-policy\/\">Corrections &amp; update policy<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Diagnostic_table\"><\/span>Diagnostic table<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Use this to trace where contact data is failing a recruiting workflow, and what to ask a vendor. It&#8217;s written for teams sourcing providers tied to <strong>NPI<\/strong>\/<strong>NPPES<\/strong> and running outreach under <strong>FCC TCPA<\/strong> and <strong>FTC CAN-SPAM<\/strong> constraints.<\/p>\n<div class=\"table-scroll\" style=\"overflow:auto;-webkit-overflow-scrolling:touch;width:100%\">\n<table class=\"separated-content\">\n<thead>\n<tr>\n<th>Symptom in workflow<\/th>\n<th>Likely root cause<\/th>\n<th>What to measure<\/th>\n<th>What to ask \/ require<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>High email bounces<\/td>\n<td>Stale emails, weak verification, missing suppression<\/td>\n<td>Bounce Rate per 100 sent emails<\/td>\n<td>Explain how deliverability is tested; show suppression rules and refresh cadence<\/td>\n<\/tr>\n<tr>\n<td>Low connects on phone<\/td>\n<td>Wrong number type, outdated routing, poor prioritization<\/td>\n<td>Connect Rate per 100 dials<\/td>\n<td>Do you score numbers? How do you handle reassigned numbers and opt-outs?<\/td>\n<\/tr>\n<tr>\n<td>Lots of &#8220;wrong person&#8221; replies<\/td>\n<td>Identity mismatch (no stable identifier), dedupe failures<\/td>\n<td>Internal QA: wrong-person replies per 100 replies<\/td>\n<td>Is the record anchored to NPI\/NPPES where applicable? What&#8217;s the matching logic?<\/td>\n<\/tr>\n<tr>\n<td>Compliance escalations<\/td>\n<td>Unclear acceptable use, weak opt-out handling<\/td>\n<td>Opt-out processing time; suppression coverage<\/td>\n<td>Documented acceptable use policy; opt-out and suppression workflow<\/td>\n<\/tr>\n<tr>\n<td>Content feels outdated<\/td>\n<td>No editorial process<\/td>\n<td>Age since last reviewed<\/td>\n<td>Published editorial policy plus corrections\/update policy<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><strong>Notes for a procurement deck<\/strong>:<\/p>\n<ul>\n<li>Think of accuracy claims as the visible tip of an iceberg; verification, suppression, refresh, and clear definitions sit below the surface and actually drive outcomes.<\/li>\n<li>A methodology flow diagram should show the path from NPI sample through matching to confidence buckets: confident, ambiguous, or no confident match.<\/li>\n<li>Ask any vendor for a column dictionary: field name, definition, source category, last-verified timestamp, and suppression flags.<\/li>\n<\/ul>\n<p><strong>Example schema columns (illustrative)<\/strong>:<\/p>\n<ul>\n<li><strong>npi<\/strong>: National Provider Identifier, when applicable.<\/li>\n<li><strong>full_name<\/strong>: normalized name used for matching.<\/li>\n<li><strong>taxonomy<\/strong>: specialty\/taxonomy code from NPPES, when applicable.<\/li>\n<li><strong>primary_practice_location<\/strong>: normalized location fields used for disambiguation.<\/li>\n<li><strong>email<\/strong>: email address, if present.<\/li>\n<li><strong>email_last_verified_at<\/strong>: timestamp of the last verification event, if available.<\/li>\n<li><strong>phone<\/strong>: phone number, if present.<\/li>\n<li><strong>suppression_flags<\/strong>: opt-out, bounce, complaint, and internal DNC indicators.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Weighted_checklist\"><\/span>Weighted checklist<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A procurement-friendly way to evaluate contact data methodology. Score each item 0-2 and weight it so the conversation stays grounded in workflow fit rather than marketing language.<\/p>\n<div class=\"table-scroll\" style=\"overflow:auto;-webkit-overflow-scrolling:touch;width:100%\">\n<table class=\"separated-content\">\n<thead>\n<tr>\n<th>Category<\/th>\n<th>Item<\/th>\n<th>Weight<\/th>\n<th>Score (0-2)<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Definitions<\/td>\n<td>Publishes metric definitions (deliverability\/connect\/answer\/reply\/bounce) with denominators<\/td>\n<td>5<\/td>\n<td><\/td>\n<td>See definitions page<\/td>\n<\/tr>\n<tr>\n<td>Testing<\/td>\n<td>Documents how contact data quality is tested and how failures are handled<\/td>\n<td>5<\/td>\n<td><\/td>\n<td>Ask for test design and limits<\/td>\n<\/tr>\n<tr>\n<td>Identity<\/td>\n<td>Uses stable identifiers, such as NPI via NPPES, where applicable<\/td>\n<td>4<\/td>\n<td><\/td>\n<td>Reduces wrong-person outreach<\/td>\n<\/tr>\n<tr>\n<td>Suppression<\/td>\n<td>Supports opt-out, bounce, complaint, and internal DNC suppression<\/td>\n<td>5<\/td>\n<td><\/td>\n<td>Protects compliance and deliverability<\/td>\n<\/tr>\n<tr>\n<td>Acceptable use<\/td>\n<td>Clear acceptable use policy aligned with recruiting outreach<\/td>\n<td>4<\/td>\n<td><\/td>\n<td>References FCC TCPA \/ FTC CAN-SPAM boundaries<\/td>\n<\/tr>\n<tr>\n<td>Updates<\/td>\n<td>Every page shows last reviewed plus a published corrections\/update policy<\/td>\n<td>3<\/td>\n<td><\/td>\n<td>Prevents stale guidance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"Outreach_templates\"><\/span>Outreach templates<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>These assume legitimate recruiting outreach, opt-outs honored, and operation under your organization&#8217;s interpretation of <strong>FCC TCPA<\/strong> and <strong>FTC CAN-SPAM<\/strong>. Have compliance review before use.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Email_template_initial\"><\/span>Email template (initial)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Subject:<\/strong> Quick question about your next role<\/p>\n<p>Hi {{FirstName}},<\/p>\n<p>I recruit clinicians in {{Specialty\/ServiceLine}}. Are you open to a brief call this week to see if {{Role\/Location}} is worth a look?<\/p>\n<p>If you&#8217;re not the right person for this message, tell me and I&#8217;ll update my records. If you&#8217;d rather not receive outreach from me, reply &#8220;opt out&#8221; and I&#8217;ll stop.<\/p>\n<p>\u2014 {{YourName}}, {{Title}}<\/p>\n<p>{{Company}}<\/p>\n<p>{{Phone}}<\/p>\n<h3><span class=\"ez-toc-section\" id=\"SMS_template_only_where_permitted_by_your_policy_and_applicable_law\"><\/span>SMS template (only where permitted by your policy and applicable law)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Hi {{FirstName}}\u2014{{YourName}} here. Recruiting for {{Role}} in {{Location}}. Open to a 5-min call? Reply STOP to opt out.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Voicemail_template\"><\/span>Voicemail template<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Hi {{FirstName}}, this is {{YourName}}. I&#8217;m recruiting for a {{Role}} opportunity in {{Location}}. If you&#8217;re open to a quick conversation, call me at {{Phone}}. If not, text or email me &#8220;opt out&#8221; and I&#8217;ll update my list.<\/p>\n<p><strong>Operational note<\/strong>: keep templates stable for a consistent test window, then compare outcomes by template version using the metric definitions below. Mixing changes mid-test makes results unreadable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_pitfalls\"><\/span>Common pitfalls<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Chasing a single &#8220;accuracy&#8221; number<\/strong>: if a vendor can&#8217;t define metrics and denominators, outcomes can&#8217;t be managed.<\/li>\n<li><strong>Ignoring suppression<\/strong>: opt-outs and bounces aren&#8217;t just compliance issues; they directly affect deliverability and recruiter time.<\/li>\n<li><strong>Confusing identity with contactability<\/strong>: a correct NPI match doesn&#8217;t guarantee an email delivers or a phone connects.<\/li>\n<li><strong>Overstating platform coverage<\/strong>: avoid uncited coverage percentages and require a reproducible method with confidence thresholds. Reference: <a href=\"https:\/\/www.linkedin.com\/legal\/user-agreement\">LinkedIn User Agreement<\/a>.<\/li>\n<li><strong>Letting trust pages rot<\/strong>: no last-reviewed date and no corrections policy is a sign the methodology is stale.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"How_to_improve_results\"><\/span>How to improve results<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Metric_definitions_canonical\"><\/span>Metric definitions (canonical)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>These are the definitions used across Heartbeat.ai trust content. When comparing vendors, force everyone onto the same denominators.<\/p>\n<ul>\n<li><strong>Deliverability Rate<\/strong> = delivered emails \/ sent emails, per 100 sent emails.<\/li>\n<li><strong>Bounce Rate<\/strong> = bounced emails \/ sent emails, per 100 sent emails.<\/li>\n<li><strong>Reply Rate<\/strong> = replies \/ delivered emails, per 100 delivered emails.<\/li>\n<li><strong>Connect Rate<\/strong> = connected calls \/ total dials, per 100 dials.<\/li>\n<li><strong>Answer Rate<\/strong> = human answers \/ connected calls, per 100 connected calls.<\/li>\n<\/ul>\n<p>Go deeper: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/accuracy-and-metrics-definitions\/\">Definitions<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Measuring_this_in_your_own_stack\"><\/span>Measuring this in your own stack<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>Email<\/strong>: export sends, deliveries, bounces, and replies by campaign and domain. Compute Deliverability Rate, Bounce Rate, and Reply Rate from the definitions above.<\/li>\n<li><strong>Phone<\/strong>: log total dials, connected calls, and human answers. Compute Connect Rate and Answer Rate from the definitions above.<\/li>\n<li><strong>Suppression<\/strong>: track opt-out events and the timestamp each was applied. Audit that suppressed contacts aren&#8217;t reintroduced on refresh.<\/li>\n<li><strong>Identity QA<\/strong>: sample &#8220;wrong person&#8221; replies and trace them back to matching logic, whether it&#8217;s an NPI\/NPPES anchor issue, a name collision, or a practice change.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Glossary\"><\/span>Glossary<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>Confidence threshold<\/strong>: the minimum score or criteria required to treat a match as confident for operational use.<\/li>\n<li><strong>No confident match<\/strong>: a record that didn&#8217;t meet the confidence threshold and shouldn&#8217;t be treated as verified for that identity.<\/li>\n<li><strong>Suppression<\/strong>: rules and lists that prevent outreach to contacts who bounced, opted out, complained, or are on an internal DNC list.<\/li>\n<li><strong>Refresh cadence<\/strong>: how often verification, scoring, and suppression are re-run to account for decay.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Legal_and_ethical_use\"><\/span>Legal and ethical use<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Heartbeat.ai supports legitimate recruiting outreach. We publish methodology and guidance, not legal advice. Your organization is responsible for implementing policies consistent with applicable laws, including the <strong>Telephone Consumer Protection Act (TCPA)<\/strong> for calling and texting practices and the <strong>CAN-SPAM Act<\/strong> for commercial email rules.<\/p>\n<p>Minimum standard we expect in any outreach workflow:<\/p>\n<ul>\n<li>Honor opt-out requests quickly and consistently across channels.<\/li>\n<li>Maintain suppression lists and don&#8217;t re-add suppressed contacts on refresh.<\/li>\n<li>Don&#8217;t attempt to bypass platform Terms of Service. Reference: <a href=\"https:\/\/www.linkedin.com\/legal\/user-agreement\">LinkedIn User Agreement<\/a>.<\/li>\n<li>Don&#8217;t use Heartbeat.ai for patient data or clinical decision-making. See <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/not-hipaa-no-patient-data\/\">Not HIPAA \/ no patient data<\/a>.<\/li>\n<\/ul>\n<p>Go deeper: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/data-ethics-acceptable-use\/\">Data ethics &amp; acceptable use<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Evidence_and_trust_notes\"><\/span>Evidence and trust notes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This page is the sitewide trust anchor: linkable, auditable, and updated. Supporting pages:<\/p>\n<ul>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/how-we-test-contact-data-quality\/\">How we test contact data quality<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/accuracy-and-metrics-definitions\/\">Accuracy and metrics definitions<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/data-sources-we-use\/\">Data sources we use<\/a><\/li>\n<li><a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/editorial-policy\/\">Editorial policy<\/a> and <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/corrections-update-policy\/\">Corrections &amp; update policy<\/a><\/li>\n<\/ul>\n<p><strong>External references<\/strong>:<\/p>\n<ul>\n<li><a href=\"https:\/\/nppes.cms.hhs.gov\/\">NPPES NPI Registry<\/a><\/li>\n<li><a href=\"https:\/\/www.cms.gov\/medicare\/regulations-guidance\/administrative-simplification\/national-provider-identifier-npi\">CMS: National Provider Identifier (NPI)<\/a><\/li>\n<li><a href=\"https:\/\/www.linkedin.com\/legal\/user-agreement\">LinkedIn User Agreement<\/a><\/li>\n<\/ul>\n<p><strong>Data we do not collect<\/strong>: patient records, clinical notes, diagnoses, or any patient-identifying medical information. This is provider and professional contact methodology only. See <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/not-hipaa-no-patient-data\/\">Not HIPAA \/ no patient data<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"What_does_%E2%80%9Ccontact_data_methodology%E2%80%9D_mean_in_recruiting\"><\/span>What does &#8220;contact data methodology&#8221; mean in recruiting?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>It&#8217;s the documented process for how contact records are sourced, matched to identity (often via NPI\/NPPES for providers), verified, scored, suppressed, and refreshed, plus the definitions used to measure outcomes.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_do_you_define_deliverability_and_connectability\"><\/span>How do you define deliverability and connectability?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Deliverability is Deliverability Rate = delivered emails \/ sent emails, per 100 sent emails. Connectability is Connect Rate = connected calls \/ total dials, per 100 dials. Full <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/accuracy-and-metrics-definitions\/\">definitions<\/a> are published separately.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Do_you_store_patient_data_or_claim_HIPAA_status\"><\/span>Do you store patient data or claim HIPAA status?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>No. Heartbeat.ai is not a patient-data product and doesn&#8217;t store patient data. See <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/not-hipaa-no-patient-data\/\">Not HIPAA \/ no patient data<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_do_you_handle_acceptable_use_and_opt-outs\"><\/span>How do you handle acceptable use and opt-outs?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>We expect legitimate recruiting outreach only, with clear opt-out handling and suppression so contacts aren&#8217;t reintroduced on refresh. Read our <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/data-ethics-acceptable-use\/\">acceptable use<\/a> guidance and align it with your counsel&#8217;s interpretation of TCPA and CAN-SPAM.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_often_is_this_hub_updated\"><\/span>How often is this hub updated?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Each trust page shows a last-reviewed date, and we maintain a corrections and update policy so methodology stays current. See <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/corrections-update-policy\/\">Corrections &amp; update policy<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Next_steps\"><\/span>Next steps<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>If you&#8217;re evaluating quality claims, start with <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/how-we-test-contact-data-quality\/\">how we test<\/a> and the <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/accuracy-and-metrics-definitions\/\">definitions<\/a>.<\/li>\n<li>If you&#8217;re building a vendor scorecard, copy the weighted checklist above and require written answers.<\/li>\n<li>If you want to see Heartbeat.ai in your workflow, create an account: <a href=\"https:\/\/heartbeat.ai\/signup\">sign up for Heartbeat.ai<\/a>.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"About_the_Author\"><\/span><b>About the Author<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a href=\"http:\/\/heartbeat.ai\/resources\/author\/ben-argeband\"><span style=\"font-weight: 400;\">Ben Argeband<\/span><\/a><span style=\"font-weight: 400;\"> is the Founder and CEO of Swordfish.ai and Heartbeat.ai. With deep expertise in data and SaaS, he has built two platforms used by sales and recruitment professionals. Ben&#8217;s focus is helping teams find direct contact information for hard-to-reach professionals and decision-makers. Connect with Ben on <\/span><a href=\"https:\/\/www.linkedin.com\/in\/ben-m-argeband-2427a8a3\/\"><span style=\"font-weight: 400;\">LinkedIn<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><br \/>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"Article\",\"about\":[\"contact data methodology\",\"data verification methodology\",\"accuracy methodology\",\"editorial standards\",\"update cadence\"],\"author\":{\"@type\":\"Person\",\"jobTitle\":\"Founder & CEO of Heartbeat.ai\",\"name\":\"Ben Argeband\"},\"dateModified\":\"2026-01-05\",\"datePublished\":\"2026-01-05\",\"headline\":\"Contact Data Methodology (Trust & Methodology Hub)\",\"isPartOf\":{\"@type\":\"WebSite\",\"name\":\"Heartbeat.ai\",\"url\":\"https:\/\/heartbeat.ai\/\"},\"mainEntityOfPage\":{\"@id\":\"https:\/\/heartbeat.ai\/resources\/trust-methodology\/\",\"@type\":\"WebPage\"},\"publisher\":{\"@type\":\"Organization\",\"name\":\"Heartbeat.ai\"}}<\/script><\/p>\n<p><script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"It's the documented process for how contact records are sourced, matched to identity (often via NPI\/NPPES for providers), verified, scored, suppressed, and refreshed\u2014plus the definitions used to measure outcomes.\"},\"name\":\"What does \\\"contact data methodology\\\" mean in recruiting?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Deliverability is Deliverability Rate = delivered emails \/ sent emails (per 100 sent emails). 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