| blob | b2606b843b63a21ab83624ac96bd43fedc434360 |
1 /**
2 * @package OpenEMR CCDAServer
3 * @link http://www.open-emr.org
4 *
5 * @author Jerry Padgett <sjpadgett@gmail.com>
6 * @copyright Copyright (c) 2016-2022 Jerry Padgett <sjpadgett@gmail.com>
7 * @license https://github.com/openemr/openemr/blob/master/LICENSE GNU General Public License 3
8 */
10 "use strict";
12 const enableDebug = true;
14 const net = require('net');
15 const server = net.createServer();
16 const to_json = require('xmljson').to_json;
17 const bbg = require(__dirname + '/oe-blue-button-generate');
18 const fs = require('fs');
19 const { DataStack } = require('./data-stack/data-stack');
20 const { cleanCode } = require('./utils/clean-code/clean-code');
21 const { safeTrim } = require('./utils/safe-trim/safe-trim');
22 const { headReplace } = require('./utils/head-replace/head-replace');
23 const { fDate, templateDate } = require('./utils/date/date');
24 const { countEntities } = require('./utils/count-entities/count-entities');
26 var conn = ''; // make our connection scope global to script
27 var oidFacility = "";
28 var all = "";
29 var npiProvider = "";
30 var npiFacility = "";
31 var webRoot = "";
32 var authorDateTime = '';
33 var documentLocation = '';
35 // do a recursive descent transformation of the node object populating the timezone offset value if we have
36 // a precision property (inside a date) with the value of timezone.
37 function populateTimezones(node, tzOffset, depthCheck) {
38 if (!node || typeof node !== 'object') {
39 return node;
40 }
41 // we should NEVER go farther than 25 recursive loops down in our heirarchy, if we do it means we have an infinite loop
42 if (depthCheck > 25) {
43 console.error("Max depth traversal reached. Potential infinite loop. Breaking out of loop")
44 return node;
45 }
47 if (Object.prototype.hasOwnProperty.call(node, 'precision') && node.precision == 'tz' && !Object.prototype.hasOwnProperty.call(node, 'timezoneOffset')) {
48 node.timezoneOffset = tzOffset;
49 } else {
50 for (const [key, value] of Object.entries(node)) {
51 node[key] = populateTimezones(value, tzOffset, depthCheck + 1);
52 }
53 }
54 return node;
55 }
57 function fetchPreviousAddresses(pd) {
58 let addressArray = [];
59 let pa = pd.previous_addresses.address;
60 let streetLine = [pd.street[0]];
61 if (pd.street[1].length > 0) {
62 streetLine = [pd.street[0], pd.street[1]];
63 }
64 addressArray.push({
65 "use": "HP",
66 "street_lines": streetLine,
67 "city": pd.city,
68 "state": pd.state,
69 "zip": pd.postalCode,
70 "country": pd.country || "US",
71 "date_time": {
72 // use current date for current residence
73 "low": {
74 "date": fDate(""),
75 "precision": "day"
76 }
77 }
78 });
79 let count = countEntities(pa);
80 // how do we ever get here where we just have one object?
81 if (count === 1) {
82 streetLine = [pa.street[0]];
83 if (pa.street[1].length > 0) {
84 streetLine = [pa.street[0], pa.street[1]];
85 }
86 addressArray.push({
87 "use": pa.use,
88 "street_lines": streetLine,
89 "city": pa.city,
90 "state": pa.state,
91 "zip": pa.postalCode,
92 "country": pa.country || "US",
93 "date_time": {
94 "low": {
95 "date": fDate(pa.period_start),
96 "precision": "day"
97 },
98 "high": {
99 "date": fDate(pa.period_end) || fDate(""),
100 "precision": "day"
101 }
102 }
103 });
104 } else if (count > 1) {
105 for (let i in pa) {
106 streetLine = [pa[i].street[0]];
107 if (pa[i].street[1].length > 0) {
108 streetLine = [pa[i].street[0], pa[i].street[1]];
109 }
110 addressArray.push({
111 "use": pa[i].use,
112 "street_lines": streetLine,
113 "city": pa[i].city,
114 "state": pa[i].state,
115 "zip": pa[i].postalCode,
116 "country": pa[i].country || "US",
117 "date_time": {
118 "low": {
119 "date": fDate(pa[i].period_start),
120 "precision": "day"
121 },
122 "high": {
123 "date": fDate(pa[i].period_end) || fDate(""),
124 "precision": "day"
125 }
126 }
127 });
128 }
129 }
130 return addressArray;
131 }
133 function populateDemographic(pd, g) {
134 let first = 'NI';
135 let middle = 'NI';
136 let last = 'NI';
137 const names = g.display_name.split(' ');
138 if (names.length === 2) {
139 first = names[0];
140 last = names[1];
141 }
142 if (names.length === 3) {
143 first = names[0];
144 last = names[2];
145 }
146 let guardian = [{
147 "relation": g.relation,
148 "addresses": [{
149 "street_lines": [g.address],
150 "city": g.city,
151 "state": g.state,
152 "zip": g.postalCode,
153 "country": g.country || "US",
154 "use": "primary home"
155 }],
156 "names": [{
157 "last": last,
158 "first": first
159 }],
160 "phone": [{
161 "number": g.telecom,
162 "type": "primary home"
163 }]
164 }];
165 if (pd.race === 'Declined To Specify' || pd.race === '') {
166 pd.race = "null_flavor";
167 }
168 if (pd.race_group === 'Declined To Specify' || pd.race_group === '') {
169 pd.race_group = "null_flavor";
170 }
171 if (pd.ethnicity === 'Declined To Specify' || pd.ethnicity === '') {
172 pd.ethnicity = "null_flavor";
173 }
174 let addressArray = fetchPreviousAddresses(pd);
175 return {
176 "name": {
177 "prefix": pd.prefix,
178 "suffix": pd.suffix,
179 "middle": [pd.mname] || "",
180 "last": pd.lname,
181 "first": pd.fname
182 },
183 "birth_name": {
184 "middle": pd.birth_mname || "",
185 "last": pd.birth_lname || "",
186 "first": pd.birth_fname || ""
187 },
188 "dob": {
189 "point": {
190 "date": fDate(pd.dob),
191 "precision": "day"
192 }
193 },
194 "gender": pd.gender.toUpperCase() || "null_flavor",
195 "identifiers": [{
196 "identifier": oidFacility || npiFacility,
197 "extension": pd.uuid
198 }],
199 "marital_status": pd.status.toUpperCase(),
200 "addresses": addressArray,
201 "phone": [
202 {
203 "number": pd.phone_home,
204 "type": "primary home"
205 }, {
206 "number": pd.phone_mobile,
207 "type": "primary mobile"
208 }, {
209 "number": pd.phone_work,
210 "type": "work place"
211 }, {
212 "number": pd.phone_emergency,
213 "type": "emergency contact"
214 },{
215 "email": pd.email,
216 "type": "contact_email"
217 }
218 ],
219 "ethnicity": pd.ethnicity || "",
220 "race": pd.race || "null_flavor",
221 "race_additional": pd.race_group || "null_flavor",
222 "languages": [{
223 "language": pd.language === 'English' ? "en-US" : pd.language === 'Spanish' ? "sp-US" : 'en-US',
224 "preferred": true,
225 "mode": "Expressed spoken",
226 "proficiency": "Good"
227 }],
228 //"religion": pd.religion.toUpperCase() || "",
229 /*"birthplace":'', {
230 "city": "",
231 "state": "",
232 "zip": "",
233 "country": ""
234 },*/
235 "attributed_provider": {
236 "identity": [
237 {
238 "root": "2.16.840.1.113883.4.6",
239 "extension": npiFacility || ""
240 }
241 ],
242 "phone": [{
243 "number": all.encounter_provider.facility_phone || "",
244 }],
245 "name": [
246 {
247 "full": all.encounter_provider.facility_name || ""
248 }
249 ],
250 "address": [
251 {
252 "street_lines": [
253 all.encounter_provider.facility_street
254 ],
255 "city": all.encounter_provider.facility_city,
256 "state": all.encounter_provider.facility_state,
257 "zip": all.encounter_provider.facility_postal_code,
258 "country": all.encounter_provider.facility_country_code || "US",
259 "use": "work place"
260 }
261 ],
262 },
263 "guardians": g.display_name ? guardian : '' //not required
264 }
265 }
267 function populateProvider(provider) {
268 // The provider role is a maybe and will only be provided for physicians as a
269 // primary care role. All other team members will id via taxonomy only and if not physicians.
270 return {
271 "function_code": provider.physician_type ? "PP" : "",
272 "date_time": {
273 "low": {
274 "date": provider.provider_since ? fDate(provider.provider_since) : fDate(""),
275 "precision": "tz"
276 }
277 },
278 "identity": [
279 {
280 "root": provider.npi ? "2.16.840.1.113883.4.6" : oidFacility,
281 "extension": provider.npi || provider.table_id || "NI"
282 }
283 ],
284 "type": [
285 {
286 "name": provider.taxonomy_description || "",
287 "code": cleanCode(provider.taxonomy) || "",
288 "code_system": "2.16.840.1.113883.6.101",
289 "code_system_name": "NUCC Health Care Provider Taxonomy"
290 }
291 ],
292 "name": [
293 {
294 "last": provider.lname || "",
295 "first": provider.fname || ""
296 }
297 ],
298 "address": [
299 {
300 "street_lines": [
301 all.encounter_provider.facility_street
302 ],
303 "city": all.encounter_provider.facility_city,
304 "state": all.encounter_provider.facility_state,
305 "zip": all.encounter_provider.facility_postal_code,
306 "country": all.encounter_provider.facility_country_code || "US"
307 }
308 ],
310 "phone": [{
311 "number": all.encounter_provider.facility_phone || ""
312 }]
313 }
314 }
316 function populateProviders(all) {
317 let providerArray = [];
318 // primary provider
319 let provider = populateProvider(all.primary_care_provider.provider);
320 providerArray.push(provider);
321 let count = countEntities(all.care_team.provider);
322 if (count === 1) {
323 provider = populateProvider(all.care_team.provider);
324 providerArray.push(provider);
325 } else if (count > 1) {
326 for (let i in all.care_team.provider) {
327 provider = populateProvider(all.care_team.provider[i]);
328 providerArray.push(provider);
329 }
330 }
331 return {
332 "providers":
333 {
334 "date_time": {
335 "low": {
336 "date": fDate(all.time_start) || fDate(""),
337 "precision": "tz"
338 },
339 "high": {
340 "date": fDate(all.time_end) || fDate(""),
341 "precision": "tz"
342 }
343 },
344 "code": {
345 "name": all.primary_diagnosis.text || "",
346 "code": cleanCode(all.primary_diagnosis.code || ""),
347 "code_system_name": all.primary_diagnosis.code_type || ""
348 },
349 "provider": providerArray,
350 }
351 }
352 }
355 function populateCareTeamMember(provider) {
356 return {
357 //"function_code": provider.physician_type ? "PP" : "",
358 "function_code": {
359 "xmlns": "urn:hl7-org:sdtc",
360 "name": provider.taxonomy_description || "",
361 "code": cleanCode(provider.taxonomy) || "",
362 "code_system": "2.16.840.1.113883.6.101",
363 "code_system_name": "NUCC Health Care Provider Taxonomy"
364 },
365 "status": "active",
366 "date_time": {
367 "low": {
368 "date": fDate(provider.provider_since) || fDate(""),
369 "precision": "tz"
370 }
371 },
372 "identifiers": [
373 {
374 "identifier": provider.npi ? "2.16.840.1.113883.4.6" : oidFacility,
375 "extension": provider.npi || provider.table_id
376 }
377 ],
378 "full_name": provider.fname + " " + provider.lname,
379 "name": {
380 "last": provider.lname || "",
381 "first": provider.fname || ""
382 },
383 "address": {
384 "street_lines": [
385 provider.street
386 ],
387 "city": provider.city,
388 "state": provider.state,
389 "zip": provider.zip,
390 "country": all.encounter_provider.facility_country_code || "US"
391 },
392 "phone": [
393 {
394 "number": provider.telecom,
395 "type": "work place"
396 }
397 ]
398 }
399 }
401 function populateAuthorFromAuthorContainer(pd) {
402 let author = pd.author || {};
403 return {
404 "code": {
405 "name": author.physician_type || '',
406 "code": author.physician_type_code || '',
407 "code_system": author.physician_type_system,
408 "code_system_name": author.physician_type_system_name
409 },
410 "date_time": {
411 "point": {
412 "date": fDate(author.time),
413 "precision": "tz"
414 }
415 },
416 "identifiers": [
417 {
418 "identifier": author.npi ? "2.16.840.1.113883.4.6" : author.id,
419 "extension": author.npi ? author.npi : 'NI'
420 }
421 ],
422 "name": [
423 {
424 "last": author.lname || "",
425 "first": author.fname || ""
426 }
427 ],
428 "organization": [
429 {
430 "identity": [
431 {
432 "root": author.facility_oid || "2.16.840.1.113883.4.6",
433 "extension": author.facility_npi || "NI"
434 }
435 ],
436 "name": [
437 author.facility_name || ""
438 ]
439 }
440 ]
441 };
442 }
444 function populateCareTeamMembers(pd) {
445 let providerArray = [];
446 // primary provider
447 let primaryCareProvider = pd.primary_care_provider || {provider: {}};
448 let providerSince = fDate(primaryCareProvider.provider.provider_since || '');
449 if (pd.primary_care_provider) {
450 let provider = populateCareTeamMember(pd.primary_care_provider.provider);
451 providerArray.push(provider);
452 let count = countEntities(pd.care_team.provider);
453 if (count === 1) {
454 provider = populateCareTeamMember(pd.care_team.provider);
455 providerSince = providerSince || fDate(provider.provider_since);
456 providerArray.push(provider);
457 } else if (count > 1) {
458 for (let i in pd.care_team.provider) {
459 provider = populateCareTeamMember(pd.care_team.provider[i]);
460 providerSince = providerSince || fDate(provider.provider_since);
461 providerArray.push(provider);
462 }
463 }
464 }
465 return {
466 "providers":
467 {
468 "provider": providerArray,
469 },
470 "status": "active",
471 "date_time": {
472 "low": {
473 "date": providerSince || fDate(""),
474 "precision": "tz"
475 }
476 },
477 // we treat this author a bit differently since we are working at the main pd object instead of the sub pd.care_team
478 "author": populateAuthorFromAuthorContainer(pd.care_team)
479 }
480 }
482 function populateMedication(pd) {
483 pd.status = 'Completed'; //@todo invoke prescribed
484 return {
485 "date_time": {
486 "low": {
487 "date": fDate(pd.start_date),
488 "precision": "tz"
489 }/*,
490 "high": {
491 "date": fDate(pd.end_date),
492 "precision": "day"
493 }*/
494 },
495 "identifiers": [{
496 "identifier": pd.sha_extension,
497 "extension": pd.extension || ""
498 }],
499 "status": pd.status,
500 "sig": pd.direction,
501 "product": {
502 "identifiers": [{
503 "identifier": pd.sha_extension || "2a620155-9d11-439e-92b3-5d9815ff4ee8",
504 "extension": pd.extension + 1 || ""
505 }],
506 "unencoded_name": pd.drug,
507 "product": {
508 "name": pd.drug,
509 "code": cleanCode(pd.rxnorm),
510 "code_system_name": "RXNORM"
511 /*"translations": [{
512 "name": pd.drug,
513 "code": pd.rxnorm,
514 "code_system_name": "RXNORM"
515 }],*/
516 },
517 //"manufacturer": ""
518 },
519 "author": {
520 "code": {
521 "name": pd.author.physician_type || '',
522 "code": pd.author.physician_type_code || '',
523 "code_system": pd.author.physician_type_system, "code_system_name": pd.author.physician_type_system_name
524 },
525 "date_time": {
526 "point": {
527 "date": fDate(pd.author.time),
528 "precision": "tz"
529 }
530 },
531 "identifiers": [
532 {
533 "identifier": pd.author.npi ? "2.16.840.1.113883.4.6" : pd.author.id,
534 "extension": pd.author.npi ? pd.author.npi : 'NI'
535 }
536 ],
537 "name": [
538 {
539 "last": pd.author.lname,
540 "first": pd.author.fname
541 }
542 ],
543 "organization": [
544 {
545 "identity": [
546 {
547 "root": pd.author.facility_oid || "2.16.840.1.113883.4.6",
548 "extension": pd.author.facility_npi || "NI"
549 }
550 ],
551 "name": [
552 pd.author.facility_name
553 ]
554 }
555 ]
556 },
557 "supply": {
558 "date_time": {
559 "low": {
560 "date": fDate(pd.start_date),
561 "precision": "day"
562 },
563 "high": {
564 "date": fDate(pd.end_date),
565 "precision": "day"
566 }
567 },
568 "repeatNumber": "0",
569 "quantity": "0",
570 "product": {
571 "identifiers": [{
572 "identifier": pd.sha_extension || "2a620155-9d11-439e-92b3-5d9815ff4ee8",
573 "extension": pd.extension + 1 || ""
574 }],
575 "unencoded_name": pd.drug,
576 "product": {
577 "name": pd.drug,
578 "code": cleanCode(pd.rxnorm),
579 /*"translations": [{
580 "name": pd.drug,
581 "code": pd.rxnorm,
582 "code_system_name": "RXNORM"
583 }],*/
584 "code_system_name": "RXNORM"
585 },
586 //"manufacturer": ""
587 },
588 "author": {
589 "code": {
590 "name": all.author.physician_type || '',
591 "code": all.author.physician_type_code || '',
592 "code_system": all.author.physician_type_system, "code_system_name": all.author.physician_type_system_name
593 },
594 "date_time": {
595 "point": {
596 "date": authorDateTime,
597 "precision": "tz"
598 }
599 },
600 "identifiers": [
601 {
602 "identifier": all.author.npi ? "2.16.840.1.113883.4.6" : all.author.id,
603 "extension": all.author.npi ? all.author.npi : 'NI'
604 }
605 ],
606 "name": [
607 {
608 "last": all.author.lname,
609 "first": all.author.fname
610 }
611 ],
612 "organization": [
613 {
614 "identity": [
615 {
616 "root": oidFacility || "2.16.840.1.113883.4.6",
617 "extension": npiFacility || ""
618 }
619 ],
620 "name": [
621 all.encounter_provider.facility_name
622 ]
623 }
624 ]
625 },
626 "instructions": {
627 "code": {
628 "name": "instruction",
629 "code": "409073007",
630 "code_system_name": "SNOMED CT"
631 },
632 "free_text": pd.instructions || "No Instructions"
633 },
634 },
635 "administration": {
636 "route": {
637 "name": pd.route || "",
638 "code": cleanCode(pd.route_code) || "",
639 "code_system_name": "Medication Route FDA"
640 },
641 "form": {
642 "name": pd.form,
643 "code": cleanCode(pd.form_code),
644 "code_system_name": "Medication Route FDA"
645 },
646 "dose": {
647 "value": parseFloat(pd.size),
648 "unit": pd.unit,
649 },
650 /*"rate": {
651 "value": parseFloat(pd.dosage),
652 "unit": ""
653 },*/
654 "interval": {
655 "period": {
656 "value": parseFloat(pd.dosage),
657 "unit": pd.interval
658 },
659 "frequency": true
660 }
661 },
662 "performer": {
663 "identifiers": [{
664 "identifier": "2.16.840.1.113883.4.6",
665 "extension": pd.npi || ""
666 }],
667 "organization": [{
668 "identifiers": [{
669 "identifier": pd.sha_extension,
670 "extension": pd.extension || ""
671 }],
672 "name": [pd.performer_name]
673 }]
674 },
675 "drug_vehicle": {
676 "name": pd.form,
677 "code": cleanCode(pd.form_code),
678 "code_system_name": "RXNORM"
679 },
680 /*"precondition": {
681 "code": {
682 "code": "ASSERTION",
683 "code_system_name": "ActCode"
684 },
685 "value": {
686 "name": "none",
687 "code": "none",
688 "code_system_name": "SNOMED CT"
689 }
690 },
691 "indication": {
692 "identifiers": [{
693 "identifier": "db734647-fc99-424c-a864-7e3cda82e703",
694 "extension": "45665"
695 }],
696 "code": {
697 "name": "Finding",
698 "code": "404684003",
699 "code_system_name": "SNOMED CT"
700 },
701 "date_time": {
702 "low": {
703 "date": fDate(pd.start_date),
704 "precision": "day"
705 }
706 },
707 "value": {
708 "name": pd.indications,
709 "code": pd.indications_code,
710 "code_system_name": "SNOMED CT"
711 }
712 },
713 "dispense": {
714 "identifiers": [{
715 "identifier": "1.2.3.4.56789.1",
716 "extension": "cb734647-fc99-424c-a864-7e3cda82e704"
717 }],
718 "performer": {
719 "identifiers": [{
720 "identifier": "2.16.840.1.113883.19.5.9999.456",
721 "extension": "2981823"
722 }],
723 "address": [{
724 "street_lines": [pd.address],
725 "city": pd.city,
726 "state": pd.state,
727 "zip": pd.zip,
728 "country": "US"
729 }],
730 "organization": [{
731 "identifiers": [{
732 "identifier": "2.16.840.1.113883.19.5.9999.1393"
733 }],
734 "name": [pd.performer_name]
735 }]
736 },
737 "product": {
738 "identifiers": [{
739 "identifier": "2a620155-9d11-439e-92b3-5d9815ff4ee8"
740 }],
741 "unencoded_name": pd.drug,
742 "product": {
743 "name": pd.drug,
744 "code": pd.rxnorm,
745 "translations": [{
746 "name": pd.drug,
747 "code": pd.rxnorm,
748 "code_system_name": "RXNORM"
749 }],
750 "code_system_name": "RXNORM"
751 },
752 "manufacturer": ""
753 }
754 }*/
755 };
756 }
758 function getFinding(pd, problem) {
759 const finding = {
760 "identifiers": [{
761 "identifier": pd.sha_extension,
762 "extension": ''
763 }],
764 "value": {
765 "name": '',
766 "code": '',
767 "code_system_name": ''
768 },
769 "date_time": {
770 "low": {
771 "date": '',
772 "precision": "day"
773 }
774 },
775 "status": '',
776 "reason": pd.encounter_reason,
777 "author": {
778 "code": {
779 "name": all.author.physician_type || '',
780 "code": all.author.physician_type_code || '',
781 "code_system": all.author.physician_type_system, "code_system_name": all.author.physician_type_system_name
782 },
783 "date_time": {
784 "point": {
785 "date": authorDateTime,
786 "precision": "tz"
787 }
788 },
789 "identifiers": [
790 {
791 "identifier": all.author.npi ? "2.16.840.1.113883.4.6" : all.author.id,
792 "extension": all.author.npi ? all.author.npi : 'UNK'
793 }
794 ],
795 "name": [
796 {
797 "last": all.author.lname,
798 "first": all.author.fname
799 }
800 ],
801 "organization": [
802 {
803 "identity": [
804 {
805 "root": oidFacility || "2.16.840.1.113883.4.6",
806 "extension": npiFacility || ""
807 }
808 ],
809 "name": [
810 all.encounter_provider.facility_name
811 ]
812 }
813 ]
814 },
815 };
817 finding.identifiers["0"].extension = problem.extension;
818 finding.date_time.low.date = fDate(problem.date);
819 finding.value.name = problem.text;
820 finding.value.code = cleanCode(problem.code);
821 finding.value.code_system_name = problem.code_type;
822 finding.status = problem.status;
823 return finding;
824 }
826 function populateEncounter(pd) {
827 // just to get diagnosis. for findings..
828 let findingObj = [];
829 let theone = {};
830 let count = 0;
831 try {
832 count = countEntities(pd.encounter_problems.problem);
833 } catch (e) {
834 count = 0;
835 }
836 if (count > 1) {
837 for (let i in pd.encounter_problems.problem) {
838 theone[i] = getFinding(pd, pd.encounter_problems.problem[i]);
839 findingObj.push(theone[i]);
840 }
841 } else if (count !== 0 && pd.encounter_problems.problem.code > '') {
842 let finding = getFinding(pd, pd.encounter_problems.problem);
843 findingObj.push(finding);
844 }
846 return {
847 "encounter": {
848 "name": pd.visit_category ? (pd.visit_category + " | " + pd.encounter_reason) : pd.code_description,
849 "code": pd.code || "185347001",
850 //"code_system": "2.16.840.1.113883.6.96",
851 "code_system_name": pd.code_type || "SNOMED CT",
852 "translations": [{
853 "name": "Ambulatory",
854 "code": "AMB",
855 "code_system_name": "ActCode"
856 }]
857 },
858 "identifiers": [{
859 "identifier": pd.sha_extension,
860 "extension": pd.extension
861 }],
862 "date_time": {
863 "point": {
864 "date": fDate(pd.date),
865 "precision": "tz"
866 }
867 },
868 "performers": [{
869 "identifiers": [{
870 "identifier": "2.16.840.1.113883.4.6",
871 "extension": pd.npi || ""
872 }],
873 "code": [{
874 "name": pd.physician_type,
875 "code": cleanCode(pd.physician_type_code),
876 "code_system_name": pd.physician_code_type
877 }],
878 "name": [
879 {
880 "last": pd.lname || "",
881 "first": pd.fname || ""
882 }
883 ],
884 "phone": [
885 {
886 "number": pd.work_phone,
887 "type": "work place"
888 }
889 ]
890 }],
891 "locations": [{
892 "name": pd.location,
893 "location_type": {
894 "name": pd.location_details,
895 "code": "1160-1",
896 "code_system_name": "HealthcareServiceLocation"
897 },
898 "address": [{
899 "street_lines": [pd.facility_address],
900 "city": pd.facility_city,
901 "state": pd.facility_state,
902 "zip": pd.facility_zip,
903 "country": pd.facility_country || "US"
904 }],
905 "phone": [
906 {
907 "number": pd.facility_phone,
908 "type": "work place"
909 }
910 ]
911 }],
912 "findings": findingObj
913 };
914 }
916 function populateAllergy(pd) {
918 if (!pd) {
919 return {
920 "no_know_allergies": "No Known Allergies",
921 "date_time": {
922 "low": templateDate("", "day"),
923 //"high": templateDate(pd.enddate, "day")
924 }
925 }
926 }
927 let allergyAuthor = {
928 "code": {
929 "name": pd.author.physician_type || '',
930 "code": pd.author.physician_type_code || '',
931 "code_system": pd.author.physician_type_system, "code_system_name": pd.author.physician_type_system_name
932 },
933 "date_time": {
934 "point": {
935 "date": fDate(pd.author.time),
936 "precision": "tz"
937 }
938 },
939 "identifiers": [
940 {
941 "identifier": pd.author.npi ? "2.16.840.1.113883.4.6" : pd.author.id,
942 "extension": pd.author.npi ? pd.author.npi : 'NI'
943 }
944 ],
945 "name": [
946 {
947 "last": pd.author.lname,
948 "first": pd.author.fname
949 }
950 ],
951 "organization": [
952 {
953 "identity": [
954 {
955 "root": pd.author.facility_oid || "2.16.840.1.113883.4.6",
956 "extension": pd.author.facility_npi || "NI"
957 }
958 ],
959 "name": [
960 pd.author.facility_name
961 ]
962 }
963 ]
964 };
966 return {
967 "identifiers": [{
968 "identifier": pd.sha_id,
969 "extension": pd.id || ""
970 }],
971 "date_time": {
972 "low": templateDate(pd.startdate, "day"),
973 //"high": templateDate(pd.enddate, "day")
974 },
975 "author": allergyAuthor,
976 "observation": {
977 "identifiers": [{
978 "identifier": pd.sha_extension || "2a620155-9d11-439e-92b3-5d9815ff4ee8",
979 "extension": pd.id + 1 || ""
980 }],
981 "author": allergyAuthor,
982 "allergen": {
983 "name": pd.title || "",
984 "code": pd.rxnorm_code_text ? cleanCode(pd.rxnorm_code) : pd.snomed_code_text ? cleanCode(pd.snomed_code) : cleanCode(""),
985 "code_system_name": pd.rxnorm_code_text ? "RXNORM" : pd.snomed_code_text ? "SNOMED CT" : ""
986 },
987 "date_time": {
988 "low": {
989 "date": fDate(pd.startdate) || fDate(""),
990 "precision": "day"
991 }
992 },
993 "intolerance": {
994 "name": "Propensity to adverse reactions to drug",
995 "code": "420134006",
996 "code_system_name": "SNOMED CT"
997 },
998 "severity": {
999 "code": {
1000 "name": pd.outcome || "",
1001 "code": cleanCode(pd.outcome_code) || "",
1002 "code_system_name": "SNOMED CT"
1003 }
1004 },
1005 "status": {
1006 "name": pd.status_table || "",
1007 "code": cleanCode(pd.status_code),
1008 "code_system_name": "SNOMED CT"
1009 },
1010 "reactions": [{
1011 "identifiers": [{
1012 "identifier": "4adc1020-7b14-11db-9fe1-0800200c9a64"
1013 }],
1014 "date_time": {
1015 "low": templateDate(pd.startdate, "day"),
1016 "high": templateDate(pd.enddate, "day")
1017 },
1018 "reaction": {
1019 "name": pd.reaction_text,
1020 "code": cleanCode(pd.reaction_code) || "",
1021 "code_system_name": pd.reaction_code_type || "SNOMED CT"
1022 },
1023 "severity": {
1024 "code": {
1025 "name": pd.outcome || "",
1026 "code": cleanCode(pd.outcome_code),
1027 "code_system_name": "SNOMED CT"
1028 }
1029 }
1030 }]
1031 }
1032 }
1033 }
1035 function populateProblem(pd) {
1036 let primary_care_provider = all.primary_care_provider || {provider: {}};
1037 return {
1038 "date_time": {
1039 "low": {
1040 "date": fDate(pd.start_date_table),
1041 "precision": "day"
1042 },
1043 /*"high": {
1044 "date": fDate(pd.end_date),
1045 "precision": "day"
1046 }*/
1047 },
1048 "identifiers": [{
1049 "identifier": pd.sha_extension,
1050 "extension": pd.extension || ""
1051 }],
1052 "translations": [{
1053 "name": "Condition",
1054 "code": "75323-6",
1055 "code_system_name": "LOINC"
1056 }],
1057 "problem": {
1058 "code": {
1059 "name": safeTrim(pd.title),
1060 "code": cleanCode(pd.code),
1061 "code_system_name": safeTrim(pd.code_type)
1062 },
1063 "date_time": {
1064 "low": {
1065 "date": fDate(pd.start_date),
1066 "precision": "day"
1067 },
1068 /*"high": {
1069 "date": fDate(pd.end_date),
1070 "precision": getPrecision()
1071 }*/
1072 }
1073 },
1074 "author": {
1075 "code": {
1076 "name": pd.author.physician_type || '',
1077 "code": pd.author.physician_type_code || '',
1078 "code_system": pd.author.physician_type_system, "code_system_name": pd.author.physician_type_system_name
1079 },
1080 "date_time": {
1081 "point": {
1082 "date": fDate(pd.author.time),
1083 "precision": "tz"
1084 }
1085 },
1086 "identifiers": [
1087 {
1088 "identifier": pd.author.npi ? "2.16.840.1.113883.4.6" : pd.author.id,
1089 "extension": pd.author.npi ? pd.author.npi : 'NI'
1090 }
1091 ],
1092 "name": [
1093 {
1094 "last": pd.author.lname,
1095 "first": pd.author.fname
1096 }
1097 ],
1098 "organization": [
1099 {
1100 "identity": [
1101 {
1102 "root": pd.author.facility_oid || "2.16.840.1.113883.4.6",
1103 "extension": pd.author.facility_npi || "NI"
1104 }
1105 ],
1106 "name": [
1107 pd.author.facility_name
1108 ]
1109 }
1110 ]
1111 },
1112 "performer": [
1113 {
1114 "identifiers": [
1115 {
1116 "identifier": "2.16.840.1.113883.4.6",
1117 "extension": primary_care_provider.provider.npi || ""
1118 }
1119 ],
1120 "name": [
1121 {
1122 "last": primary_care_provider.provider.lname || "",
1123 "first": primary_care_provider.provider.fname || ""
1124 }
1125 ]
1126 }],
1127 "onset_age": pd.age,
1128 "onset_age_unit": "Year",
1129 "status": {
1130 "name": pd.status_table,
1131 "date_time": {
1132 "low": {
1133 "date": fDate(pd.start_date),
1134 "precision": "day"
1135 },
1136 /*"high": {
1137 "date": fDate(pd.end_date),
1138 "precision": getPrecision()
1139 }*/
1140 }
1141 },
1142 "patient_status": pd.observation,
1143 "source_list_identifiers": [{
1144 "identifier": pd.sha_extension,
1145 "extension": pd.extension || ""
1146 }]
1147 };
1149 }
1151 function populateProcedure(pd) {
1152 return {
1153 "procedure": {
1154 "name": pd.description,
1155 "code": cleanCode(pd.code),
1156 //"code_system": "2.16.840.1.113883.6.12",
1157 "code_system_name": pd.code_type
1158 },
1159 "identifiers": [{
1160 "identifier": "d68b7e32-7810-4f5b-9cc2-acd54b0fd85d",
1161 "extension": pd.extension
1162 }],
1163 "status": "completed",
1164 "date_time": {
1165 "point": {
1166 "date": fDate(pd.date),
1167 "precision": "day"
1168 }
1169 },
1170 /*"body_sites": [{
1171 "name": "",
1172 "code": "",
1173 "code_system_name": ""
1174 }],
1175 "specimen": {
1176 "identifiers": [{
1177 "identifier": "c2ee9ee9-ae31-4628-a919-fec1cbb58683"
1178 }],
1179 "code": {
1180 "name": "",
1181 "code": "",
1182 "code_system_name": "SNOMED CT"
1183 }
1184 },*/
1185 "performers": [{
1186 "identifiers": [{
1187 "identifier": "2.16.840.1.113883.4.6",
1188 "extension": pd.npi || ""
1189 }],
1190 "address": [{
1191 "street_lines": [pd.address],
1192 "city": pd.city,
1193 "state": pd.state,
1194 "zip": pd.zip,
1195 "country": "US"
1196 }],
1197 "phone": [{
1198 "number": pd.work_phone,
1199 "type": "work place"
1200 }],
1201 "organization": [{
1202 "identifiers": [{
1203 "identifier": pd.facility_sha_extension,
1204 "extension": pd.facility_extension
1205 }],
1206 "name": [pd.facility_name],
1207 "address": [{
1208 "street_lines": [pd.facility_address],
1209 "city": pd.facility_city,
1210 "state": pd.facility_state,
1211 "zip": pd.facility_zip,
1212 "country": pd.facility_country || "US"
1213 }],
1214 "phone": [{
1215 "number": pd.facility_phone,
1216 "type": "work place"
1217 }]
1218 }]
1219 }],
1220 "author": populateAuthorFromAuthorContainer(pd),
1221 "procedure_type": "procedure"
1222 };
1223 }
1225 function populateMedicalDevice(pd) {
1226 return {
1227 "identifiers": [{
1228 "identifier": pd.sha_extension,
1229 "extension": pd.extension
1230 }],
1231 "date_time": {
1232 "low": {
1233 "date": fDate(pd.start_date),
1234 "precision": "day"
1235 }/*,
1236 "high": {
1237 "date": fDate(pd.end_date),
1238 "precision": "day"
1239 }*/
1240 },
1241 "device_type": "UDI",
1242 "device": {
1243 "name": pd.code_text,
1244 "code": cleanCode(pd.code),
1245 "code_system_name": "SNOMED CT",
1246 "identifiers": [{
1247 "identifier": "2.16.840.1.113883.3.3719",
1248 "extension": pd.udi
1249 }],
1250 "status": "completed",
1251 "body_sites": [{
1252 "name": "",
1253 "code": "",
1254 "code_system_name": ""
1255 }],
1256 "udi": pd.udi
1257 },
1258 "author": {
1259 "code": {
1260 "name": pd.author.physician_type || '',
1261 "code": pd.author.physician_type_code || '',
1262 "code_system": pd.author.physician_type_system, "code_system_name": pd.author.physician_type_system_name
1263 },
1264 "date_time": {
1265 "point": {
1266 "date": fDate(pd.author.time),
1267 "precision": "tz"
1268 }
1269 },
1270 "identifiers": [
1271 {
1272 "identifier": pd.author.npi ? "2.16.840.1.113883.4.6" : pd.author.id,
1273 "extension": pd.author.npi ? pd.author.npi : 'NI'
1274 }
1275 ],
1276 "name": [
1277 {
1278 "last": pd.author.lname,
1279 "first": pd.author.fname
1280 }
1281 ],
1282 "organization": [
1283 {
1284 "identity": [
1285 {
1286 "root": pd.author.facility_oid || "2.16.840.1.113883.4.6",
1287 "extension": pd.author.facility_npi || "NI"
1288 }
1289 ],
1290 "name": [
1291 pd.author.facility_name
1292 ]
1293 }
1294 ]
1295 }
1296 }
1297 }
1299 function populateResult(pd) {
1300 let icode = pd.subtest.abnormal_flag;
1301 let value = parseFloat(pd.subtest.result_value) || pd.subtest.result_value || "";
1302 let type = isNaN(value) ? "ST" : "PQ";
1303 type = !pd.subtest.unit ? "ST" : type;
1304 value += "";
1305 let range_type = pd.subtest.range.toUpperCase() == "NEGATIVE" ? "CO" : type;
1306 type = value.toUpperCase() == "NEGATIVE" ? "CO" : type;
1308 switch (pd.subtest.abnormal_flag.toUpperCase()) {
1309 case "NO":
1310 icode = "Normal";
1311 break;
1312 case "YES":
1313 icode = "Abnormal";
1314 break;
1315 case "":
1316 icode = "";
1317 break;
1318 }
1319 let result = {
1320 "identifiers": [{
1321 "identifier": pd.subtest.root,
1322 "extension": pd.subtest.extension
1323 }],
1324 "result": {
1325 "name": pd.title,
1326 "code": cleanCode(pd.subtest.result_code) || "",
1327 "code_system_name": "LOINC"
1328 },
1329 "date_time": {
1330 "point": {
1331 "date": fDate(pd.date_ordered),
1332 "precision": "day"
1333 }
1334 },
1335 "status": pd.order_status,
1336 "reference_range": {
1337 "low": pd.subtest.low,
1338 "high": pd.subtest.high,
1339 "unit": pd.subtest.unit,
1340 "type": type,
1341 "range_type": range_type
1342 },
1343 "value": value + "",
1344 "unit": pd.subtest.unit,
1345 "type": type,
1346 "range": pd.subtest.range,
1347 "range_type": range_type
1348 };
1349 // interpretation cannot be an empty value so we skip it if it is
1350 // empty as Observation.interpretationCode is [0..*]
1351 if (icode !== "") {
1352 result["interpretations"] = [icode];
1353 }
1354 return result;
1355 }
1357 function getResultSet(results) {
1359 if (!results) return '';
1361 // not sure if the result set should be grouped better on the backend as the author information needs to be more nuanced here
1362 let tResult = results.result[0] || results.result;
1363 let resultSet = {
1364 "identifiers": [{
1365 "identifier": tResult.root,
1366 "extension": tResult.extension
1367 }],
1368 "author": populateAuthorFromAuthorContainer(tResult),
1369 "result_set": {
1370 "name": tResult.test_name,
1371 "code": cleanCode(tResult.test_code),
1372 "code_system_name": "LOINC"
1373 }
1374 };
1375 let rs = [];
1376 let many = [];
1377 let theone = {};
1378 let count = 0;
1379 many.results = [];
1380 try {
1381 count = countEntities(results.result);
1382 } catch (e) {
1383 count = 0;
1384 }
1385 if (count > 1) {
1386 for (let i in results.result) {
1387 theone[i] = populateResult(results.result[i]);
1388 many.results.push(theone[i]);
1389 }
1390 } else if (count !== 0) {
1391 theone = populateResult(results.result);
1392 many.results.push(theone);
1393 }
1394 rs.results = Object.assign(resultSet);
1395 rs.results.results = Object.assign(many.results);
1396 return rs;
1397 }
1399 function getPlanOfCare(pd) {
1400 let name = '';
1401 let code = '';
1402 let code_system_name = "";
1403 let status = "Active";
1404 let one = true;
1405 let encounter;
1407 let planType = "observation";
1408 switch (pd.care_plan_type) {
1409 case 'plan_of_care':
1410 planType = "observation"; // mood code INT. sets code in template
1411 break;
1412 case 'test_or_order':
1413 planType = "observation"; // mood code RQO
1414 break;
1415 case 'procedure':
1416 planType = "procedure";
1417 break;
1418 case 'appointments':
1419 planType = "encounter";
1420 break;
1421 case 'instructions':
1422 planType = "instructions";
1423 break;
1424 case 'referral':
1425 planType = ""; // for now exclude. unsure how to template.
1426 break;
1427 default:
1428 planType = "observation";
1429 }
1430 if (pd.code_type === 'RXCUI') {
1431 pd.code_type = 'RXNORM';
1432 }
1433 if (pd.code_type === 'RXNORM') {
1434 planType = "substanceAdministration";
1435 }
1436 if (planType === "") {
1437 return false;
1438 }
1440 for (let key in all.encounter_list.encounter) {
1441 // skip loop if the property is from prototype
1442 if (!Object.prototype.hasOwnProperty.call(all.encounter_list.encounter, key)) {
1443 continue;
1444 }
1445 encounter = all.encounter_list.encounter[key];
1446 if (pd.encounter == encounter.encounter_id) {
1447 one = false;
1448 name = encounter.encounter_diagnosis.text;
1449 code = cleanCode(encounter.encounter_diagnosis.code);
1450 code_system_name = encounter.encounter_diagnosis.code_type;
1451 status = encounter.encounter_diagnosis.status;
1452 encounter = all.encounter_list.encounter[key]; // to be sure.
1453 break;
1454 }
1455 }
1456 if (one) {
1457 let value = "";
1458 if (all.encounter_list && all.encounter_list.encounter && all.encounter_list.encounter.encounter_diagnosis) {
1459 value = all.encounter_list.encounter.encounter_diagnosis;
1460 }
1461 name = value.text;
1462 code = cleanCode(value.code);
1463 code_system_name = value.code_type;
1464 status = value.status;
1465 encounter = all.encounter_list.encounter;
1466 }
1468 return {
1469 "plan": {
1470 "name": pd.code_text || "",
1471 "code": cleanCode(pd.code) || "",
1472 "code_system_name": pd.code_type || "SNOMED CT"
1473 },
1474 "identifiers": [{
1475 "identifier": pd.sha_extension,
1476 "extension": pd.extension || ""
1477 }],
1478 "goal": {
1479 "code": cleanCode(pd.code) || "",
1480 "name": safeTrim(pd.description) || ""
1481 },
1482 "date_time": {
1483 "point": {
1484 "date": fDate(pd.date),
1485 "precision": "day"
1486 }
1487 },
1488 "type": planType,
1489 "status": {
1490 "code": cleanCode(pd.status)
1491 },
1492 "author": populateAuthorFromAuthorContainer(pd),
1493 "performers": [{
1494 "identifiers": [{
1495 "identifier": "2.16.840.1.113883.4.6",
1496 "extension": encounter.npi || ""
1497 }],
1498 "code": [{
1499 "name": encounter.physician_type,
1500 "code": cleanCode(encounter.physician_type_code),
1501 "code_system_name": "SNOMED CT"
1502 }],
1503 "name": [
1504 {
1505 "last": encounter.lname || "",
1506 "first": encounter.fname || ""
1507 }
1508 ],
1509 "phone": [
1510 {
1511 "number": encounter.work_phone,
1512 "type": "work place"
1513 }
1514 ]
1515 }],
1516 "locations": [{
1517 "name": encounter.location,
1518 "location_type": {
1519 "name": encounter.location_details,
1520 "code": "1160-1",
1521 "code_system_name": "HealthcareServiceLocation"
1522 },
1523 "address": [{
1524 "street_lines": [encounter.facility_address],
1525 "city": encounter.facility_city,
1526 "state": encounter.facility_state,
1527 "zip": encounter.facility_zip,
1528 "country": encounter.facility_country || "US"
1529 }],
1530 "phone": [
1531 {
1532 "number": encounter.facility_phone,
1533 "type": "work place"
1534 }
1535 ]
1536 }],
1537 "findings": [{
1538 "identifiers": [{
1539 "identifier": encounter.sha_extension,
1540 "extension": encounter.extension
1541 }],
1542 "value": {
1543 "name": name,
1544 "code": code,
1545 "code_system_name": code_system_name
1546 },
1547 "date_time": {
1548 "low": {
1549 "date": fDate(encounter.date),
1550 "precision": "day"
1551 }
1552 },
1553 "status": status,
1554 "reason": encounter.encounter_reason
1555 }],
1556 "name": safeTrim(pd.description),
1557 "mood_code": pd.moodCode
1558 };
1559 }
1561 function getGoals(pd) {
1562 return {
1563 "goal_code": {
1564 "name": pd.code_text !== "NULL" ? pd.code_text : "",
1565 "code": cleanCode(pd.code) || "",
1566 "code_system_name": pd.code_type || ""
1567 },
1568 "identifiers": [{
1569 "identifier": pd.sha_extension,
1570 "extension": pd.extension,
1571 }],
1572 "date_time": {
1573 "point": {
1574 "date": fDate(pd.date),
1575 "precision": "day"
1576 }
1577 },
1578 "type": "observation",
1579 "status": {
1580 "code": "active", //cleanCode(pd.status)
1581 },
1582 "author": populateAuthorFromAuthorContainer(pd),
1583 "name": pd.description
1584 };
1585 }
1587 function getFunctionalStatus(pd) {
1588 let functionalStatusAuthor = {
1589 "code": {
1590 "name": all.author.physician_type || '',
1591 "code": all.author.physician_type_code || '',
1592 "code_system": all.author.physician_type_system, "code_system_name": all.author.physician_type_system_name
1593 },
1594 "date_time": {
1595 "point": {
1596 "date": authorDateTime,
1597 "precision": "tz"
1598 }
1599 },
1600 "identifiers": [
1601 {
1602 "identifier": all.author.npi ? "2.16.840.1.113883.4.6" : all.author.id,
1603 "extension": all.author.npi ? all.author.npi : 'NI'
1604 }
1605 ],
1606 "name": [
1607 {
1608 "last": all.author.lname,
1609 "first": all.author.fname
1610 }
1611 ],
1612 "organization": [
1613 {
1614 "identity": [
1615 {
1616 "root": oidFacility || "2.16.840.1.113883.4.6",
1617 "extension": npiFacility || ""
1618 }
1619 ],
1620 "name": [
1621 all.encounter_provider.facility_name
1622 ]
1623 }
1624 ]
1625 };
1627 return {
1628 "status": "completed",
1629 "author": functionalStatusAuthor,
1630 "identifiers": [{
1631 "identifier": "9a6d1bac-17d3-4195-89a4-1121bc809000",
1632 "extension": pd.extension || '',
1633 }],
1635 "observation": {
1636 "value": {
1637 "name": pd.code_text !== "NULL" ? safeTrim(pd.code_text) : "",
1638 "code": cleanCode(pd.code) || "",
1639 "code_system_name": pd.code_type || "SNOMED-CT"
1640 },
1641 "identifiers": [{
1642 "identifier": "9a6d1bac-17d3-4195-89a4-1121bc8090ab",
1643 "extension": pd.extension || '',
1644 }],
1645 "date_time": {
1646 "point": {
1647 "date": fDate(pd.date),
1648 "precision": "day"
1649 }
1650 },
1651 "status": "completed",
1652 "author": functionalStatusAuthor
1653 }
1654 };
1655 }
1657 function getMentalStatus(pd) {
1658 return {
1659 "value": {
1660 "name": pd.code_text !== "NULL" ? pd.code_text : "",
1661 "code": cleanCode(pd.code) || "",
1662 "code_system_name": pd.code_type || ""
1663 },
1664 "identifiers": [{
1665 "identifier": "9a6d1bac-17d3-4195-89a4-1121bc809ccc",
1666 "extension": pd.extension,
1667 }],
1668 "note": safeTrim(pd.description),
1669 "date_time": {
1670 "low": templateDate(pd.date, "day")
1671 //"high": templateDate(pd.date, "day")
1672 },
1673 "author": {
1674 "code": {
1675 "name": all.author.physician_type || '',
1676 "code": all.author.physician_type_code || '',
1677 "code_system": all.author.physician_type_system, "code_system_name": all.author.physician_type_system_name
1678 },
1679 "date_time": {
1680 "point": {
1681 "date": authorDateTime,
1682 "precision": "tz"
1683 }
1684 },
1685 "identifiers": [
1686 {
1687 "identifier": all.author.npi ? "2.16.840.1.113883.4.6" : all.author.id,
1688 "extension": all.author.npi ? all.author.npi : 'NI'
1689 }
1690 ],
1691 "name": [
1692 {
1693 "last": all.author.lname,
1694 "first": all.author.fname
1695 }
1696 ],
1697 "organization": [
1698 {
1699 "identity": [
1700 {
1701 "root": oidFacility || "2.16.840.1.113883.4.6",
1702 "extension": npiFacility || ""
1703 }
1704 ],
1705 "name": [
1706 all.encounter_provider.facility_name
1707 ]
1708 }
1709 ]
1710 }
1711 };
1712 }
1714 function getAssessments(pd) {
1715 return {
1716 "description": safeTrim(pd.description),
1717 "author": populateAuthorFromAuthorContainer(pd)
1718 };
1719 }
1721 function getHealthConcerns(pd) {
1722 let one = true;
1723 let issue_uuid;
1724 let problems = [], problem = {};
1725 if (countEntities(pd.issues.issue_uuid) !== 0) {
1726 for (let key in pd.issues.issue_uuid) {
1727 issue_uuid = pd.issues.issue_uuid[key];
1728 if (issue_uuid) {
1729 one = false;
1730 }
1731 problem = {
1732 "identifiers": [{
1733 "identifier": issue_uuid
1734 }]
1735 };
1736 problems.push(problem);
1737 }
1738 }
1739 if (one) {
1740 if (pd.issues.issue_uuid) {
1741 problem = {
1742 "identifiers": [{
1743 "identifier": pd.issues.issue_uuid
1744 }]
1745 };
1746 problems.push(problem);
1747 }
1748 }
1749 return {
1750 // todo need to make array of health concerns
1751 "type": "act",
1752 "text": safeTrim(pd.text),
1753 "value": {
1754 "name": pd.code_text || "",
1755 "code": cleanCode(pd.code) || "",
1756 "code_system_name": pd.code_type || "SNOMED CT"
1757 },
1758 "author": populateAuthorFromAuthorContainer(pd),
1759 "identifiers": [{
1760 "identifier": pd.sha_extension,
1761 "extension": pd.extension,
1762 }],
1763 problems: problems
1764 }
1765 }
1767 function getReferralReason(pd) {
1768 return {
1769 "reason": safeTrim(pd.text),
1770 "author": populateAuthorFromAuthorContainer(pd)
1771 };
1772 }
1774 function populateVital(pd) {
1775 return {
1776 "identifiers": [{
1777 "identifier": pd.sha_extension,
1778 "extension": pd.extension
1779 }],
1780 "status": "completed",
1781 "date_time": {
1782 "point": {
1783 "date": fDate(pd.effectivetime),
1784 "precision": "day"
1785 }
1786 },
1787 // our list of vitals per organizer.
1788 "vital_list": [{
1789 "identifiers": [{
1790 "identifier": pd.sha_extension,
1791 "extension": pd.extension_bps
1792 }],
1793 "vital": {
1794 "name": "Blood Pressure Systolic",
1795 "code": "8480-6",
1796 "code_system_name": "LOINC"
1797 },
1798 "status": "completed",
1799 "date_time": {
1800 "point": {
1801 "date": fDate(pd.effectivetime),
1802 "precision": "day"
1803 }
1804 },
1805 "interpretations": ["Normal"],
1806 "value": parseFloat(pd.bps) || pd.bps,
1807 "unit": "mm[Hg]",
1808 "author": populateAuthorFromAuthorContainer(pd),
1809 }, {
1810 "identifiers": [{
1811 "identifier": pd.sha_extension,
1812 "extension": pd.extension_bpd
1813 }],
1814 "vital": {
1815 "name": "Blood Pressure Diastolic",
1816 "code": "8462-4",
1817 "code_system_name": "LOINC"
1818 },
1819 "status": "completed",
1820 "date_time": {
1821 "point": {
1822 "date": fDate(pd.effectivetime),
1823 "precision": "day"
1824 }
1825 },
1826 "interpretations": ["Normal"],
1827 "value": parseFloat(pd.bpd) || pd.bpd,
1828 "unit": "mm[Hg]",
1829 "author": populateAuthorFromAuthorContainer(pd),
1830 }, {
1831 "identifiers": [{
1832 "identifier": pd.sha_extension,
1833 "extension": pd.extension_height
1834 }],
1835 "vital": {
1836 "name": "Height",
1837 "code": "8302-2",
1838 "code_system_name": "LOINC"
1839 },
1840 "status": "completed",
1841 "date_time": {
1842 "point": {
1843 "date": fDate(pd.effectivetime),
1844 "precision": "day"
1845 }
1846 },
1847 "interpretations": ["Normal"],
1848 "value": parseFloat(pd.height) || pd.height,
1849 "unit": pd.unit_height,
1850 "author": populateAuthorFromAuthorContainer(pd),
1851 }, {
1852 "identifiers": [{
1853 "identifier": pd.sha_extension,
1854 "extension": pd.extension_weight
1855 }],
1856 "vital": {
1857 "name": "Weight Measured",
1858 "code": "29463-7",
1859 "code_system_name": "LOINC"
1860 },
1861 "status": "completed",
1862 "date_time": {
1863 "point": {
1864 "date": fDate(pd.effectivetime),
1865 "precision": "day"
1866 }
1867 },
1868 "interpretations": ["Normal"],
1869 "value": parseFloat(pd.weight) || "",
1870 "unit": pd.unit_weight,
1871 "author": populateAuthorFromAuthorContainer(pd),
1872 }, {
1873 "identifiers": [{
1874 "identifier": pd.sha_extension,
1875 "extension": pd.extension_BMI
1876 }],
1877 "vital": {
1878 "name": "BMI (Body Mass Index)",
1879 "code": "39156-5",
1880 "code_system_name": "LOINC"
1881 },
1882 "status": "completed",
1883 "date_time": {
1884 "point": {
1885 "date": fDate(pd.effectivetime),
1886 "precision": "day"
1887 }
1888 },
1889 "interpretations": [pd.BMI_status == 'Overweight' ? 'High' : pd.BMI_status == 'Overweight' ? 'Low' : 'Normal'],
1890 "value": parseFloat(pd.BMI) || "",
1891 "unit": "kg/m2",
1892 "author": populateAuthorFromAuthorContainer(pd),
1893 }, {
1894 "identifiers": [{
1895 "identifier": pd.sha_extension,
1896 "extension": pd.extension_pulse
1897 }],
1898 "vital": {
1899 "name": "Heart Rate",
1900 "code": "8867-4",
1901 "code_system_name": "LOINC"
1902 },
1903 "status": "completed",
1904 "date_time": {
1905 "point": {
1906 "date": fDate(pd.effectivetime),
1907 "precision": "day"
1908 }
1909 },
1910 "interpretations": ["Normal"],
1911 "value": parseFloat(pd.pulse) || "",
1912 "unit": "/min",
1913 "author": populateAuthorFromAuthorContainer(pd),
1914 }, {
1915 "identifiers": [{
1916 "identifier": "2.16.840.1.113883.3.140.1.0.6.10.14.2",
1917 "extension": pd.extension_breath
1918 }],
1919 "vital": {
1920 "name": "Respiratory Rate",
1921 "code": "9279-1",
1922 "code_system_name": "LOINC"
1923 },
1924 "status": "completed",
1925 "date_time": {
1926 "point": {
1927 "date": fDate(pd.effectivetime),
1928 "precision": "day"
1929 }
1930 },
1931 "interpretations": ["Normal"],
1932 "value": parseFloat(pd.breath) || "",
1933 "unit": "/min",
1934 "author": populateAuthorFromAuthorContainer(pd),
1935 }, {
1936 "identifiers": [{
1937 "identifier": "2.16.840.1.113883.3.140.1.0.6.10.14.3",
1938 "extension": pd.extension_temperature
1939 }],
1940 "vital": {
1941 "name": "Body Temperature",
1942 "code": "8310-5",
1943 "code_system_name": "LOINC"
1944 },
1945 "status": "completed",
1946 "date_time": {
1947 "point": {
1948 "date": fDate(pd.effectivetime),
1949 "precision": "day"
1950 }
1951 },
1952 "interpretations": ["Normal"],
1953 "value": parseFloat(pd.temperature) || "",
1954 "unit": pd.unit_temperature,
1955 "author": populateAuthorFromAuthorContainer(pd),
1956 }, {
1957 "identifiers": [{
1958 "identifier": pd.sha_extension,
1959 "extension": pd.extension_oxygen_saturation
1960 }],
1961 "vital": {
1962 "name": "O2 % BldC Oximetry",
1963 "code": "59408-5",
1964 "code_system_name": "LOINC"
1965 },
1966 "status": "completed",
1967 "date_time": {
1968 "point": {
1969 "date": fDate(pd.effectivetime),
1970 "precision": "day"
1971 }
1972 },
1973 "interpretations": ["Normal"],
1974 "value": parseFloat(pd.oxygen_saturation) || "",
1975 "unit": "%",
1976 "author": populateAuthorFromAuthorContainer(pd),
1977 }, {
1978 "identifiers": [{
1979 "identifier": pd.sha_extension,
1980 "extension": pd.extension_ped_weight_height
1981 }],
1982 "vital": { // --------------------------------------------------------------------------------
1983 "name": "Weight for Height Percentile",
1984 "code": "77606-2",
1985 "code_system_name": "LOINC"
1986 },
1987 "status": "completed",
1988 "date_time": {
1989 "point": {
1990 "date": fDate(pd.effectivetime),
1991 "precision": "day"
1992 }
1993 },
1994 "interpretations": ["Normal"],
1995 "value": parseFloat(pd.ped_weight_height) || "",
1996 "unit": "%",
1997 "author": populateAuthorFromAuthorContainer(pd),
1998 }, {
1999 "identifiers": [{
2000 "identifier": pd.sha_extension,
2001 "extension": pd.extension_inhaled_oxygen_concentration
2002 }],
2003 "vital": {
2004 "name": "Inhaled Oxygen Concentration",
2005 "code": "3150-0",
2006 "code_system_name": "LOINC"
2007 },
2008 "status": "completed",
2009 "date_time": {
2010 "point": {
2011 "date": fDate(pd.effectivetime),
2012 "precision": "day"
2013 }
2014 },
2015 "interpretations": ["Normal"],
2016 "value": parseFloat(pd.inhaled_oxygen_concentration) || "",
2017 "unit": "%",
2018 "author": populateAuthorFromAuthorContainer(pd),
2019 }, {
2020 "identifiers": [{
2021 "identifier": pd.sha_extension,
2022 "extension": pd.extension_ped_bmi
2023 }],
2024 "vital": {
2025 "name": "BMI Percentile",
2026 "code": "59576-9",
2027 "code_system_name": "LOINC"
2028 },
2029 "status": "completed",
2030 "date_time": {
2031 "point": {
2032 "date": fDate(pd.effectivetime),
2033 "precision": "day"
2034 }
2035 },
2036 "interpretations": ["Normal"],
2037 "value": parseFloat(pd.ped_bmi) || "",
2038 "unit": "%",
2039 "author": populateAuthorFromAuthorContainer(pd),
2040 }, {
2041 "identifiers": [{
2042 "identifier": pd.sha_extension,
2043 "extension": pd.extension_ped_head_circ
2044 }],
2045 "vital": {
2046 "name": "Head Occipital-frontal Circumference Percentile",
2047 "code": "8289-1",
2048 "code_system_name": "LOINC"
2049 },
2050 "status": "completed",
2051 "date_time": {
2052 "point": {
2053 "date": fDate(pd.effectivetime),
2054 "precision": "day"
2055 }
2056 },
2057 "interpretations": ["Normal"],
2058 "value": parseFloat(pd.ped_head_circ) || "",
2059 "unit": "%",
2060 "author": populateAuthorFromAuthorContainer(pd),
2061 }
2062 ]
2063 }
2064 }
2066 function populateSocialHistory(pd) {
2067 return {
2068 "date_time": {
2069 "low": templateDate(pd.date, "day")
2070 //"high": templateDate(pd.date, "day")
2071 },
2072 "identifiers": [{
2073 "identifier": pd.sha_extension,
2074 "extension": pd.extension
2075 }],
2076 "code": {
2077 "name": pd.code
2078 },
2079 "element": pd.element,
2080 "value": pd.description,
2081 "gender": all.patient.gender,
2082 "author": {
2083 "code": {
2084 "name": pd.author.physician_type || '',
2085 "code": pd.author.physician_type_code || '',
2086 "code_system": pd.author.physician_type_system, "code_system_name": pd.author.physician_type_system_name
2087 },
2088 "date_time": {
2089 "point": {
2090 "date": fDate(pd.author.time),
2091 "precision": "tz"
2092 }
2093 },
2094 "identifiers": [
2095 {
2096 "identifier": pd.author.npi ? "2.16.840.1.113883.4.6" : pd.author.id,
2097 "extension": pd.author.npi ? pd.author.npi : 'NI'
2098 }
2099 ],
2100 "name": [
2101 {
2102 "last": pd.author.lname,
2103 "first": pd.author.fname
2104 }
2105 ],
2106 "organization": [
2107 {
2108 "identity": [
2109 {
2110 "root": pd.author.facility_oid || "2.16.840.1.113883.4.6",
2111 "extension": pd.author.facility_npi || "NI"
2112 }
2113 ],
2114 "name": [
2115 pd.author.facility_name
2116 ]
2117 }
2118 ]
2119 }
2120 , "gender_author": {
2121 "code": {
2122 "name": all.patient.author.physician_type || '',
2123 "code": all.patient.author.physician_type_code || '',
2124 "code_system": all.patient.author.physician_type_system, "code_system_name": all.patient.author.physician_type_system_name
2125 },
2126 "date_time": {
2127 "point": {
2128 "date": fDate(all.patient.author.time),
2129 "precision": "tz"
2130 }
2131 },
2132 "identifiers": [
2133 {
2134 "identifier": all.patient.author.npi ? "2.16.840.1.113883.4.6" : all.patient.author.id,
2135 "extension": all.patient.author.npi ? all.patient.author.npi : 'NI'
2136 }
2137 ],
2138 "name": [
2139 {
2140 "last": all.patient.author.lname,
2141 "first": all.patient.author.fname
2142 }
2143 ],
2144 "organization": [
2145 {
2146 "identity": [
2147 {
2148 "root": all.patient.author.facility_oid || "2.16.840.1.113883.4.6",
2149 "extension": all.patient.author.facility_npi || "NI"
2150 }
2151 ],
2152 "name": [
2153 all.patient.author.facility_name
2154 ]
2155 }
2156 ]
2157 }
2158 };
2159 }
2161 function populateImmunization(pd) {
2162 return {
2163 "date_time": {
2164 "low": {
2165 "date": fDate(pd.administered_on),
2166 "precision": "day"
2167 }
2168 },
2169 "identifiers": [{
2170 "identifier": pd.sha_extension,
2171 "extension": pd.extension || ""
2172 }],
2173 "status": "complete",
2174 "product": {
2175 "product": {
2176 "name": pd.code_text,
2177 "code": cleanCode(pd.cvx_code),
2178 "code_system_name": "CVX"
2179 /*"translations": [{
2180 "name": "",
2181 "code": "",
2182 "code_system_name": "CVX"
2183 }]*/
2184 },
2185 "lot_number": "",
2186 "manufacturer": ""
2187 },
2188 "administration": {
2189 "route": {
2190 "name": pd.route_of_administration,
2191 "code": cleanCode(pd.route_code) || "",
2192 "code_system_name": "Medication Route FDA"
2193 }/*,
2194 "dose": {
2195 "value": 50,
2196 "unit": "mcg"
2197 }*/
2198 },
2199 "performer": {
2200 "identifiers": [{
2201 "identifier": "2.16.840.1.113883.4.6",
2202 "extension": pd.npi || ""
2203 }],
2204 "name": [{
2205 "last": pd.lname,
2206 "first": pd.fname
2207 }],
2208 "address": [{
2209 "street_lines": [pd.address],
2210 "city": pd.city,
2211 "state": pd.state,
2212 "zip": pd.zip,
2213 "country": "US"
2214 }],
2215 "organization": [{
2216 "identifiers": [{
2217 "identifier": "2.16.840.1.113883.4.6",
2218 "extension": npiFacility || ""
2219 }],
2220 "name": [pd.facility_name]
2221 }]
2222 },
2223 "instructions": {
2224 "code": {
2225 "name": "immunization education",
2226 "code": "171044003",
2227 "code_system_name": "SNOMED CT"
2228 },
2229 "free_text": "Needs Attention for more data."
2230 },
2231 "author": {
2232 "code": {
2233 "name": pd.author.physician_type || '',
2234 "code": pd.author.physician_type_code || '',
2235 "code_system": pd.author.physician_type_system, "code_system_name": pd.author.physician_type_system_name
2236 },
2237 "date_time": {
2238 "point": {
2239 "date": fDate(pd.author.time),
2240 "precision": "tz"
2241 }
2242 },
2243 "identifiers": [
2244 {
2245 "identifier": pd.author.npi ? "2.16.840.1.113883.4.6" : pd.author.id,
2246 "extension": pd.author.npi ? pd.author.npi : 'NI'
2247 }
2248 ],
2249 "name": [
2250 {
2251 "last": pd.author.lname,
2252 "first": pd.author.fname
2253 }
2254 ],
2255 "organization": [
2256 {
2257 "identity": [
2258 {
2259 "root": pd.author.facility_oid || "2.16.840.1.113883.4.6",
2260 "extension": pd.author.facility_npi || "NI"
2261 }
2262 ],
2263 "name": [
2264 pd.author.facility_name
2265 ]
2266 }
2267 ]
2268 }
2269 };
2270 }
2272 function populatePayer(pd) {
2273 return {
2274 "identifiers": [{
2275 "identifier": "1fe2cdd0-7aad-11db-9fe1-0800200c9a66"
2276 }],
2277 "policy": {
2278 "identifiers": [{
2279 "identifier": "3e676a50-7aac-11db-9fe1-0800200c9a66"
2280 }],
2281 "code": {
2282 "code": "SELF",
2283 "code_system_name": "HL7 RoleCode"
2284 },
2285 "insurance": {
2286 "code": {
2287 "code": "PAYOR",
2288 "code_system_name": "HL7 RoleCode"
2289 },
2290 "performer": {
2291 "identifiers": [{
2292 "identifier": "2.16.840.1.113883.19"
2293 }],
2294 "address": [{
2295 "street_lines": ["123 Insurance Road"],
2296 "city": "Blue Bell",
2297 "state": "MA",
2298 "zip": "02368",
2299 "country": "US",
2300 "use": "work place"
2301 }],
2302 "phone": [{
2303 "number": "(781)555-1515",
2304 "type": "work place"
2305 }],
2306 "organization": [{
2307 "name": ["Good Health Insurance"],
2308 "address": [{
2309 "street_lines": ["123 Insurance Road"],
2310 "city": "Blue Bell",
2311 "state": "MA",
2312 "zip": "02368",
2313 "country": "US",
2314 "use": "work place"
2315 }],
2316 "phone": [{
2317 "number": "(781)555-1515",
2318 "type": "work place"
2319 }]
2320 }],
2321 "code": [{
2322 "code": "PAYOR",
2323 "code_system_name": "HL7 RoleCode"
2324 }]
2325 }
2326 }
2327 },
2328 "guarantor": {
2329 "code": {
2330 "code": "GUAR",
2331 "code_system_name": "HL7 Role"
2332 },
2333 "identifiers": [{
2334 "identifier": "329fcdf0-7ab3-11db-9fe1-0800200c9a66"
2335 }],
2336 "name": [{
2337 "prefix": "Mr.",
2338 "middle": ["Frankie"],
2339 "last": "Everyman",
2340 "first": "Adam"
2341 }],
2342 "address": [{
2343 "street_lines": ["17 Daws Rd."],
2344 "city": "Blue Bell",
2345 "state": "MA",
2346 "zip": "02368",
2347 "country": "US",
2348 "use": "primary home"
2349 }],
2350 "phone": [{
2351 "number": "(781)555-1212",
2352 "type": "primary home"
2353 }]
2354 },
2355 "participant": {
2356 "code": {
2357 "name": "Self",
2358 "code": "SELF",
2359 "code_system_name": "HL7 Role"
2360 },
2361 "performer": {
2362 "identifiers": [{
2363 "identifier": "14d4a520-7aae-11db-9fe1-0800200c9a66",
2364 "extension": "1138345"
2365 }],
2366 "address": [{
2367 "street_lines": ["17 Daws Rd."],
2368 "city": "Blue Bell",
2369 "state": "MA",
2370 "zip": "02368",
2371 "country": "US",
2372 "use": "primary home"
2373 }],
2374 "code": [{
2375 "name": "Self",
2376 "code": "SELF",
2377 "code_system_name": "HL7 Role"
2378 }]
2379 },
2380 "name": [{
2381 "prefix": "Mr.",
2382 "middle": ["A."],
2383 "last": "Everyman",
2384 "first": "Frank"
2385 }]
2386 },
2387 "policy_holder": {
2388 "performer": {
2389 "identifiers": [{
2390 "identifier": "2.16.840.1.113883.19",
2391 "extension": "1138345"
2392 }],
2393 "address": [{
2394 "street_lines": ["17 Daws Rd."],
2395 "city": "Blue Bell",
2396 "state": "MA",
2397 "zip": "02368",
2398 "country": "US",
2399 "use": "primary home"
2400 }]
2401 }
2402 },
2403 "authorization": {
2404 "identifiers": [{
2405 "identifier": "f4dce790-8328-11db-9fe1-0800200c9a66"
2406 }],
2407 "procedure": {
2408 "code": {
2409 "name": "Colonoscopy",
2410 "code": "73761001",
2411 "code_system_name": "SNOMED CT"
2412 }
2413 }
2414 }
2415 };
2416 }
2418 function populateNote(pd) {
2419 return {
2420 "date_time": {
2421 "point": {
2422 "date": fDate(pd.date),
2423 "precision": "day"
2424 }
2425 },
2426 "translations": {
2427 code_system: "2.16.840.1.113883.6.1",
2428 code_system_name: "LOINC",
2429 code: cleanCode(pd.code),
2430 name: pd.code_text || ""
2431 },
2432 "author": populateAuthorFromAuthorContainer(pd),
2433 "note": safeTrim(pd.description),
2434 };
2435 }
2437 function populateParticipant(participant) {
2438 return {
2439 "name": {
2440 "prefix": participant.prefix || "",
2441 "suffix": participant.suffix || "",
2442 "middle": [participant.mname] || "",
2443 "last": participant.lname || "",
2444 "first": participant.fname || ""
2445 },
2446 "typeCode": participant.type || "",
2447 "classCode": "ASSIGNED",
2448 "code": {
2449 "name": participant.organization_taxonomy_description || "",
2450 "code": cleanCode(participant.organization_taxonomy) || "",
2451 "code_system": "2.16.840.1.113883.6.101",
2452 "code_system_name": "NUCC Health Care Provider Taxonomy"
2453 },
2454 "identifiers": [{
2455 "identifier": participant.organization_npi ? "2.16.840.1.113883.4.6" : participant.organization_id,
2456 "extension": participant.organization_npi ? participant.organization_npi : ''
2457 }],
2458 "date_time": {
2459 "point": {
2460 "date": participant.date_time,
2461 "precision": "tz"
2462 }
2463 },
2464 "phone": [
2465 {
2466 "number": participant.phonew1 || "",
2467 "type": "WP"
2468 }
2469 ],
2470 "address": [
2471 {
2472 "street_lines": [
2473 participant.street
2474 ],
2475 "city": participant.city,
2476 "state": participant.state,
2477 "zip": participant.postalCode,
2478 "country": participant.country || "US",
2479 "use": participant.address_use || "WP"
2480 }
2481 ],
2482 }
2483 }
2485 function populateHeader(pd) {
2486 // default doc type ToC CCD
2487 let name = "Summarization of Episode Note";
2488 let docCode = "34133-9";
2489 let docOid = "2.16.840.1.113883.10.20.22.1.2";
2490 if (pd.doc_type == 'referral') {
2491 name = "Referral Note";
2492 docCode = "57133-1";
2493 docOid = "2.16.840.1.113883.10.20.22.1.14";
2494 }
2496 if (pd.doc_type == 'unstructured') {
2497 name = "Patient Documents";
2498 docCode = "34133-9";
2499 docOid = "2.16.840.1.113883.10.20.22.1.10";
2500 }
2502 const head = {
2503 "identifiers": [
2504 {
2505 "identifier": oidFacility,
2506 "extension": "123456"
2507 }
2508 ],
2509 "code": {
2510 "name": name,
2511 "code": docCode,
2512 "code_system_name": "LOINC"
2513 },
2514 "template": {
2515 "root": docOid,
2516 "extension": "2015-08-01"
2517 },
2518 "title": name,
2519 "date_time": {
2520 "point": {
2521 "date": fDate(pd.created_time_timezone),
2522 "precision": "tz"
2523 }
2524 },
2525 "author": {
2526 "code": {
2527 "name": all.author.physician_type || '',
2528 "code": all.author.physician_type_code || '',
2529 "code_system": all.author.physician_type_system, "code_system_name": all.author.physician_type_system_name
2530 },
2531 "date_time": {
2532 "point": {
2533 "date": authorDateTime,
2534 "precision": "tz"
2535 }
2536 },
2537 "identifiers": [
2538 {
2539 "identifier": all.author.npi ? "2.16.840.1.113883.4.6" : all.author.id,
2540 "extension": all.author.npi ? all.author.npi : 'NI'
2541 }
2542 ],
2543 "name": [
2544 {
2545 "last": all.author.lname,
2546 "first": all.author.fname
2547 }
2548 ],
2549 "address": [
2550 {
2551 "street_lines": [
2552 all.author.streetAddressLine
2553 ],
2554 "city": all.author.city,
2555 "state": all.author.state,
2556 "zip": all.author.postalCode,
2557 "country": all.author.country || "US",
2558 "use": "work place"
2559 }
2560 ],
2561 "phone": [
2562 {
2563 "number": all.author.telecom || "",
2564 "type": "WP"
2565 }
2566 ],
2567 "organization": [
2568 {
2569 "identity": [
2570 {
2571 "root": oidFacility || "2.16.840.1.113883.4.6",
2572 "extension": npiFacility || ""
2573 }
2574 ],
2575 "name": [
2576 all.encounter_provider.facility_name
2577 ],
2578 "address": [
2579 {
2580 "street_lines": [
2581 all.encounter_provider.facility_street
2582 ],
2583 "city": all.encounter_provider.facility_city,
2584 "state": all.encounter_provider.facility_state,
2585 "zip": all.encounter_provider.facility_postal_code,
2586 "country": all.encounter_provider.facility_country_code || "US",
2587 "use": "work place"
2588 }
2589 ],
2590 "phone": [
2591 {
2592 "number": all.encounter_provider.facility_phone,
2593 "type": "work primary"
2594 }
2595 ]
2596 }
2597 ]
2598 },
2599 "custodian": {
2600 "identity": [
2601 {
2602 "root": "2.16.840.1.113883.4.6",
2603 "extension": npiFacility || ""
2604 }
2605 ],
2606 "name": [
2607 pd.custodian.organization || pd.custodian.name
2608 ],
2609 "address": [
2610 {
2611 "street_lines": [
2612 pd.custodian.streetAddressLine
2613 ],
2614 "city": pd.custodian.city,
2615 "state": pd.custodian.state,
2616 "zip": pd.custodian.postalCode,
2617 "country": pd.custodian.country || "US"
2618 }
2619 ],
2620 "phone": [
2621 {
2622 "number": pd.custodian.telecom,
2623 "type": "work primary"
2624 }
2625 ]
2626 },
2627 "information_recipient": {
2628 "name": {
2629 "prefix": pd.information_recipient.prefix || "",
2630 "suffix": pd.information_recipient.suffix || "",
2631 "middle": [pd.information_recipient.mname] || "",
2632 "last": pd.information_recipient.lname || "",
2633 "first": pd.information_recipient.fname || ""
2634 },
2635 "organization": {
2636 "name": pd.information_recipient.organization || "org"
2637 },
2638 }
2639 };
2640 let participants = [];
2641 let docParticipants = pd.document_participants || {participant: []};
2642 let count = 0;
2643 try {
2644 count = countEntities(docParticipants.participant);
2645 } catch (e) {
2646 count = 0
2647 }
2648 if (count === 1) {
2649 participants = [populateParticipant(docParticipants.participant)];
2650 } else {
2651 // grab the values of our object
2652 participants = Object.values(docParticipants.participant).filter(pcpt => pcpt.type).map(pcpt => populateParticipant(pcpt));
2653 }
2654 if (participants.length) {
2655 head.participants = participants;
2656 }
2658 if (countEntities(all.encounter_list.encounter) === 1) {
2659 let primary_care_provider = pd.primary_care_provider || {provider: {}};
2660 head.component_of = {
2661 "identifiers": [
2662 {
2663 "identifier": oidFacility || "",
2664 "extension": "PT-" + (pd.patient.id || "")
2665 }
2666 ],
2667 "code": {
2668 "name": pd.primary_diagnosis.text || "",
2669 "code": pd.primary_diagnosis.code || "",
2670 "code_system_name": pd.primary_diagnosis.code_type || ""
2671 },
2672 "date_time": {
2673 "low": {
2674 "date": pd.primary_diagnosis.encounter_date || "",
2675 "precision": "tz"
2676 },
2677 "high": {
2678 "date": pd.primary_diagnosis.encounter_end_date || "",
2679 "precision": "tz"
2680 }
2681 },
2682 "responsible_party": {
2683 "root": oidFacility,
2684 "name": {
2685 "last": pd.author.lname,
2686 "first": pd.author.fname
2687 },
2688 },
2689 "encounter_participant": {
2690 "root": oidFacility,
2691 "name": {
2692 "last": primary_care_provider.provider.lname || "",
2693 "first": primary_care_provider.provider.fname || ""
2694 },
2695 "address": [
2696 {
2697 "street_lines": [
2698 pd.encounter_provider.facility_street
2699 ],
2700 "city": pd.encounter_provider.facility_city,
2701 "state": pd.encounter_provider.facility_state,
2702 "zip": pd.encounter_provider.facility_postal_code,
2703 "country": pd.encounter_provider.facility_country_code || "US",
2704 "use": "work place"
2705 }
2706 ],
2707 "phone": [
2708 {
2709 "number": pd.encounter_provider.facility_phone,
2710 "type": "work primary"
2711 }
2712 ]
2713 }
2714 }
2715 }
2717 return head;
2718 }
2720 function getMeta(pd) {
2721 let meta = {};
2722 meta = {
2723 "type": pd.doc_type,
2724 "identifiers": [
2725 {
2726 "identifier": oidFacility || "NI",
2727 "extension": "TT988"
2728 }
2729 ],
2730 "confidentiality": "Normal",
2731 "set_id": {
2732 "identifier": oidFacility || "NI",
2733 "extension": "sTT988"
2734 }
2735 }
2736 return meta;
2737 }
2739 /**
2740 / * function generateCcda
2741 /* The main document builder
2742 /* pd array the xml parsed array of data sent from CCM.
2743 */
2744 function generateCcda(pd) {
2745 let doc = {};
2746 let data = {};
2747 let count = 0;
2748 let many = [];
2749 let theone = {};
2750 all = pd;
2751 let primary_care_provider = all.primary_care_provider || {};
2752 npiProvider = primary_care_provider.provider ? primary_care_provider.provider.npi : "NI";
2753 oidFacility = all.encounter_provider.facility_oid ? all.encounter_provider.facility_oid : "2.16.840.1.113883.19.5.99999.1";
2754 npiFacility = all.encounter_provider.facility_npi;
2755 webRoot = all.serverRoot;
2756 documentLocation = all.document_location;
2758 authorDateTime = pd.created_time_timezone;
2759 if (pd.author.time.length > 7) {
2760 authorDateTime = pd.author.time;
2761 } else if (all.encounter_list && all.encounter_list.encounter) {
2762 if (countEntities(all.encounter_list.encounter) === 1) {
2763 authorDateTime = all.encounter_list.encounter.date;
2764 } else {
2765 authorDateTime = all.encounter_list.encounter[0].date;
2766 }
2767 }
2769 authorDateTime = fDate(authorDateTime);
2770 // Demographics
2771 let demographic = populateDemographic(pd.patient, pd.guardian, pd);
2772 // This populates documentationOf. We are using providerOrganization also.
2773 if (pd.primary_care_provider) {
2774 Object.assign(demographic, populateProviders(pd));
2775 }
2776 data.demographics = Object.assign(demographic);
2777 // Encounters
2778 let encs = [];
2779 let enc = {};
2780 encs.encounters = [];
2781 try {
2782 count = countEntities(pd.encounter_list.encounter);
2783 } catch (e) {
2784 count = 0
2785 }
2786 if (count > 1) {
2787 for (let i in pd.encounter_list.encounter) {
2788 enc[i] = populateEncounter(pd.encounter_list.encounter[i]);
2789 encs.encounters.push(enc[i]);
2790 }
2791 } else if (count !== 0) {
2792 enc = populateEncounter(pd.encounter_list.encounter);
2793 encs.encounters.push(enc);
2794 }
2795 if (count !== 0) {
2796 data.encounters = Object.assign(encs.encounters);
2797 }
2798 // vitals
2799 let vitals = [];
2800 let vital = {};
2801 vitals.vitals = [];
2802 try {
2803 count = countEntities(pd.history_physical.vitals_list.vitals);
2804 } catch (e) {
2805 count = 0
2806 }
2807 if (count > 1) {
2808 for (let i in pd.history_physical.vitals_list.vitals) {
2809 vitals[i] = populateVital(pd.history_physical.vitals_list.vitals[i]);
2810 vitals.vitals.push(vitals[i]);
2811 }
2812 } else if (count !== 0) {
2813 vital = populateVital(pd.history_physical.vitals_list.vitals);
2814 vitals.vitals.push(vital);
2815 }
2816 if (count !== 0) {
2817 data.vitals = Object.assign(vitals.vitals);
2818 }
2819 // Medications
2820 let meds = [];
2821 let m = {};
2822 meds.medications = [];
2823 try {
2824 count = countEntities(pd.medications.medication);
2825 } catch (e) {
2826 count = 0
2827 }
2828 if (count > 1) {
2829 for (let i in pd.medications.medication) {
2830 m[i] = populateMedication(pd.medications.medication[i]);
2831 meds.medications.push(m[i]);
2832 }
2833 } else if (count !== 0) {
2834 m = populateMedication(pd.medications.medication);
2835 meds.medications.push(m);
2836 }
2837 if (count !== 0) {
2838 data.medications = Object.assign(meds.medications);
2839 }
2840 // Allergies
2841 let allergies = [];
2842 let allergy = {};
2843 allergies.allergies = [];
2844 try {
2845 count = countEntities(pd.allergies.allergy);
2846 } catch (e) {
2847 count = 0
2848 }
2849 if (count > 1) {
2850 for (let i in pd.allergies.allergy) {
2851 allergy[i] = populateAllergy(pd.allergies.allergy[i]);
2852 allergies.allergies.push(allergy[i]);
2853 }
2854 } else if (count <= 1) {
2855 allergy = populateAllergy(pd.allergies.allergy);
2856 allergies.allergies.push(allergy);
2857 count = 1;
2858 }
2859 if (count !== 0) {
2860 data.allergies = Object.assign(allergies.allergies);
2861 }
2862 // Problems
2863 let problems = [];
2864 let problem = {};
2865 problems.problems = [];
2866 try {
2867 count = countEntities(pd.problem_lists.problem);
2868 } catch (e) {
2869 count = 0
2870 }
2871 if (count > 1) {
2872 for (let i in pd.problem_lists.problem) {
2873 problem[i] = populateProblem(pd.problem_lists.problem[i], pd);
2874 problems.problems.push(problem[i]);
2875 }
2876 } else if (count !== 0) {
2877 problem = populateProblem(pd.problem_lists.problem);
2878 problems.problems.push(problem);
2879 }
2880 if (count !== 0) {
2881 data.problems = Object.assign(problems.problems);
2882 }
2883 // Procedures
2884 many = [];
2885 theone = {};
2886 many.procedures = [];
2887 try {
2888 count = countEntities(pd.procedures.procedure);
2889 } catch (e) {
2890 count = 0
2891 }
2892 if (count > 1) {
2893 for (let i in pd.procedures.procedure) {
2894 theone[i] = populateProcedure(pd.procedures.procedure[i]);
2895 many.procedures.push(theone[i]);
2896 }
2897 } else if (count !== 0) {
2898 theone = populateProcedure(pd.procedures.procedure);
2899 many.procedures.push(theone);
2900 }
2901 if (count !== 0) {
2902 data.procedures = Object.assign(many.procedures);
2903 }
2904 // Medical Devices
2905 many = [];
2906 theone = {};
2907 many.medical_devices = [];
2908 try {
2909 count = countEntities(pd.medical_devices.device);
2910 } catch (e) {
2911 count = 0
2912 }
2913 if (count > 1) {
2914 for (let i in pd.medical_devices.device) {
2915 theone[i] = populateMedicalDevice(pd.medical_devices.device[i]);
2916 many.medical_devices.push(theone[i]);
2917 }
2918 } else if (count !== 0) {
2919 theone = populateMedicalDevice(pd.medical_devices.device);
2920 many.medical_devices.push(theone);
2921 }
2922 if (count !== 0) {
2923 data.medical_devices = Object.assign(many.medical_devices);
2924 }
2925 // Results
2926 if (pd.results) {
2927 data.results = Object.assign(getResultSet(pd.results, pd)['results']);
2928 }
2930 // Referral TODO sjp I'm not happy with this.
2931 // different referral sources. 1st is dynamic with doc gen from CCM.
2932 // 2nd is the latest referral from transactions.
2933 if (pd.referral_reason[0].text !== "") {
2934 data.referral_reason = Object.assign(getReferralReason(pd.referral_reason[0], pd));
2935 } else if (pd.referral_reason[1].text !== "" && typeof pd.referral_reason[1].text !== 'undefined') {
2936 data.referral_reason = Object.assign(getReferralReason(pd.referral_reason[1], pd));
2937 } else {
2938 data.referral_reason = {}; // leave as empty so we can get our null flavor section.
2939 }
2940 // Health Concerns
2941 many = [];
2942 theone = {};
2943 many.health_concerns = [];
2944 try {
2945 count = countEntities(pd.health_concerns.concern);
2946 } catch (e) {
2947 count = 0
2948 }
2949 if (count > 1) {
2950 for (let i in pd.health_concerns.concern) {
2951 theone[i] = getHealthConcerns(pd.health_concerns.concern[i]);
2952 many.health_concerns.push(theone[i]);
2953 break;
2954 }
2955 } else if (count !== 0) {
2956 theone = getHealthConcerns(pd.health_concerns.concern);
2957 many.health_concerns.push(theone);
2958 }
2959 if (count !== 0) {
2960 data.health_concerns = Object.assign(many.health_concerns);
2961 } else {
2962 data.health_concerns = {"type": "act"}; // leave it as an empty section that we'll null flavor
2963 }
2964 // Immunizations
2965 many = [];
2966 theone = {};
2967 many.immunizations = [];
2968 try {
2969 count = countEntities(pd.immunizations.immunization);
2970 } catch (e) {
2971 count = 0;
2972 }
2973 if (count > 1) {
2974 for (let i in pd.immunizations.immunization) {
2975 theone[i] = populateImmunization(pd.immunizations.immunization[i]);
2976 many.immunizations.push(theone[i]);
2977 }
2978 } else if (count !== 0) {
2979 theone = populateImmunization(pd.immunizations.immunization);
2980 many.immunizations.push(theone);
2981 }
2982 if (count !== 0) {
2983 data.immunizations = Object.assign(many.immunizations);
2984 }
2985 // Plan of Care
2986 many = [];
2987 theone = {};
2988 many.plan_of_care = [];
2989 try {
2990 count = countEntities(pd.planofcare.item);
2991 } catch (e) {
2992 count = 0
2993 }
2994 if (count > 1) {
2995 for (let i in pd.planofcare.item) {
2996 if (cleanCode(pd.planofcare.item[i].date) === '') {
2997 i--;
2998 continue;
2999 }
3000 theone[i] = getPlanOfCare(pd.planofcare.item[i]);
3001 if (theone[i]) {
3002 many.plan_of_care.push(theone[i]);
3003 }
3004 }
3005 } else if (count !== 0) {
3006 theone = getPlanOfCare(pd.planofcare.item);
3007 if (theone) {
3008 many.plan_of_care.push(theone);
3009 }
3010 }
3011 if (count !== 0) {
3012 data.plan_of_care = Object.assign(many.plan_of_care);
3013 }
3014 // Goals
3015 many = [];
3016 theone = {};
3017 many.goals = [];
3018 try {
3019 count = countEntities(pd.goals.item);
3020 } catch (e) {
3021 count = 0
3022 }
3023 if (count > 1) {
3024 for (let i in pd.goals.item) {
3025 theone[i] = getGoals(pd.goals.item[i]);
3026 many.goals.push(theone[i]);
3027 }
3028 } else if (count !== 0) {
3029 theone = getGoals(pd.goals.item);
3030 many.goals.push(theone);
3031 }
3032 if (count !== 0) {
3033 data.goals = Object.assign(many.goals);
3034 }
3035 // Assessments.
3036 many = [];
3037 theone = {};
3038 many.clinicalNoteAssessments = [];
3039 try {
3040 count = countEntities(pd.clinical_notes.evaluation_note);
3041 } catch (e) {
3042 count = 0
3043 }
3044 if (count > 1) {
3045 for (let i in pd.clinical_notes.evaluation_note) {
3046 theone[i] = getAssessments(pd.clinical_notes.evaluation_note[i]);
3047 many.clinicalNoteAssessments.push(theone[i]);
3048 break; // for now only one assessment. @todo concat notes to one.
3049 }
3050 } else if (count !== 0) {
3051 theone = getAssessments(pd.clinical_notes.evaluation_note);
3052 many.clinicalNoteAssessments.push(theone);
3053 }
3054 if (count !== 0) {
3055 data.clinicalNoteAssessments = Object.assign(many.clinicalNoteAssessments);
3056 }
3058 // Functional Status.
3059 many = [];
3060 theone = {};
3061 many.functional_status = [];
3062 try {
3063 count = countEntities(pd.functional_status.item);
3064 } catch (e) {
3065 count = 0
3066 }
3067 if (count > 1) {
3068 for (let i in pd.functional_status.item) {
3069 theone[i] = getFunctionalStatus(pd.functional_status.item[i]);
3070 many.functional_status.push(theone[i]);
3071 }
3072 } else if (count !== 0) {
3073 theone = getFunctionalStatus(pd.functional_status.item);
3074 many.functional_status.push(theone);
3075 }
3076 if (count !== 0) {
3077 data.functional_status = Object.assign(many.functional_status);
3078 }
3080 // Mental Status.
3081 many = [];
3082 theone = {};
3083 many.mental_status = [];
3084 try {
3085 count = countEntities(pd.mental_status.item);
3086 } catch (e) {
3087 count = 0
3088 }
3089 if (count > 1) {
3090 for (let i in pd.mental_status.item) {
3091 theone[i] = getMentalStatus(pd.mental_status.item[i]);
3092 many.mental_status.push(theone[i]);
3093 }
3094 } else if (count !== 0) {
3095 theone = getMentalStatus(pd.mental_status.item);
3096 many.mental_status.push(theone);
3097 }
3098 if (count !== 0) {
3099 data.mental_status = Object.assign(many.mental_status);
3100 }
3102 // Social History
3103 many = [];
3104 theone = {};
3105 many.social_history = [];
3106 try {
3107 count = countEntities(pd.history_physical.social_history.history_element);
3108 } catch (e) {
3109 count = 0
3110 }
3111 if (count > 1) {
3112 for (let i in pd.history_physical.social_history.history_element) {
3113 if (i > 0) break;
3114 theone[i] = populateSocialHistory(pd.history_physical.social_history.history_element[i]);
3115 many.social_history.push(theone[i]);
3116 }
3117 } else if (count !== 0) {
3118 theone = populateSocialHistory(pd.history_physical.social_history.history_element);
3119 many.social_history.push(theone);
3120 }
3121 if (count !== 0) {
3122 data.social_history = Object.assign(many.social_history);
3123 }
3124 // Notes
3125 for (let currentNote in pd.clinical_notes) {
3126 many = [];
3127 theone = {};
3128 switch (pd.clinical_notes[currentNote].clinical_notes_type) {
3129 case 'evaluation_note':
3130 continue;
3131 case 'progress_note':
3132 break;
3133 case 'history_physical':
3134 pd.clinical_notes[currentNote].code_text = "History and Physical";
3135 break;
3136 case 'nurse_note':
3137 break;
3138 case 'general_note':
3139 break;
3140 case 'discharge_summary':
3141 break;
3142 case 'procedure_note':
3143 break;
3144 case 'consultation_note':
3145 break;
3146 case 'imaging_narrative':
3147 break;
3148 case 'laboratory_report_narrative':
3149 break;
3150 case 'pathology_report_narrative':
3151 break;
3152 default:
3153 continue;
3154 }
3155 try {
3156 count = countEntities(pd.clinical_notes[currentNote]);
3157 } catch (e) {
3158 count = 0
3159 }
3160 if (count > 1) {
3161 for (let i in pd.clinical_notes[currentNote]) {
3162 theone[i] = populateNote(pd.clinical_notes[currentNote]);
3163 many.push(theone[i]);
3164 }
3165 } else if (count !== 0) {
3166 theone = populateNote(pd.clinical_notes[currentNote]);
3167 many.push(theone);
3168 }
3169 if (count !== 0) {
3170 data[currentNote] = Object.assign(many);
3171 }
3172 }
3173 // Care Team and members
3174 if (pd.care_team.is_active == 'active') {
3175 data.care_team = Object.assign(populateCareTeamMembers(pd));
3176 }
3178 // ------------------------------------------ End Sections ---------------------------------------- //
3180 // sections data objects
3181 doc.data = Object.assign(data);
3182 // document meta data and header objects
3183 let meta = getMeta(pd);
3184 let header = populateHeader(pd);
3185 meta.ccda_header = Object.assign(header);
3186 doc.meta = Object.assign(meta);
3188 if (pd.timezone_local_offset) {
3189 populateTimezones(doc, pd.timezone_local_offset, 0);
3190 }
3191 // build to cda
3192 let xml = bbg.generateCCD(doc);
3194 /* Debug */
3195 if (enableDebug === true) {
3196 let place = documentLocation + "/documents/temp/";
3197 if (fs.existsSync(place)) {
3198 fs.writeFile(place + "ccda.json", JSON.stringify(all, null, 4), function (err) {
3199 if (err) {
3200 return console.log(err);
3201 }
3202 });
3203 fs.writeFile(place + "ccda.xml", xml, function (err) {
3204 if (err) {
3205 return console.log(err);
3206 }
3207 });
3208 }
3209 }
3211 return xml;
3212 }
3214 let unstructuredTemplate = null;
3216 function generateUnstructured(pd) {
3217 let doc = {};
3218 let data = {};
3219 let count = 0;
3220 let many = [];
3221 let theone = {};
3222 // include unstructured document type oid in header
3223 pd.doc_type = 'unstructured';
3224 all = pd;
3225 let primary_care_provider = all.primary_care_provider || {};
3226 npiProvider = primary_care_provider.provider ? primary_care_provider.provider.npi : "NI";
3227 oidFacility = all.encounter_provider.facility_oid ? all.encounter_provider.facility_oid : "2.16.840.1.113883.19.5.99999.1";
3228 npiFacility = all.encounter_provider.facility_npi || "NI";
3229 webRoot = all.serverRoot;
3230 documentLocation = all.document_location;
3231 authorDateTime = pd.created_time_timezone;
3232 if (pd.author.time.length > 7) {
3233 authorDateTime = pd.author.time;
3234 } else if (all.encounter_list && all.encounter_list.encounter) {
3235 if (countEntities(all.encounter_list.encounter) === 1) {
3236 authorDateTime = all.encounter_list.encounter.date;
3237 } else {
3238 authorDateTime = all.encounter_list.encounter[0].date;
3239 }
3240 }
3241 authorDateTime = fDate(authorDateTime);
3242 // Demographics is needed in unstructured
3243 let demographic = populateDemographic(pd.patient, pd.guardian, pd);
3244 data.demographics = Object.assign(demographic);
3246 if (pd.primary_care_provider) {
3247 Object.assign(demographic, populateProviders(pd));
3248 }
3249 doc.data = Object.assign(data);
3251 // document meta data and header objects
3252 let meta = getMeta(pd);
3253 let header = populateHeader(pd);
3254 meta.ccda_header = Object.assign(header);
3255 doc.meta = Object.assign(meta);
3257 // set TZ offset for moment
3258 if (pd.timezone_local_offset) {
3259 populateTimezones(doc, pd.timezone_local_offset, 0);
3260 }
3261 // build to cda
3262 let xml = bbg.generateCCD(doc);
3263 unstructuredTemplate = unstructuredTemplate.trim();
3264 xml = xml.replace(/<\/ClinicalDocument>/g, unstructuredTemplate);
3265 xml += "</ClinicalDocument>" + "\n";
3267 /* Debug */
3268 if (enableDebug === true) {
3269 let place = documentLocation + "/documents/temp/";
3270 if (fs.existsSync(place)) {
3271 fs.writeFile(place + "unstructured.xml", xml, function (err) {
3272 if (err) {
3273 return console.log(err);
3274 }
3275 });
3276 }
3277 }
3279 return xml;
3280 }
3282 function processConnection(connection) {
3283 conn = connection; // make it global
3284 let remoteAddress = conn.remoteAddress + ':' + conn.remotePort;
3285 conn.setEncoding('utf8');
3286 //console.log('server remote address ', remoteAddress);
3287 let xml_complete = "";
3289 function eventData(xml) {
3290 xml_complete = xml.toString();
3291 // ensure we have an array start and end
3292 if (xml_complete.match(/^<CCDA/g) && xml_complete.match(/<\/CCDA>$/g)) {
3293 let doc = "";
3294 let xslUrl = "";
3295 /* eslint-disable-next-line no-control-regex */
3296 xml_complete = xml_complete.replace(/(\u000b\u001c)/gm, "").trim();
3297 xml_complete = xml_complete.replace(/\t\s+/g, " ").trim();
3298 // convert xml data set for document to json array
3299 to_json(xml_complete, function (error, data) {
3300 if (error) {
3301 console.log(
3302 "toJson error: " + error + "Len: " + xml_complete.length
3303 );
3304 return "ERROR: Failed json build";
3305 }
3306 let unstructured = "";
3307 let isUnstruturedData = !!data.CCDA.patient_files;
3308 // extract unstructured documents file component templates. One per file.
3309 if (isUnstruturedData) {
3310 unstructuredTemplate = xml_complete.substring(
3311 xml_complete.lastIndexOf("<patient_files>") + 15,
3312 xml_complete.lastIndexOf("</patient_files>")
3313 );
3314 }
3315 // create doc_type document i.e. CCD Referral etc.
3316 if (data.CCDA.doc_type !== "unstructured") {
3317 doc = generateCcda(data.CCDA);
3318 if (data.CCDA.xslUrl) {
3319 xslUrl = data.CCDA.xslUrl || "";
3320 }
3321 doc = headReplace(doc, xslUrl);
3322 } else {
3323 unstructured = generateUnstructured(data.CCDA);
3324 if (data.CCDA.xslUrl) {
3325 xslUrl = data.CCDA.xslUrl || "";
3326 }
3327 doc = headReplace(unstructured, xslUrl);
3328 // combine the two documents to send back all at once.
3329 doc += unstructured;
3330 }
3331 // auto build an Unstructured document of supplied embedded files.
3332 if (
3333 data.CCDA.doc_type !== "unstructured" &&
3334 isUnstruturedData
3335 ) {
3336 unstructured = generateUnstructured(data.CCDA);
3337 unstructured = headReplace(unstructured, xslUrl);
3338 // combine the two documents to send back all at once.
3339 doc += unstructured;
3340 }
3341 });
3342 // send results back to eagerly awaiting CCM for disposal.
3343 doc = doc
3344 .toString()
3345 /* eslint-disable-next-line no-control-regex */
3346 .replace(/(\u000b\u001c|\r)/gm, "")
3347 .trim();
3348 let chunk = "";
3349 let numChunks = Math.ceil(doc.length / 1024);
3350 for (let i = 0, o = 0; i < numChunks; ++i, o += 1024) {
3351 chunk = doc.substring(o, o + 1024);
3352 conn.write(chunk);
3353 }
3354 conn.write(String.fromCharCode(28) + "\r\r" + "");
3355 conn.end();
3356 }
3357 }
3359 function eventCloseConn() {
3360 //console.log('connection from %s closed', remoteAddress);
3361 }
3363 function eventErrorConn(err) {
3364 console.log('Connection %s error: %s', remoteAddress, err.message);
3365 console.log(err.stack);
3366 conn.destroy();
3367 }
3369 // Connection Events //
3370 // CCM will send one File Separator characters to mark end of array.
3371 let received = new DataStack(String.fromCharCode(28));
3372 conn.on("data", data => {
3373 received.push(data);
3374 while (!received.endOfCcda() && data.length > 0) {
3375 data = "";
3376 eventData(received.returnData());
3377 }
3378 });
3380 conn.once('close', eventCloseConn);
3381 conn.on('error', eventErrorConn);
3382 }
3384 function setUp(server) {
3385 server.on('connection', processConnection);
3386 server.listen(6661, '127.0.0.1', function () { // never change port!
3387 //console.log('server listening to ', server.address());
3388 });
3389 }
3391 // start up listener for requests from CCM or others.
3392 setUp(server);
3394 /* ---------------------------------For future use in header. Do not remove!-------------------------------------------- */
3395 /*"data_enterer": {
3396 "identifiers": [
3397 {
3398 "identifier": "2.16.840.1.113883.4.6",
3399 "extension": "999999943252"
3400 }
3401 ],
3402 "name": [
3403 {
3404 "last": pd.data_enterer.lname,
3405 "first": pd.data_enterer.fname
3406 }
3407 ],
3408 "address": [
3409 {
3410 "street_lines": [
3411 pd.data_enterer.streetAddressLine
3412 ],
3413 "city": pd.data_enterer.city,
3414 "state": pd.data_enterer.state,
3415 "zip": pd.data_enterer.postalCode,
3416 "country": pd.data_enterer.country
3417 }
3418 ],
3419 "phone": [
3420 {
3421 "number": pd.data_enterer.telecom,
3422 "type": "work place"
3423 }
3424 ]
3425 },
3426 "informant": {
3427 "identifiers": [
3428 {
3429 "identifier": "2.16.840.1.113883.19.5",
3430 "extension": "KP00017"
3431 }
3432 ],
3433 "name": [
3434 {
3435 "last": pd.informer.lname || "",
3436 "first": pd.informer.fname || ""
3437 }
3438 ],
3439 "address": [
3440 {
3441 "street_lines": [
3442 pd.informer.streetAddressLine || ""
3443 ],
3444 "city": pd.informer.city,
3445 "state": pd.informer.state,
3446 "zip": pd.informer.postalCode,
3447 "country": pd.informer.country
3448 }
3449 ],
3450 "phone": [
3451 {
3452 "number": pd.informer.telecom || "",
3453 "type": "work place"
3454 }
3455 ]
3456 },*/
3457 /*"service_event": {
3458 "code": {
3459 "name": "",
3460 "code": "",
3461 "code_system_name": "SNOMED CT"
3462 },
3463 "date_time": {
3464 "low": {
3465 "date": "2021-03-11",
3466 "precision": "day"
3467 },
3468 "high": {
3469 "date": pd.created_time,
3470 "precision": "day"
3471 }
3472 },
3473 "performer": [
3474 {
3475 "performer": [
3476 {
3477 "identifiers": [
3478 {
3479 "identifier": "2.16.840.1.113883.4.6",
3480 "extension": npiProvider
3481 }
3482 ],
3483 "name": [
3484 {
3485 "last": pd.information_recipient.lname || "DAH",
3486 "first": pd.information_recipient.fname || "DAH"
3487 }
3488 ],
3489 "address": [
3490 {
3491 "street_lines": [
3492 pd.information_recipient.streetAddressLine
3493 ],
3494 "city": pd.information_recipient.city,
3495 "state": pd.information_recipient.state,
3496 "zip": pd.information_recipient.postalCode,
3497 "country": pd.information_recipient.country || "US"
3498 }
3499 ],
3500 "phone": [
3501 {
3502 "number": pd.information_recipient.telecom,
3503 "type": "work place"
3504 }
3505 ],
3506 "organization": [
3507 {
3508 "identifiers": [
3509 {
3510 "identifier": "2.16.840.1.113883.19.5.9999.1393"
3511 }
3512 ],
3513 "name": [
3514 pd.encounter_provider.facility_name
3515 ],
3516 "address": [
3517 {
3518 "street_lines": [
3519 pd.encounter_provider.facility_street
3520 ],
3521 "city": pd.encounter_provider.facility_city,
3522 "state": pd.encounter_provider.facility_state,
3523 "zip": pd.encounter_provider.facility_postal_code,
3524 "country": pd.encounter_provider.facility_country_code || "US"
3525 }
3526 ],
3527 "phone": [
3528 {
3529 "number": pd.encounter_provider.facility_phone,
3530 "type": "primary work"
3531 }
3532 ]
3533 }
3534 ],
3535 "code": [
3536 {
3537 "name": "",
3538 "code": "",
3539 "code_system_name": "Provider Codes"
3540 }
3541 ]
3542 }
3543 ],
3544 "code": {
3545 "name": "Primary Performer",
3546 "code": "PP",
3547 "code_system_name": "Provider Role"
3548 }
3549 }
3550 ]
3551 }*/